From fecef0b05a6d7390fa46283d46045941dd29c3fb Mon Sep 17 00:00:00 2001 From: Konstantinos Tsilimparis Date: Mon, 7 Apr 2025 17:26:04 +0200 Subject: [PATCH 01/31] ENH: add ctf275 montage This is based on the channel positions and orientations provided by fieldtrip: https://github.com/fieldtrip/fieldtrip/blob/master/template/gradiometer/ctf275.mat --- mne/channels/data/montages/ctf275.txt | 305 ++++++++++++++++++++++++++ 1 file changed, 305 insertions(+) create mode 100644 mne/channels/data/montages/ctf275.txt diff --git a/mne/channels/data/montages/ctf275.txt b/mne/channels/data/montages/ctf275.txt new file mode 100644 index 00000000000..d694ffc58ca --- /dev/null +++ b/mne/channels/data/montages/ctf275.txt @@ -0,0 +1,305 @@ +Name x y z ex ey ez +MLC11 0.086 0.0103 0.1355 0.346 0.0418 0.9373 +MLC12 0.0742 0.0286 0.1379 0.2638 0.1842 0.9468 +MLC13 0.0659 0.0488 0.1335 0.2456 0.3738 0.8944 +MLC14 0.0535 0.0678 0.1269 0.2278 0.5748 0.786 +MLC15 0.0403 0.0822 0.1182 0.1537 0.6329 0.7588 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-0.0261 0.0198 0.9452 -0.3265 0.0044 \ No newline at end of file From 003a7ac3e9442dd0a8fc5824e5af1a4ffe2b4e4b Mon Sep 17 00:00:00 2001 From: Konstantinos Tsilimparis Date: Wed, 9 Apr 2025 16:33:15 +0200 Subject: [PATCH 03/31] ENH: add ch_type in the Neuromag and CTF montages That way we can parse the txt files and select the channel type we want to interpolate to. For example we do not want to interpolate to the ch_type = 'ref' for the CTF. --- mne/channels/data/montages/ctf275.txt | 610 ++++++++++---------- mne/channels/data/montages/neuromag306.txt | 614 ++++++++++----------- 2 files changed, 612 insertions(+), 612 deletions(-) diff --git a/mne/channels/data/montages/ctf275.txt b/mne/channels/data/montages/ctf275.txt index d694ffc58ca..cca4d274aa7 100644 --- a/mne/channels/data/montages/ctf275.txt +++ b/mne/channels/data/montages/ctf275.txt @@ -1,305 +1,305 @@ -Name x y z ex ey ez -MLC11 0.086 0.0103 0.1355 0.346 0.0418 0.9373 -MLC12 0.0742 0.0286 0.1379 0.2638 0.1842 0.9468 -MLC13 0.0659 0.0488 0.1335 0.2456 0.3738 0.8944 -MLC14 0.0535 0.0678 0.1269 0.2278 0.5748 0.786 -MLC15 0.0403 0.0822 0.1182 0.1537 0.6329 0.7588 -MLC16 0.0243 0.0921 0.1072 0.1428 0.8536 0.5009 -MLC17 0.0065 0.0961 0.0911 -0.1102 0.9547 0.2762 -MLC21 0.053 0.0316 0.1426 0.2195 0.2189 0.9507 -MLC22 0.0446 0.0513 0.1382 0.1941 0.3723 0.9076 -MLC23 0.032 0.0677 0.1316 0.1201 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-0.822 0.0459 +MRT21 grad 0.0911 -0.0897 0.0339 0.3966 -0.9165 0.0531 +MRT22 grad 0.0618 -0.1008 0.033 0.2988 -0.9532 0.0467 +MRT23 grad 0.0406 -0.107 0.0396 0.0772 -0.9965 0.0325 +MRT24 grad 0.0182 -0.1117 0.0392 -0.0446 -0.9987 0.0249 +MRT25 grad -0.0094 -0.1126 0.0375 -0.1313 -0.9912 0.0195 +MRT26 grad -0.0326 -0.1067 0.0371 -0.3011 -0.9536 0.0092 +MRT27 grad -0.0513 -0.0884 0.037 -0.5075 -0.8617 -0.003 +MRT31 grad 0.0993 -0.0867 0.0144 0.4282 -0.902 0.0552 +MRT32 grad 0.0771 -0.0962 0.019 0.3644 -0.9298 0.051 +MRT33 grad 0.0466 -0.1069 0.019 0.1291 -0.991 0.0358 +MRT34 grad 0.0242 -0.1127 0.0185 -0.0272 -0.9993 0.026 +MRT35 grad 0.0019 -0.1131 0.0192 -0.0793 -0.9966 0.0228 +MRT36 grad -0.021 -0.1112 0.0187 -0.1989 -0.98 -0.0021 +MRT37 grad -0.0426 -0.097 0.0185 -0.398 -0.9173 -0.0141 +MRT41 grad 0.0848 -0.0938 -0.0016 0.3806 -0.9233 0.052 +MRT42 grad 0.062 -0.1017 0.004 0.2822 -0.9583 0.0456 +MRT43 grad 0.0355 -0.1123 0.0002 0.025 -0.9993 0.0293 +MRT44 grad 0.0132 -0.1131 -0.0002 -0.0446 -0.9987 0.0249 +MRT45 grad -0.0093 -0.1129 0.0004 -0.1485 -0.9887 0.0185 +MRT46 grad -0.0332 -0.1064 0 -0.3167 -0.9485 -0.0092 +MRT47 grad -0.0515 -0.0884 0 -0.5224 -0.8527 -0.0039 +MRT51 grad 0.0695 -0.1003 -0.0166 0.3318 -0.9421 0.0489 +MRT52 grad 0.048 -0.1095 -0.018 0.1118 -0.9931 0.0347 +MRT53 grad 0.0247 -0.1134 -0.0195 -0.0272 -0.9993 0.026 +MRT54 grad 0.0017 -0.1139 -0.0189 -0.0956 -0.9954 0.0043 +MRT55 grad -0.0217 -0.1107 -0.0184 -0.2339 -0.9722 0.0133 +MRT56 grad -0.0423 -0.0962 -0.0186 -0.4297 -0.9028 -0.0159 +MRT57 grad -0.0603 -0.0813 -0.0186 -0.6214 -0.783 -0.0271 +MZC01 grad 0.0681 -0.0007 0.1413 0.2659 -0.0067 0.964 +MZC02 grad 0.0314 -0.001 0.1495 0.1636 0.0005 0.9865 +MZC03 grad -0.0071 -0.0011 0.1521 -0.0476 -0.0166 0.9987 +MZC04 grad -0.0457 -0.0014 0.1402 -0.5235 -0.019 0.8518 +MZF01 grad 0.1513 0 0.0494 0.9952 0.0126 0.0973 +MZF02 grad 0.1424 -0.0001 0.0879 0.9093 0.0094 0.416 +MZF03 grad 0.1067 -0.0004 0.1267 0.5362 -0.0024 0.8441 +MZO01 grad -0.0983 -0.0017 0.0598 -0.9487 -0.0114 0.316 +MZO02 grad -0.0997 -0.0017 0.0186 -0.9996 -0.0096 -0.0276 +MZO03 grad -0.0983 -0.0018 -0.0184 -0.9989 -0.0095 -0.045 +MZP01 grad -0.0851 -0.0016 0.0947 -0.8876 -0.0118 0.4605 +BG1 ref -0.0541 -0.0702 0.2384 -0.7126 0.7004 -0.0394 +BG2 ref -0.0541 -0.0702 0.2384 -0.6987 -0.7137 -0.0489 +BG3 ref -0.0541 -0.0702 0.2384 0.0624 0.0073 -0.998 +BP1 ref -0.0554 0.0644 0.2393 -0.7126 0.7004 -0.0394 +BP2 ref -0.0554 0.0644 0.2393 -0.6987 -0.7137 -0.0489 +BP3 ref -0.0554 0.0644 0.2393 0.0624 0.0073 -0.998 +BR1 ref 0.0789 0.0656 0.2477 -0.7126 0.7004 -0.0394 +BR2 ref 0.0789 0.0656 0.2477 -0.6987 -0.7137 -0.0489 +BR3 ref 0.0789 0.0656 0.2477 0.0624 0.0073 -0.998 +G11 ref -0.026 -0.0978 0.24 -0.7126 0.7004 -0.0394 +G12 ref -0.0541 -0.0703 0.2384 -0.6987 -0.7137 -0.0489 +G13 ref -0.0541 -0.0703 0.2384 0.0624 0.0073 -0.998 +G22 ref -0.0267 -0.0422 0.2403 -0.6987 -0.7137 -0.0489 +G23 ref -0.0541 -0.0703 0.2384 0.0624 0.0073 -0.998 +P11 ref -0.0274 0.0369 0.2408 -0.7126 0.7004 -0.0394 +P12 ref -0.0555 0.0644 0.2393 -0.6987 -0.7137 -0.0489 +P13 ref -0.0555 0.0644 0.2393 0.0624 0.0073 -0.998 +P22 ref -0.0279 0.0925 0.2412 -0.6987 -0.7137 -0.0489 +P23 ref -0.0554 0.0644 0.2393 0.0624 0.0073 -0.998 +Q11 ref 0.1086 -0.0969 0.2484 -0.7126 0.7004 -0.0394 +Q12 ref 0.0806 -0.0693 0.2468 -0.6987 -0.7137 -0.0489 +Q13 ref 0.0806 -0.0693 0.2468 0.0624 0.0073 -0.998 +Q22 ref 0.1078 -0.0409 0.2487 -0.6987 -0.7137 -0.0489 +Q23 ref 0.0803 -0.0689 0.2468 0.0624 0.0073 -0.998 +R11 ref 0.107 0.038 0.2493 -0.7126 0.7004 -0.0394 +R12 ref 0.079 0.0656 0.2477 -0.6987 -0.7137 -0.0489 +R13 ref 0.079 0.0656 0.2477 0.0624 0.0073 -0.998 +R22 ref 0.1067 0.094 0.2497 -0.6987 -0.7137 -0.0489 +R23 ref 0.0792 0.066 0.2477 0.0624 0.0073 -0.998 \ No newline at end of file diff --git a/mne/channels/data/montages/neuromag306.txt b/mne/channels/data/montages/neuromag306.txt index 41fa747f36a..ddc37c6b246 100644 --- a/mne/channels/data/montages/neuromag306.txt +++ b/mne/channels/data/montages/neuromag306.txt @@ -1,307 +1,307 @@ -Name x y z ex ey ez -MEG0113 -0.1064 0.0529 -0.0203 -0.9829 0.1844 -0.0019 -MEG0112 -0.1064 0.0529 -0.0203 -0.9829 0.1844 -0.0019 -MEG0111 -0.1064 0.0529 -0.0203 -0.9829 0.1844 -0.0019 -MEG0122 -0.1019 0.0726 0.0129 -0.9658 0.2572 -0.0325 -MEG0123 -0.1019 0.0726 0.0129 -0.9658 0.2572 -0.0325 -MEG0121 -0.1019 0.0726 0.0129 -0.9658 0.2572 -0.0325 -MEG0132 -0.1083 0.0397 0.0148 -0.9965 0.0816 -0.0188 -MEG0133 -0.1083 0.0397 0.0148 -0.9965 0.0816 -0.0188 -MEG0131 -0.1083 0.0397 0.0148 -0.9965 0.0816 -0.0188 -MEG0143 -0.1096 0.0195 -0.0196 -0.9995 0.0264 0.0197 -MEG0142 -0.1096 0.0195 -0.0196 -0.9995 0.0264 0.0197 -MEG0141 -0.1096 0.0195 -0.0196 -0.9995 0.0264 0.0197 -MEG0213 -0.1073 0.0455 0.0491 -0.9936 0.1035 0.0446 -MEG0212 -0.1073 0.0455 0.0491 -0.9936 0.1035 0.0446 -MEG0211 -0.1073 0.0455 0.0491 -0.9936 0.1035 0.0446 -MEG0222 -0.0988 0.0558 0.0817 -0.9266 0.1772 0.3316 -MEG0223 -0.0988 0.0558 0.0817 -0.9266 0.1772 0.3316 -MEG0221 -0.0988 0.0558 0.0817 -0.9266 0.1772 0.3316 -MEG0232 -0.101 0.02 0.0844 -0.9359 -0.0193 0.3518 -MEG0233 -0.101 0.02 0.0844 -0.9359 -0.0193 0.3518 -MEG0231 -0.101 0.02 0.0844 -0.9359 -0.0193 0.3518 -MEG0243 -0.1081 0.0115 0.0512 -0.9941 -0.0788 0.075 -MEG0242 -0.1081 0.0115 0.0512 -0.9941 -0.0788 0.075 -MEG0241 -0.1081 0.0115 0.0512 -0.9941 -0.0788 0.075 -MEG0313 -0.0861 0.1114 0.0443 -0.8737 0.4845 0.0422 -MEG0312 -0.0861 0.1114 0.0443 -0.8737 0.4845 0.0422 -MEG0311 -0.0861 0.1114 0.0443 -0.8737 0.4845 0.0422 -MEG0322 -0.0887 0.0912 0.0785 -0.8628 0.3815 0.3317 -MEG0323 -0.0887 0.0912 0.0785 -0.8628 0.3815 0.3317 -MEG0321 -0.0887 0.0912 0.0785 -0.8628 0.3815 0.3317 -MEG0333 -0.0702 0.0939 0.1079 -0.7075 0.372 0.6009 -MEG0332 -0.0702 0.0939 0.1079 -0.7075 0.372 0.6009 -MEG0331 -0.0702 0.0939 0.1079 -0.7075 0.372 0.6009 -MEG0343 -0.1002 0.0784 0.0463 -0.9551 0.2926 0.0459 -MEG0342 -0.1002 0.0784 0.0463 -0.9551 0.2926 0.0459 -MEG0341 -0.1002 0.0784 0.0463 -0.9551 0.2926 0.0459 -MEG0413 -0.0807 0.0596 0.1123 -0.7561 0.1942 0.625 -MEG0412 -0.0807 0.0596 0.1123 -0.7561 0.1942 0.625 -MEG0411 -0.0807 0.0596 0.1123 -0.7561 0.1942 0.625 -MEG0422 -0.0525 0.061 0.1356 -0.4822 0.2266 0.8463 -MEG0423 -0.0525 0.061 0.1356 -0.4822 0.2266 0.8463 -MEG0421 -0.0525 0.061 0.1356 -0.4822 0.2266 0.8463 -MEG0432 -0.0535 0.0265 0.1403 -0.4947 0.0523 0.8675 -MEG0433 -0.0535 0.0265 0.1403 -0.4947 0.0523 0.8675 -MEG0431 -0.0535 0.0265 0.1403 -0.4947 0.0523 0.8675 -MEG0443 -0.0828 0.0246 0.1162 -0.7634 -0.0045 0.6459 -MEG0442 -0.0828 0.0246 0.1162 -0.7634 -0.0045 0.6459 -MEG0441 -0.0828 0.0246 0.1162 -0.7634 -0.0045 0.6459 -MEG0513 -0.0637 0.1384 0.0465 -0.6127 0.7845 0.0956 -MEG0512 -0.0637 0.1384 0.0465 -0.6127 0.7845 0.0956 -MEG0511 -0.0637 0.1384 0.0465 -0.6127 0.7845 0.0956 -MEG0523 -0.0331 0.1531 0.0491 -0.2883 0.9494 0.1245 -MEG0522 -0.0331 0.1531 0.0491 -0.2883 0.9494 0.1245 -MEG0521 -0.0331 0.1531 0.0491 -0.2883 0.9494 0.1245 -MEG0532 -0.0336 0.1436 0.0813 -0.2713 0.8806 0.3886 -MEG0533 -0.0336 0.1436 0.0813 -0.2713 0.8806 0.3886 -MEG0531 -0.0336 0.1436 0.0813 -0.2713 0.8806 0.3886 -MEG0542 -0.0672 0.1246 0.0787 -0.687 0.6441 0.3365 -MEG0543 -0.0672 0.1246 0.0787 -0.687 0.6441 0.3365 -MEG0541 -0.0672 0.1246 0.0787 -0.687 0.6441 0.3365 -MEG0613 -0.0357 0.1233 0.1097 -0.2934 0.6735 0.6785 -MEG0612 -0.0357 0.1233 0.1097 -0.2934 0.6735 0.6785 -MEG0611 -0.0357 0.1233 0.1097 -0.2934 0.6735 0.6785 -MEG0622 0.0003 0.0981 0.1339 -0.0032 0.4747 0.8801 -MEG0623 0.0003 0.0981 0.1339 -0.0032 0.4747 0.8801 -MEG0621 0.0003 0.0981 0.1339 -0.0032 0.4747 0.8801 -MEG0633 -0.0182 0.0655 0.1464 -0.1288 0.265 0.9556 -MEG0632 -0.0182 0.0655 0.1464 -0.1288 0.265 0.9556 -MEG0631 -0.0182 0.0655 0.1464 -0.1288 0.265 0.9556 -MEG0642 -0.0367 0.0954 0.1295 -0.3099 0.4849 0.8178 -MEG0643 -0.0367 0.0954 0.1295 -0.3099 0.4849 0.8178 -MEG0641 -0.0367 0.0954 0.1295 -0.3099 0.4849 0.8178 -MEG0713 -0.0183 0.0323 0.1527 -0.1513 0.0736 0.9858 -MEG0712 -0.0183 0.0323 0.1527 -0.1513 0.0736 0.9858 -MEG0711 -0.0183 0.0323 0.1527 -0.1513 0.0736 0.9858 -MEG0723 0.0189 0.0324 0.1527 0.1491 0.1212 0.9814 -MEG0722 0.0189 0.0324 0.1527 0.1491 0.1212 0.9814 -MEG0721 0.0189 0.0324 0.1527 0.1491 0.1212 0.9814 -MEG0733 0.019 -0.0016 0.152 0.1492 -0.1534 0.9768 -MEG0732 0.019 -0.0016 0.152 0.1492 -0.1534 0.9768 -MEG0731 0.019 -0.0016 0.152 0.1492 -0.1534 0.9768 -MEG0743 -0.0182 -0.0021 0.1519 -0.1189 -0.1509 0.9814 -MEG0742 -0.0182 -0.0021 0.1519 -0.1189 -0.1509 0.9814 -MEG0741 -0.0182 -0.0021 0.1519 -0.1189 -0.1509 0.9814 -MEG0813 0.0003 0.1581 0.0501 -0.0112 0.986 0.1666 -MEG0812 0.0003 0.1581 0.0501 -0.0112 0.986 0.1666 -MEG0811 0.0003 0.1581 0.0501 -0.0112 0.986 0.1666 -MEG0822 0.0002 0.148 0.0825 -0.0049 0.907 0.421 -MEG0823 0.0002 0.148 0.0825 -0.0049 0.907 0.421 -MEG0821 0.0002 0.148 0.0825 -0.0049 0.907 0.421 -MEG0913 0.0334 0.1532 0.0491 0.2616 0.9542 0.1453 -MEG0912 0.0334 0.1532 0.0491 0.2616 0.9542 0.1453 -MEG0911 0.0334 0.1532 0.0491 0.2616 0.9542 0.1453 -MEG0923 0.0642 0.1386 0.0467 0.6219 0.778 0.089 -MEG0922 0.0642 0.1386 0.0467 0.6219 0.778 0.089 -MEG0921 0.0642 0.1386 0.0467 0.6219 0.778 0.089 -MEG0932 0.0675 0.1249 0.079 0.6744 0.6573 0.3364 -MEG0933 0.0675 0.1249 0.079 0.6744 0.6573 0.3364 -MEG0931 0.0675 0.1249 0.079 0.6744 0.6573 0.3364 -MEG0942 0.034 0.1436 0.0815 0.2621 0.883 0.3895 -MEG0943 0.034 0.1436 0.0815 0.2621 0.883 0.3895 -MEG0941 0.034 0.1436 0.0815 0.2621 0.883 0.3895 -MEG1013 0.0002 0.1281 0.1115 -0.0277 0.7171 0.6964 -MEG1012 0.0002 0.1281 0.1115 -0.0277 0.7171 0.6964 -MEG1011 0.0002 0.1281 0.1115 -0.0277 0.7171 0.6964 -MEG1023 0.036 0.1235 0.1099 0.2684 0.6789 0.6834 -MEG1022 0.036 0.1235 0.1099 0.2684 0.6789 0.6834 -MEG1021 0.036 0.1235 0.1099 0.2684 0.6789 0.6834 -MEG1032 0.0371 0.0955 0.1298 0.3053 0.4832 0.8205 -MEG1033 0.0371 0.0955 0.1298 0.3053 0.4832 0.8205 -MEG1031 0.0371 0.0955 0.1298 0.3053 0.4832 0.8205 -MEG1043 0.0187 0.0657 0.1464 0.1259 0.2893 0.9489 -MEG1042 0.0187 0.0657 0.1464 0.1259 0.2893 0.9489 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0.1026 -0.0208 0.0531 0.9465 -0.3069 0.0995 +MEG2422 grad 0.1026 -0.0208 0.0531 0.9465 -0.3069 0.0995 +MEG2421 mag 0.1026 -0.0208 0.0531 0.9465 -0.3069 0.0995 +MEG2433 grad 0.0875 -0.0509 0.0556 0.8338 -0.5381 0.1235 +MEG2432 grad 0.0875 -0.0509 0.0556 0.8338 -0.5381 0.1235 +MEG2431 mag 0.0875 -0.0509 0.0556 0.8338 -0.5381 0.1235 +MEG2442 grad 0.0789 -0.0469 0.0892 0.765 -0.522 0.3771 +MEG2443 grad 0.0789 -0.0469 0.0892 0.765 -0.522 0.3771 +MEG2441 mag 0.0789 -0.0469 0.0892 0.765 -0.522 0.3771 +MEG2512 grad 0.0639 -0.0806 0.0248 0.5947 -0.8035 0.0273 +MEG2513 grad 0.0639 -0.0806 0.0248 0.5947 -0.8035 0.0273 +MEG2511 mag 0.0639 -0.0806 0.0248 0.5947 -0.8035 0.0273 +MEG2522 grad 0.0871 -0.0561 0.0223 0.8294 -0.558 0.0273 +MEG2523 grad 0.0871 -0.0561 0.0223 0.8294 -0.558 0.0273 +MEG2521 mag 0.0871 -0.0561 0.0223 0.8294 -0.558 0.0273 +MEG2533 grad 0.0767 -0.0728 -0.0104 0.7119 -0.6982 0.0759 +MEG2532 grad 0.0767 -0.0728 -0.0104 0.7119 -0.6982 0.0759 +MEG2531 mag 0.0767 -0.0728 -0.0104 0.7119 -0.6982 0.0759 +MEG2543 grad 0.0498 -0.0925 -0.0088 0.4216 -0.9012 0.1005 +MEG2542 grad 0.0498 -0.0925 -0.0088 0.4216 -0.9012 0.1005 +MEG2541 mag 0.0498 -0.0925 -0.0088 0.4216 -0.9012 0.1005 +MEG2612 grad 0.1096 0.0067 0.0167 0.9978 -0.0658 -0.011 +MEG2613 grad 0.1096 0.0067 0.0167 0.9978 -0.0658 -0.011 +MEG2611 mag 0.1096 0.0067 0.0167 0.9978 -0.0658 -0.011 +MEG2623 grad 0.1078 -0.0136 -0.0157 0.9751 -0.2184 0.0377 +MEG2622 grad 0.1078 -0.0136 -0.0157 0.9751 -0.2184 0.0377 +MEG2621 mag 0.1078 -0.0136 -0.0157 0.9751 -0.2184 0.0377 +MEG2633 grad 0.0961 -0.0454 -0.0128 0.8783 -0.4746 0.0573 +MEG2632 grad 0.0961 -0.0454 -0.0128 0.8783 -0.4746 0.0573 +MEG2631 mag 0.0961 -0.0454 -0.0128 0.8783 -0.4746 0.0573 +MEG2642 grad 0.1026 -0.0261 0.0198 0.9452 -0.3265 0.0044 +MEG2643 grad 0.1026 -0.0261 0.0198 0.9452 -0.3265 0.0044 +MEG2641 mag 0.1026 -0.0261 0.0198 0.9452 -0.3265 0.0044 \ No newline at end of file From 8bfba64628cceb5336df4a72e39a3893014ec154 Mon Sep 17 00:00:00 2001 From: Konstantinos Tsilimparis Date: Wed, 9 Apr 2025 16:33:53 +0200 Subject: [PATCH 04/31] ENH: allow make_standard_montage to accept and parse the new meg montages --- mne/channels/_standard_montage_utils.py | 47 +++++++++++++++++++++++++ 1 file changed, 47 insertions(+) diff --git a/mne/channels/_standard_montage_utils.py b/mne/channels/_standard_montage_utils.py index eb3dc10d10e..3dcfeb0e512 100644 --- a/mne/channels/_standard_montage_utils.py +++ b/mne/channels/_standard_montage_utils.py @@ -118,6 +118,51 @@ def _mgh_or_standard(basename, head_size, coord_frame="unknown"): ) +def _meg(basename, head_size): + fname = op.join(MONTAGE_PATH, basename) + # Create a simple class instance instead of a list + class CustomMontage: + pass + + montage = CustomMontage() + + # Read the file + with open(fname) as f: + # Skip header line + header = f.readline().strip().split() + + ch_names = [] + ch_types = [] + pos = [] + ori = [] + + for line in f: + parts = line.strip().split() + ch_names.append(parts[0]) + ch_types.append(parts[1]) + pos.append([float(parts[2]), float(parts[3]), float(parts[4])]) + ori.append([float(parts[5]), float(parts[6]), float(parts[7])]) + + pos = np.array(pos) + + montage.ch_names = ch_names + + # # Create a dictionary mapping channel names to positions + # montage.ch_pos = dict(zip(ch_names, pos)) + montage.ch_pos = pos + # # TODO - make_dig_montage(): + # For custom montages without fiducials, this parameter must be set + # to ``'head'``. -> + # "kind": FIFF.FIFFV_POINT_EEG, -> + # dig names each chennel as EEG #1 ... + + # These aren't standard DigMontage attributes but can be useful + montage.ch_types = ch_types + montage.ori = ori + + return montage + + standard_montage_look_up_table = { "EGI_256": _egi_256, "easycap-M1": partial(_easycap, basename="easycap-M1.txt"), @@ -165,6 +210,8 @@ def _mgh_or_standard(basename, head_size, coord_frame="unknown"): "brainproducts-RNP-BA-128": partial( _easycap, basename="brainproducts-RNP-BA-128.txt" ), + "ctf275": partial(_meg, basename="ctf275.txt"), + "neuromag306": partial(_meg, basename="neuromag306.txt"), } From 1857f143b7c950516205350f634df26e537ffe76 Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Wed, 9 Apr 2025 14:34:19 +0000 Subject: [PATCH 05/31] [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --- mne/channels/_standard_montage_utils.py | 7 ++++--- 1 file changed, 4 insertions(+), 3 deletions(-) diff --git a/mne/channels/_standard_montage_utils.py b/mne/channels/_standard_montage_utils.py index 3dcfeb0e512..acca4a36736 100644 --- a/mne/channels/_standard_montage_utils.py +++ b/mne/channels/_standard_montage_utils.py @@ -120,10 +120,11 @@ def _mgh_or_standard(basename, head_size, coord_frame="unknown"): def _meg(basename, head_size): fname = op.join(MONTAGE_PATH, basename) + # Create a simple class instance instead of a list class CustomMontage: pass - + montage = CustomMontage() # Read the file @@ -152,8 +153,8 @@ class CustomMontage: montage.ch_pos = pos # # TODO - make_dig_montage(): # For custom montages without fiducials, this parameter must be set - # to ``'head'``. -> - # "kind": FIFF.FIFFV_POINT_EEG, -> + # to ``'head'``. -> + # "kind": FIFF.FIFFV_POINT_EEG, -> # dig names each chennel as EEG #1 ... # These aren't standard DigMontage attributes but can be useful From 73a5042e76aedac346913e25bafff5373425dc03 Mon Sep 17 00:00:00 2001 From: Konstantinos Tsilimparis Date: Wed, 9 Apr 2025 16:34:40 +0200 Subject: [PATCH 06/31] ENH: add neuromag and ctf to the builtin standard montages --- mne/channels/montage.py | 9 +++++++++ 1 file changed, 9 insertions(+) diff --git a/mne/channels/montage.py b/mne/channels/montage.py index 15cef38dec7..32ed92cc324 100644 --- a/mne/channels/montage.py +++ b/mne/channels/montage.py @@ -186,6 +186,15 @@ class _BuiltinStandardMontage: name="brainproducts-RNP-BA-128", description="Brain Products with 10-10 electrode names (128 channels)", ), + _BuiltinStandardMontage( + name="ctf275", + description="CTF system with 275 sensors (275 axial gradiometers)", + ), + _BuiltinStandardMontage( + name="neuromag306", + description="Neuromag system with 306 sensors (204 gradiometers, " + "102 magnetometers)", + ), ] From 61ba5a2f837dc26cb1e9e0561f463447eea34047 Mon Sep 17 00:00:00 2001 From: Konstantinos Tsilimparis Date: Wed, 9 Apr 2025 16:42:25 +0200 Subject: [PATCH 07/31] ENH: extend the initial setup/configuration of the function to MEG --- mne/channels/channels.py | 23 +++++++++++++++++++++-- 1 file changed, 21 insertions(+), 2 deletions(-) diff --git a/mne/channels/channels.py b/mne/channels/channels.py index d0e57eecb5f..55f9b70f153 100644 --- a/mne/channels/channels.py +++ b/mne/channels/channels.py @@ -960,7 +960,7 @@ def interpolate_bads( return self - def interpolate_to(self, sensors, origin="auto", method="spline", reg=0.0): + def interpolate_to(self, sensors, origin="auto", method=None, reg=0.0): """Interpolate EEG data onto a new montage. .. warning:: @@ -1003,8 +1003,27 @@ def interpolate_to(self, sensors, origin="auto", method="spline", reg=0.0): from .montage import DigMontage # Check that the method option is valid. - _check_option("method", method, ["spline", "MNE"]) _validate_type(sensors, DigMontage, "sensors") + method = _handle_default("interpolation_method", method) + + # Filter method to only include 'eeg' and 'meg' + supported_ch_types = ['eeg', 'meg'] + keys_to_delete = [key for key in method if key not in supported_ch_types] + for key in keys_to_delete: + del method[key] + # otherwise when method = "spline", the _handle_default function + # forces all channel types to use that method + # TODO: Check if there is a better way to handle this + if 'meg' in method: + method['meg'] = 'MNE' + valids = { + "eeg": ("spline", "MNE"), + "meg": ("MNE") + } + for key in method: + _check_option("method[key]", key, tuple(valids)) + _check_option(f"method['{key}']", method[key], valids[key]) + logger.info("Setting channel interpolation method to %s.", method) # Get target positions from the montage ch_pos = sensors.get_positions().get("ch_pos", {}) From cb614d9009f66301ea817a4fdd96144f2a020330 Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Wed, 9 Apr 2025 14:43:03 +0000 Subject: [PATCH 08/31] [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --- mne/channels/channels.py | 15 ++++++--------- 1 file changed, 6 insertions(+), 9 deletions(-) diff --git a/mne/channels/channels.py b/mne/channels/channels.py index 55f9b70f153..39a03a9f601 100644 --- a/mne/channels/channels.py +++ b/mne/channels/channels.py @@ -1007,19 +1007,16 @@ def interpolate_to(self, sensors, origin="auto", method=None, reg=0.0): method = _handle_default("interpolation_method", method) # Filter method to only include 'eeg' and 'meg' - supported_ch_types = ['eeg', 'meg'] + supported_ch_types = ["eeg", "meg"] keys_to_delete = [key for key in method if key not in supported_ch_types] for key in keys_to_delete: del method[key] - # otherwise when method = "spline", the _handle_default function - # forces all channel types to use that method + # otherwise when method = "spline", the _handle_default function + # forces all channel types to use that method # TODO: Check if there is a better way to handle this - if 'meg' in method: - method['meg'] = 'MNE' - valids = { - "eeg": ("spline", "MNE"), - "meg": ("MNE") - } + if "meg" in method: + method["meg"] = "MNE" + valids = {"eeg": ("spline", "MNE"), "meg": ("MNE")} for key in method: _check_option("method[key]", key, tuple(valids)) _check_option(f"method['{key}']", method[key], valids[key]) From aad0c4ecc3b7f19036a574dd9d9f5b470a1ad650 Mon Sep 17 00:00:00 2001 From: Konstantinos Tsilimparis Date: Wed, 9 Apr 2025 16:43:31 +0200 Subject: [PATCH 09/31] COS: comment --- mne/channels/channels.py | 1 + 1 file changed, 1 insertion(+) diff --git a/mne/channels/channels.py b/mne/channels/channels.py index 55f9b70f153..79fa800367e 100644 --- a/mne/channels/channels.py +++ b/mne/channels/channels.py @@ -1011,6 +1011,7 @@ def interpolate_to(self, sensors, origin="auto", method=None, reg=0.0): keys_to_delete = [key for key in method if key not in supported_ch_types] for key in keys_to_delete: del method[key] + # Force MEG to always use MNE method, # otherwise when method = "spline", the _handle_default function # forces all channel types to use that method # TODO: Check if there is a better way to handle this From d4c043c6631d987f4f155a6bc4f4d97e87815085 Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Wed, 9 Apr 2025 14:46:34 +0000 Subject: [PATCH 10/31] [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --- mne/channels/channels.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/mne/channels/channels.py b/mne/channels/channels.py index 70d0d4c6fdd..5126dab4227 100644 --- a/mne/channels/channels.py +++ b/mne/channels/channels.py @@ -1012,9 +1012,9 @@ def interpolate_to(self, sensors, origin="auto", method=None, reg=0.0): for key in keys_to_delete: del method[key] - # Force MEG to always use MNE method, - # otherwise when method = "spline", the _handle_default function - # forces all channel types to use that method + # Force MEG to always use MNE method, + # otherwise when method = "spline", the _handle_default function + # forces all channel types to use that method # TODO: Check if there is a better way to handle this if "meg" in method: method["meg"] = "MNE" From 10f8b7f80c4c2a07cbe9ac463e230d4a8734f32e Mon Sep 17 00:00:00 2001 From: Konstantinos Tsilimparis Date: Wed, 9 Apr 2025 17:05:59 +0200 Subject: [PATCH 11/31] ENH: add example and TODOs --- examples/preprocessing/interpolate_to.py | 17 +++++++++++++++++ mne/channels/channels.py | 3 +++ 2 files changed, 20 insertions(+) diff --git a/examples/preprocessing/interpolate_to.py b/examples/preprocessing/interpolate_to.py index b97a7251cbb..1fa539aabb4 100644 --- a/examples/preprocessing/interpolate_to.py +++ b/examples/preprocessing/interpolate_to.py @@ -29,6 +29,23 @@ print(__doc__) ylim = (-10, 10) +# %% +# Load the MEG data +data_path = sample.data_path() +raw_meg = mne.io.read_raw_fif(fif_file_path) + +raw_meg.pick("meg") + +# %% +# Define the target montage +standard_montage_meg = make_standard_montage("ctf275") + +# %% +# Use interpolate_to to project MEG data to the standard montage +raw_meg_interpolated_mne = raw_meg.copy().interpolate_to( + standard_montage_meg, method="MNE" +) + # %% # Load EEG data data_path = sample.data_path() diff --git a/mne/channels/channels.py b/mne/channels/channels.py index 70d0d4c6fdd..50156dfc65e 100644 --- a/mne/channels/channels.py +++ b/mne/channels/channels.py @@ -1004,6 +1004,8 @@ def interpolate_to(self, sensors, origin="auto", method=None, reg=0.0): # Check that the method option is valid. _validate_type(sensors, DigMontage, "sensors") + # TODO: Handle the error: sensors must be an instance of DigMontage, got .CustomMontage'> instead. + method = _handle_default("interpolation_method", method) # Filter method to only include 'eeg' and 'meg' @@ -1025,6 +1027,7 @@ def interpolate_to(self, sensors, origin="auto", method=None, reg=0.0): logger.info("Setting channel interpolation method to %s.", method) # Get target positions from the montage + # TODO: handle the error: AttributeError: 'CustomMontage' object has no attribute 'get_positions' ch_pos = sensors.get_positions().get("ch_pos", {}) target_ch_names = list(ch_pos.keys()) if not target_ch_names: From f36d9b124fde73dd402db8521b37922c2a0674de Mon Sep 17 00:00:00 2001 From: Konstantinos Tsilimparis Date: Wed, 9 Apr 2025 17:52:38 +0200 Subject: [PATCH 12/31] ENH: added missing line in example --- examples/preprocessing/interpolate_to.py | 1 + 1 file changed, 1 insertion(+) diff --git a/examples/preprocessing/interpolate_to.py b/examples/preprocessing/interpolate_to.py index 1fa539aabb4..aa39e16b422 100644 --- a/examples/preprocessing/interpolate_to.py +++ b/examples/preprocessing/interpolate_to.py @@ -32,6 +32,7 @@ # %% # Load the MEG data data_path = sample.data_path() +fif_file_path = data_path / "MEG" / "sample" / "sample_audvis_raw.fif" raw_meg = mne.io.read_raw_fif(fif_file_path) raw_meg.pick("meg") From ea76998ef62037e033e53d3342e417832c9fc335 Mon Sep 17 00:00:00 2001 From: Konstantinos Tsilimparis Date: Sun, 12 Oct 2025 16:29:10 +0200 Subject: [PATCH 13/31] ENH: Add CTF and Neuromag montage CSV files Added new montage data files for CTF151, CTF275, and Neuromag306 systems in CSV format to mne/channels/data/montages/. These files provide sensor location and orientation information. --- mne/channels/data/montages/ctf151.csv | 152 ++++++++++ mne/channels/data/montages/ctf275.csv | 275 ++++++++++++++++++ mne/channels/data/montages/neuromag306.csv | 307 +++++++++++++++++++++ 3 files changed, 734 insertions(+) create mode 100644 mne/channels/data/montages/ctf151.csv create mode 100644 mne/channels/data/montages/ctf275.csv create mode 100644 mne/channels/data/montages/neuromag306.csv diff --git a/mne/channels/data/montages/ctf151.csv b/mne/channels/data/montages/ctf151.csv new file mode 100644 index 00000000000..40c42b0b2e8 --- /dev/null +++ b/mne/channels/data/montages/ctf151.csv @@ -0,0 +1,152 @@ +name,coil_type,x,y,z,ex_x,ex_y,ex_z,ey_x,ey_y,ey_z,ez_x,ez_y,ez_z +MLC11-606,5001,-0.014323,0.048909,0.083084,0.728954,0.665674,-0.159696,-0.680884,0.680884,-0.269802,-0.070866,0.305408,0.949581 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2021,3024,0.018600,-0.080200,0.068900,0.000200,-0.620001,-0.784601,0.986601,0.128300,-0.101100,0.163347,-0.774068,0.611719 +MEG 2032,3012,0.016900,-0.097200,0.039700,-0.000300,-0.372910,-0.927926,0.990028,0.130604,-0.052801,0.140881,-0.918688,0.369152 +MEG 2033,3012,0.016900,-0.097200,0.039700,0.990028,0.130604,-0.052801,0.000300,0.372910,0.927926,0.140881,-0.918688,0.369152 +MEG 2031,3024,0.016900,-0.097200,0.039700,-0.000300,-0.372910,-0.927926,0.990028,0.130604,-0.052801,0.140881,-0.918688,0.369152 +MEG 2042,3012,-0.017000,-0.097200,0.039700,-0.000500,-0.373789,-0.927473,0.989971,-0.131196,0.052398,-0.141267,-0.918145,0.370106 +MEG 2043,3012,-0.017000,-0.097200,0.039700,0.989971,-0.131196,0.052398,0.000500,0.373789,0.927473,-0.141267,-0.918145,0.370106 +MEG 2041,3024,-0.017000,-0.097200,0.039700,-0.000500,-0.373789,-0.927473,0.989971,-0.131196,0.052398,-0.141267,-0.918145,0.370106 +MEG 2113,3012,0.000000,-0.108600,0.007000,-0.000100,-0.168403,-0.985718,0.999819,0.020400,-0.003600,0.020715,-0.985540,0.168371 +MEG 2112,3012,0.000000,-0.108600,0.007000,0.999819,0.020400,-0.003600,0.000100,0.168403,0.985718,0.020715,-0.985540,0.168371 +MEG 2111,3024,0.000000,-0.108600,0.007000,-0.000100,-0.168403,-0.985718,0.999819,0.020400,-0.003600,0.020715,-0.985540,0.168371 +MEG 2122,3012,0.000000,-0.110600,-0.027500,0.000000,0.040700,-0.999200,1.000000,-0.000400,0.000000,-0.000400,-0.999200,-0.040700 +MEG 2123,3012,0.000000,-0.110600,-0.027500,1.000000,-0.000400,0.000000,0.000000,-0.040700,0.999200,-0.000400,-0.999200,-0.040700 +MEG 2121,3024,0.000000,-0.110600,-0.027500,0.000000,0.040700,-0.999200,1.000000,-0.000400,0.000000,-0.000400,-0.999200,-0.040700 +MEG 2133,3012,0.017000,-0.109800,-0.061800,0.009200,-0.010800,-0.999899,0.985499,0.169500,0.007300,0.169404,-0.985467,0.012203 +MEG 2132,3012,0.017000,-0.109800,-0.061800,0.985499,0.169500,0.007300,-0.009200,0.010800,0.999899,0.169404,-0.985467,0.012203 +MEG 2131,3024,0.017000,-0.109800,-0.061800,0.009200,-0.010800,-0.999899,0.985499,0.169500,0.007300,0.169404,-0.985467,0.012203 +MEG 2143,3012,-0.017100,-0.109800,-0.061900,-0.006200,-0.012200,-0.999906,0.982706,-0.185001,-0.003800,-0.184937,-0.982638,0.013136 +MEG 2142,3012,-0.017100,-0.109800,-0.061900,0.982706,-0.185001,-0.003800,0.006200,0.012200,0.999906,-0.184937,-0.982638,0.013136 +MEG 2141,3024,-0.017100,-0.109800,-0.061900,-0.006200,-0.012200,-0.999906,0.982706,-0.185001,-0.003800,-0.184937,-0.982638,0.013136 +MEG 2212,3012,0.051700,-0.027600,0.092800,0.859962,0.026999,-0.509577,0.107595,0.966557,0.232790,0.498820,-0.255018,0.828297 +MEG 2213,3012,0.051700,-0.027600,0.092800,0.107595,0.966557,0.232790,-0.859962,-0.026999,0.509577,0.498820,-0.255018,0.828297 +MEG 2211,3024,0.051700,-0.027600,0.092800,0.859962,0.026999,-0.509577,0.107595,0.966557,0.232790,0.498820,-0.255018,0.828297 +MEG 2223,3012,0.078600,-0.028400,0.069800,0.627473,-0.042698,-0.777467,0.223290,0.966359,0.127195,0.745881,-0.253412,0.615898 +MEG 2222,3012,0.078600,-0.028400,0.069800,0.223290,0.966359,0.127195,-0.627473,0.042698,0.777467,0.745881,-0.253412,0.615898 +MEG 2221,3024,0.078600,-0.028400,0.069800,0.627473,-0.042698,-0.777467,0.223290,0.966359,0.127195,0.745881,-0.253412,0.615898 +MEG 2233,3012,0.055300,-0.062800,0.070600,0.377694,-0.493292,-0.783588,0.759388,0.649190,-0.042699,0.529761,-0.578920,0.619796 +MEG 2232,3012,0.055300,-0.062800,0.070600,0.759388,0.649190,-0.042699,-0.377694,0.493292,0.783588,0.529761,-0.578920,0.619796 +MEG 2231,3024,0.055300,-0.062800,0.070600,0.377694,-0.493292,-0.783588,0.759388,0.649190,-0.042699,0.529761,-0.578920,0.619796 +MEG 2242,3012,0.018200,-0.054200,0.092200,-0.069799,-0.841688,-0.535392,0.989086,0.011400,-0.146898,0.129746,-0.539803,0.831706 +MEG 2243,3012,0.018200,-0.054200,0.092200,0.989086,0.011400,-0.146898,0.069799,0.841688,0.535392,0.129746,-0.539803,0.831706 +MEG 2241,3024,0.018200,-0.054200,0.092200,-0.069799,-0.841688,-0.535392,0.989086,0.011400,-0.146898,0.129746,-0.539803,0.831706 +MEG 2312,3012,0.051300,-0.086100,0.039700,0.153007,-0.302213,-0.940940,0.880738,0.473520,-0.008900,0.448244,-0.827360,0.338622 +MEG 2313,3012,0.051300,-0.086100,0.039700,0.880738,0.473520,-0.008900,-0.153007,0.302213,0.940940,0.448244,-0.827360,0.338622 +MEG 2311,3024,0.051300,-0.086100,0.039700,0.153007,-0.302213,-0.940940,0.880738,0.473520,-0.008900,0.448244,-0.827360,0.338622 +MEG 2323,3012,0.063700,-0.088400,0.006200,0.056398,-0.071297,-0.995860,0.798667,0.601776,0.002200,0.599127,-0.795485,0.090881 +MEG 2322,3012,0.063700,-0.088400,0.006200,0.798667,0.601776,0.002200,-0.056398,0.071297,0.995860,0.599127,-0.795485,0.090881 +MEG 2321,3024,0.063700,-0.088400,0.006200,0.056398,-0.071297,-0.995860,0.798667,0.601776,0.002200,0.599127,-0.795485,0.090881 +MEG 2332,3012,0.033000,-0.105100,-0.027700,-0.004900,0.023300,-0.999715,0.951414,0.307905,0.002500,0.307875,-0.951130,-0.023677 +MEG 2333,3012,0.033000,-0.105100,-0.027700,0.951414,0.307905,0.002500,0.004900,-0.023300,0.999715,0.307875,-0.951130,-0.023677 +MEG 2331,3024,0.033000,-0.105100,-0.027700,-0.004900,0.023300,-0.999715,0.951414,0.307905,0.002500,0.307875,-0.951130,-0.023677 +MEG 2343,3012,0.033300,-0.103400,0.006300,0.041500,-0.123300,-0.991501,0.948001,0.318300,0.000100,0.315583,-0.939949,0.130098 +MEG 2342,3012,0.033300,-0.103400,0.006300,0.948001,0.318300,0.000100,-0.041500,0.123300,0.991501,0.315583,-0.939949,0.130098 +MEG 2341,3024,0.033300,-0.103400,0.006300,0.041500,-0.123300,-0.991501,0.948001,0.318300,0.000100,0.315583,-0.939949,0.130098 +MEG 2412,3012,0.095200,-0.030600,0.039200,0.323999,-0.063600,-0.943897,0.309099,0.950098,0.042100,0.894117,-0.305398,0.327489 +MEG 2413,3012,0.095200,-0.030600,0.039200,0.309099,0.950098,0.042100,-0.323999,0.063600,0.943897,0.894117,-0.305398,0.327489 +MEG 2411,3024,0.095200,-0.030600,0.039200,0.323999,-0.063600,-0.943897,0.309099,0.950098,0.042100,0.894117,-0.305398,0.327489 +MEG 2423,3012,0.101700,-0.033800,0.005800,0.066101,-0.028300,-0.997412,0.314004,0.949411,-0.006100,0.947126,-0.312788,0.071643 +MEG 2422,3012,0.101700,-0.033800,0.005800,0.314004,0.949411,-0.006100,-0.066101,0.028300,0.997412,0.947126,-0.312788,0.071643 +MEG 2421,3024,0.101700,-0.033800,0.005800,0.066101,-0.028300,-0.997412,0.314004,0.949411,-0.006100,0.947126,-0.312788,0.071643 +MEG 2433,3012,0.086600,-0.063900,0.005600,0.060099,-0.044799,-0.997187,0.549293,0.835589,-0.004400,0.833435,-0.547483,0.074826 +MEG 2432,3012,0.086600,-0.063900,0.005600,0.549293,0.835589,-0.004400,-0.060099,0.044799,0.997187,0.833435,-0.547483,0.074826 +MEG 2431,3024,0.086600,-0.063900,0.005600,0.060099,-0.044799,-0.997187,0.549293,0.835589,-0.004400,0.833435,-0.547483,0.074826 +MEG 2442,3012,0.078100,-0.062900,0.039400,0.259507,-0.203505,-0.944125,0.598716,0.800921,-0.008000,0.757798,-0.563187,0.329687 +MEG 2443,3012,0.078100,-0.062900,0.039400,0.598716,0.800921,-0.008000,-0.259507,0.203505,0.944125,0.757798,-0.563187,0.329687 +MEG 2441,3024,0.078100,-0.062900,0.039400,0.259507,-0.203505,-0.944125,0.598716,0.800921,-0.008000,0.757798,-0.563187,0.329687 +MEG 2512,3012,0.063000,-0.090600,-0.027600,-0.013600,0.025200,-0.999598,0.808198,0.588899,0.003800,0.588757,-0.807821,-0.028376 +MEG 2513,3012,0.063000,-0.090600,-0.027600,0.808198,0.588899,0.003800,0.013600,-0.025200,0.999598,0.588757,-0.807821,-0.028376 +MEG 2511,3024,0.063000,-0.090600,-0.027600,-0.013600,0.025200,-0.999598,0.808198,0.588899,0.003800,0.588757,-0.807821,-0.028376 +MEG 2522,3012,0.086100,-0.066100,-0.028000,-0.021299,0.011700,-0.999673,0.571385,0.820678,-0.002600,0.820380,-0.571254,-0.024165 +MEG 2523,3012,0.086100,-0.066100,-0.028000,0.571385,0.820678,-0.002600,0.021299,-0.011700,0.999673,0.820380,-0.571254,-0.024165 +MEG 2521,3024,0.086100,-0.066100,-0.028000,-0.021299,0.011700,-0.999673,0.571385,0.820678,-0.002600,0.820380,-0.571254,-0.024165 +MEG 2533,3012,0.075700,-0.079800,-0.062000,0.014300,-0.000900,-0.999897,0.690798,0.722998,0.009300,0.722916,-0.690860,0.010961 +MEG 2532,3012,0.075700,-0.079800,-0.062000,0.690798,0.722998,0.009300,-0.014300,0.000900,0.999897,0.722916,-0.690860,0.010961 +MEG 2531,3024,0.075700,-0.079800,-0.062000,0.014300,-0.000900,-0.999897,0.690798,0.722998,0.009300,0.722916,-0.690860,0.010961 +MEG 2543,3012,0.048800,-0.099400,-0.062100,0.011000,-0.007000,-0.999915,0.903014,0.429706,0.007000,0.429621,-0.903014,0.011048 +MEG 2542,3012,0.048800,-0.099400,-0.062100,0.903014,0.429706,0.007000,-0.011000,0.007000,0.999915,0.429621,-0.903014,0.011048 +MEG 2541,3024,0.048800,-0.099400,-0.062100,0.011000,-0.007000,-0.999915,0.903014,0.429706,0.007000,0.429621,-0.903014,0.011048 +MEG 2612,3012,0.108700,-0.003300,-0.028000,-0.020500,0.003400,-0.999813,0.081001,0.996713,0.001700,0.996532,-0.080951,-0.020708 +MEG 2613,3012,0.108700,-0.003300,-0.028000,0.081001,0.996713,0.001700,0.020500,-0.003400,0.999813,0.996532,-0.080951,-0.020708 +MEG 2611,3024,0.108700,-0.003300,-0.028000,-0.020500,0.003400,-0.999813,0.081001,0.996713,0.001700,0.996532,-0.080951,-0.020708 +MEG 2623,3012,0.106800,-0.020600,-0.062100,0.013100,0.004500,-0.999904,0.215701,0.976404,0.007200,0.976343,-0.215775,0.011820 +MEG 2622,3012,0.106800,-0.020600,-0.062100,0.215701,0.976404,0.007200,-0.013100,-0.004500,0.999904,0.976343,-0.215775,0.011820 +MEG 2621,3024,0.106800,-0.020600,-0.062100,0.013100,0.004500,-0.999904,0.215701,0.976404,0.007200,0.976343,-0.215775,0.011820 +MEG 2633,3012,0.095100,-0.052400,-0.062000,0.015200,0.003300,-0.999879,0.476590,0.879082,0.010100,0.879009,-0.476686,0.011789 +MEG 2632,3012,0.095100,-0.052400,-0.062000,0.476590,0.879082,0.010100,-0.015200,-0.003300,0.999879,0.879009,-0.476686,0.011789 +MEG 2631,3024,0.095100,-0.052400,-0.062000,0.015200,0.003300,-0.999879,0.476590,0.879082,0.010100,0.879009,-0.476686,0.011789 +MEG 2642,3012,0.101700,-0.036100,-0.027800,-0.027699,0.005300,-0.999573,0.341491,0.939874,-0.004500,0.939449,-0.341469,-0.027844 +MEG 2643,3012,0.101700,-0.036100,-0.027800,0.341491,0.939874,-0.004500,0.027699,-0.005300,0.999573,0.939449,-0.341469,-0.027844 +MEG 2641,3024,0.101700,-0.036100,-0.027800,-0.027699,0.005300,-0.999573,0.341491,0.939874,-0.004500,0.939449,-0.341469,-0.027844 From bf411bf2be3faaa7f2ec3cf42a403efc386169a4 Mon Sep 17 00:00:00 2001 From: Konstantinos Tsilimparis Date: Sun, 12 Oct 2025 16:35:06 +0200 Subject: [PATCH 14/31] ENH: Add read_meg_montage for loading MEG csv montages Introduces the read_meg_montage function to load canonical MEG sensor positions and orientations from CSV files for supported systems ('neuromag', 'ctf151', 'ctf275'). This utility constructs an Info object with sensor metadata for use in field interpolation. --- mne/channels/montage.py | 94 +++++++++++++++++++++++++++++++++++++++++ 1 file changed, 94 insertions(+) diff --git a/mne/channels/montage.py b/mne/channels/montage.py index 339dc0aa439..fec8491031d 100644 --- a/mne/channels/montage.py +++ b/mne/channels/montage.py @@ -7,8 +7,10 @@ from collections import OrderedDict from copy import deepcopy from dataclasses import dataclass +from pathlib import Path import numpy as np +import pandas as pd from .._fiff._digitization import ( _coord_frame_const, @@ -1689,6 +1691,98 @@ def read_custom_montage( return montage +@verbose +def read_meg_montage(system, verbose=None): + """Load canonical MEG sensor definitions from CSV files. + + Parameters + ---------- + system : str + The MEG system name. Currently supported: 'neuromag', 'ctf151' or + 'ctf275'. + + Returns + ------- + info : Info + An Info object containing the canonical sensor definitions. + + Notes + ----- + This function loads pre-defined canonical sensor positions and + orientations from CSV files stored in mne/channels/data/montages/. + + """ + # Validate system input + _check_option("system", system, ["neuromag", "ctf151", "ctf275"]) + + # Map system names to CSV filenames + system_files = { + "neuromag": "neuromag306.csv", + "ctf151": "ctf151.csv", + "ctf275": "ctf275.csv" + } + + # Load the CSV file + montage_dir = Path(__file__).parent / "data" / "montages" + csv_file = montage_dir / system_files[system] + + if not csv_file.exists(): + raise FileNotFoundError( + f"Canonical sensor file not found: {csv_file}. " + ) + + # Read sensor definitions + df = pd.read_csv(csv_file) + + # Build channel info list + ch_names = df["name"].tolist() + + # Create base info structure + # Use a dummy sample rate; it won't matter for field interpolation + info = create_info(ch_names=ch_names, sfreq=1000.0, ch_types="mag") + + # Update each channel with full coil information + for idx, row in df.iterrows(): + ch = info["chs"][idx] + + # Set coil type + ch["coil_type"] = int(row["coil_type"]) + + # Set position and orientation in loc field + # loc[0:3] = position + ch["loc"][:3] = [row["x"], row["y"], row["z"]] + + # loc[3:6] = ex (first orientation vector) + ch["loc"][3:6] = [row["ex_x"], row["ex_y"], row["ex_z"]] + + # loc[6:9] = ey (second orientation vector) + ch["loc"][6:9] = [row["ey_x"], row["ey_y"], row["ey_z"]] + + # loc[9:12] = ez (third orientation vector / normal) + ch["loc"][9:12] = [row["ez_x"], row["ez_y"], row["ez_z"]] + + # Set coordinate frame to device coordinates + ch["coord_frame"] = FIFF.FIFFV_COORD_DEVICE + + # Set channel kind to MEG + ch["kind"] = FIFF.FIFFV_MEG_CH + + # Set unit based on coil type + # Neuromag gradiometers (3012) use T/m, magnetometers (3024) use T + # CTF gradiometers (5001) use T + if ch["coil_type"] == 3012: # Neuromag planar gradiometer + ch["unit"] = FIFF.FIFF_UNIT_T_M + else: # Magnetometers and CTF gradiometers + ch["unit"] = FIFF.FIFF_UNIT_T + + # Set device to head transform to identity (canonical positions) + # This will be updated based on the actual data being interpolated + info["dev_head_t"] = Transform( + "meg", "head", np.eye(4) + ) + + return info + def compute_dev_head_t(montage): """Compute device to head transform from a DigMontage. From 015113ee4e145484847b9ece3f7868c731fab062 Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Sun, 12 Oct 2025 14:35:21 +0000 Subject: [PATCH 15/31] [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --- mne/channels/montage.py | 11 ++++------- 1 file changed, 4 insertions(+), 7 deletions(-) diff --git a/mne/channels/montage.py b/mne/channels/montage.py index fec8491031d..667239d9f63 100644 --- a/mne/channels/montage.py +++ b/mne/channels/montage.py @@ -1691,6 +1691,7 @@ def read_custom_montage( return montage + @verbose def read_meg_montage(system, verbose=None): """Load canonical MEG sensor definitions from CSV files. @@ -1719,7 +1720,7 @@ def read_meg_montage(system, verbose=None): system_files = { "neuromag": "neuromag306.csv", "ctf151": "ctf151.csv", - "ctf275": "ctf275.csv" + "ctf275": "ctf275.csv", } # Load the CSV file @@ -1727,9 +1728,7 @@ def read_meg_montage(system, verbose=None): csv_file = montage_dir / system_files[system] if not csv_file.exists(): - raise FileNotFoundError( - f"Canonical sensor file not found: {csv_file}. " - ) + raise FileNotFoundError(f"Canonical sensor file not found: {csv_file}. ") # Read sensor definitions df = pd.read_csv(csv_file) @@ -1777,9 +1776,7 @@ def read_meg_montage(system, verbose=None): # Set device to head transform to identity (canonical positions) # This will be updated based on the actual data being interpolated - info["dev_head_t"] = Transform( - "meg", "head", np.eye(4) - ) + info["dev_head_t"] = Transform("meg", "head", np.eye(4)) return info From e135ef9948f560d638213dc35b6956d8b8888c4c Mon Sep 17 00:00:00 2001 From: Konstantinos Tsilimparis Date: Sun, 12 Oct 2025 16:49:09 +0200 Subject: [PATCH 16/31] ENH: Enhance interpolate_to to include MEG The interpolate_to method now supports also MEG data interpolation. It enables transformation between Neuromag and CTF sensor layouts using field interpolation. --- mne/channels/channels.py | 371 ++++++++++++++++++++++++--------------- 1 file changed, 234 insertions(+), 137 deletions(-) diff --git a/mne/channels/channels.py b/mne/channels/channels.py index 19f1ffe4760..79b2c52dd4b 100644 --- a/mne/channels/channels.py +++ b/mne/channels/channels.py @@ -962,37 +962,66 @@ def interpolate_bads( return self - def interpolate_to(self, sensors, origin="auto", method=None, reg=0.0): - """Interpolate EEG data onto a new montage. + def interpolate_to( + self, sensors, origin="auto", method="MNE", mode="accurate", reg=0.0 + ): + """Interpolate data onto a new sensor configuration. - .. warning:: - Be careful, only EEG channels are interpolated. Other channel types are - not interpolated. + This method can interpolate EEG data onto a new montage or transform + MEG data to a different sensor configuration (e.g., Neuromag to CTF). Parameters ---------- - sensors : DigMontage - The target montage containing channel positions to interpolate onto. + sensors : DigMontage | str + For EEG: A DigMontage object containing target channel positions. + For MEG: A string specifying the target MEG system. Currently + supported: ``'neuromag'``, ``'ctf151'`` or ``'ctf275'``. origin : array-like, shape (3,) | str Origin of the sphere in the head coordinate frame and in meters. Can be ``'auto'`` (default), which means a head-digitization-based - origin fit. + origin fit. Used for both EEG and MEG interpolation. method : str - Method to use for EEG channels. - Supported methods are 'spline' (default) and 'MNE'. + Interpolation method to use. + For EEG: ``'MNE'`` (default) or ``'spline'``. + For MEG: ``'MNE'`` (default). + mode : str + Either ``'accurate'`` (default) or ``'fast'``, determines the + quality of the Legendre polynomial expansion used for + interpolation of MEG channels using the minimum-norm method. + Only used for MEG interpolation. reg : float - The regularization parameter for the interpolation method - (only used when the method is 'spline'). + The regularization parameter for the interpolation method. + Only used when ``method='spline'`` for EEG channels. Returns ------- inst : instance of Raw, Epochs, or Evoked - The instance with updated channel locations and data. + A new instance with interpolated data and updated channel + information. Notes ----- - This method is useful for standardizing EEG layouts across datasets. - However, some attributes may be lost after interpolation. + **For EEG data:** + + This method interpolates EEG channels onto a new montage using + spherical splines or minimum-norm estimation. Non-EEG channels + are preserved without modification. + + **For MEG data:** + + This method transforms MEG data to a different sensor configuration + using field interpolation. + + Common use cases for MEG transformation: + + - Transform Neuromag data to CTF sensor layout for comparison + - Transform CTF data to Neuromag sensor layout + - Simulate what data would look like on a different MEG system + + .. warning:: + MEG field interpolation assumes that the head position relative + to the sensors is similar between systems. Large differences in + head position may affect interpolation accuracy. .. versionadded:: 1.10.0 """ @@ -1002,143 +1031,211 @@ def interpolate_to(self, sensors, origin="auto", method=None, reg=0.0): from ..io import RawArray from ..io.base import BaseRaw from .interpolation import _make_interpolation_matrix - from .montage import DigMontage + from .montage import DigMontage, read_meg_montage - # Check that the method option is valid. - _validate_type(sensors, DigMontage, "sensors") - # TODO: Handle the error: sensors must be an instance of DigMontage, got .CustomMontage'> instead. + _check_preload(self, "interpolation") - method = _handle_default("interpolation_method", method) + # Determine if we're doing EEG or MEG interpolation + is_meg_interpolation = isinstance(sensors, str) + is_eeg_interpolation = isinstance(sensors, DigMontage) + + if is_eeg_interpolation: + + # Check that the method option is valid. + _validate_type(sensors, DigMontage, "sensors") + # TODO: Handle the error: sensors must be an instance of DigMontage, got .CustomMontage'> instead. + + method = _handle_default("interpolation_method", method) + + # Filter method to only include 'eeg' and 'meg' + supported_ch_types = ["eeg", "meg"] + keys_to_delete = [key for key in method if key not in supported_ch_types] + for key in keys_to_delete: + del method[key] + + # Force MEG to always use MNE method, + # otherwise when method = "spline", the _handle_default function + # forces all channel types to use that method + # TODO: Check if there is a better way to handle this + if "meg" in method: + method["meg"] = "MNE" + valids = {"eeg": ("spline", "MNE"), "meg": ("MNE")} + for key in method: + _check_option("method[key]", key, tuple(valids)) + _check_option(f"method['{key}']", method[key], valids[key]) + logger.info("Setting channel interpolation method to %s.", method) + + # Get target positions from the montage + # TODO: handle the error: AttributeError: 'CustomMontage' object has no attribute 'get_positions' + ch_pos = sensors.get_positions().get("ch_pos", {}) + target_ch_names = list(ch_pos.keys()) + if not target_ch_names: + raise ValueError( + "The provided sensors configuration has no channel positions." + ) - # Filter method to only include 'eeg' and 'meg' - supported_ch_types = ["eeg", "meg"] - keys_to_delete = [key for key in method if key not in supported_ch_types] - for key in keys_to_delete: - del method[key] + # Get original channel order + orig_names = self.info["ch_names"] + + # Identify EEG channel + picks_good_eeg = pick_types(self.info, meg=False, eeg=True, exclude="bads") + if len(picks_good_eeg) == 0: + raise ValueError("No good EEG channels available for interpolation.") + # Also get the full list of EEG channel indices (including bad channels) + picks_remove_eeg = pick_types(self.info, meg=False, eeg=True, exclude=[]) + eeg_names_orig = [orig_names[i] for i in picks_remove_eeg] + + # Identify non-EEG channels in original order + non_eeg_names_ordered = [ch for ch in orig_names if ch not in eeg_names_orig] + + # Create destination info for new EEG channels + sfreq = self.info["sfreq"] + info_interp = create_info( + ch_names=target_ch_names, + sfreq=sfreq, + ch_types=["eeg"] * len(target_ch_names), + ) + info_interp.set_montage(sensors) + info_interp["bads"] = [ch for ch in self.info["bads"] if ch in target_ch_names] + # Do not assign "projs" directly. + + # Compute the interpolation mapping + if method == "spline": + origin_val = _check_origin(origin, self.info) + pos_from = self.info._get_channel_positions(picks_good_eeg) - origin_val + pos_to = np.stack(list(ch_pos.values()), axis=0) + + def _check_pos_sphere(pos): + d = np.linalg.norm(pos, axis=-1) + d_norm = np.mean(d / np.mean(d)) + if np.abs(1.0 - d_norm) > 0.1: + warn("Your spherical fit is poor; interpolation may be inaccurate.") + + _check_pos_sphere(pos_from) + _check_pos_sphere(pos_to) + mapping = _make_interpolation_matrix(pos_from, pos_to, alpha=reg) - # Force MEG to always use MNE method, - # otherwise when method = "spline", the _handle_default function - # forces all channel types to use that method - # TODO: Check if there is a better way to handle this - if "meg" in method: - method["meg"] = "MNE" - valids = {"eeg": ("spline", "MNE"), "meg": ("MNE")} - for key in method: - _check_option("method[key]", key, tuple(valids)) - _check_option(f"method['{key}']", method[key], valids[key]) - logger.info("Setting channel interpolation method to %s.", method) + else: + assert method == "MNE" + info_eeg = pick_info(self.info, picks_good_eeg) + # If the original info has an average EEG reference projector but + # the destination info does not, + # update info_interp via a temporary RawArray. + if _has_eeg_average_ref_proj(self.info) and not _has_eeg_average_ref_proj( + info_interp + ): + # Create dummy data: shape (n_channels, 1) + temp_data = np.zeros((len(info_interp["ch_names"]), 1)) + temp_raw = RawArray(temp_data, info_interp, first_samp=0) + # Using the public API, add an average reference projector. + temp_raw.set_eeg_reference( + ref_channels="average", projection=True, verbose=False + ) + # Extract the updated info. + info_interp = temp_raw.info + mapping = _map_meg_or_eeg_channels( + info_eeg, info_interp, mode="accurate", origin=origin + ) - # Get target positions from the montage - # TODO: handle the error: AttributeError: 'CustomMontage' object has no attribute 'get_positions' - ch_pos = sensors.get_positions().get("ch_pos", {}) - target_ch_names = list(ch_pos.keys()) - if not target_ch_names: - raise ValueError( - "The provided sensors configuration has no channel positions." + # Interpolate EEG data + data_good = self.get_data(picks=picks_good_eeg) + data_interp = mapping @ data_good + + # Create a new instance for the interpolated EEG channels + # TODO: Creating a new instance leads to a loss of information. + # We should consider updating the existing instance in the future + # by 1) drop channels, 2) add channels, 3) re-order channels. + if isinstance(self, BaseRaw): + inst_interp = RawArray(data_interp, info_interp, first_samp=self.first_samp) + elif isinstance(self, BaseEpochs): + inst_interp = EpochsArray(data_interp, info_interp) + else: + assert isinstance(self, Evoked) + inst_interp = EvokedArray(data_interp, info_interp) + + # Merge only if non-EEG channels exist + if not non_eeg_names_ordered: + return inst_interp + + inst_non_eeg = self.copy().pick(non_eeg_names_ordered).load_data() + inst_out = inst_non_eeg.add_channels([inst_interp], force_update_info=True) + + # Reorder channels + # Insert the entire new EEG block at the position of the first EEG channel. + orig_names_arr = np.array(orig_names) + mask_eeg = np.isin(orig_names_arr, eeg_names_orig) + if mask_eeg.any(): + first_eeg_index = np.where(mask_eeg)[0][0] + pre = orig_names_arr[:first_eeg_index] + new_eeg = np.array(info_interp["ch_names"]) + post = orig_names_arr[first_eeg_index:] + post = post[~np.isin(orig_names_arr[first_eeg_index:], eeg_names_orig)] + new_order = np.concatenate((pre, new_eeg, post)).tolist() + else: + new_order = orig_names + inst_out.reorder_channels(new_order) + return inst_out + + elif is_meg_interpolation: + # MEG interpolation to canonical sensor configuration + _check_option("sensors", sensors, ["neuromag", "ctf151", "ctf275"]) + _check_option("method", method, ["MNE"]) + _check_option("mode", mode, ["accurate", "fast"]) + + # Get MEG channels from source + picks_meg = pick_types( + self.info, meg=True, ref_meg=False, exclude="bads" ) + if len(picks_meg) == 0: + raise ValueError( + "No good MEG channels available for interpolation." + ) - # Get original channel order - orig_names = self.info["ch_names"] - - # Identify EEG channel - picks_good_eeg = pick_types(self.info, meg=False, eeg=True, exclude="bads") - if len(picks_good_eeg) == 0: - raise ValueError("No good EEG channels available for interpolation.") - # Also get the full list of EEG channel indices (including bad channels) - picks_remove_eeg = pick_types(self.info, meg=False, eeg=True, exclude=[]) - eeg_names_orig = [orig_names[i] for i in picks_remove_eeg] - - # Identify non-EEG channels in original order - non_eeg_names_ordered = [ch for ch in orig_names if ch not in eeg_names_orig] - - # Create destination info for new EEG channels - sfreq = self.info["sfreq"] - info_interp = create_info( - ch_names=target_ch_names, - sfreq=sfreq, - ch_types=["eeg"] * len(target_ch_names), - ) - info_interp.set_montage(sensors) - info_interp["bads"] = [ch for ch in self.info["bads"] if ch in target_ch_names] - # Do not assign "projs" directly. - - # Compute the interpolation mapping - if method == "spline": - origin_val = _check_origin(origin, self.info) - pos_from = self.info._get_channel_positions(picks_good_eeg) - origin_val - pos_to = np.stack(list(ch_pos.values()), axis=0) + # Load target sensor configuration + info_to = read_meg_montage(sensors) - def _check_pos_sphere(pos): - d = np.linalg.norm(pos, axis=-1) - d_norm = np.mean(d / np.mean(d)) - if np.abs(1.0 - d_norm) > 0.1: - warn("Your spherical fit is poor; interpolation may be inaccurate.") + # Update dev_head_t from source data + if self.info["dev_head_t"] is not None: + info_to["dev_head_t"] = self.info["dev_head_t"].copy() - _check_pos_sphere(pos_from) - _check_pos_sphere(pos_to) - mapping = _make_interpolation_matrix(pos_from, pos_to, alpha=reg) + # Get source MEG info + info_from = pick_info(self.info, picks_meg) - else: - assert method == "MNE" - info_eeg = pick_info(self.info, picks_good_eeg) - # If the original info has an average EEG reference projector but - # the destination info does not, - # update info_interp via a temporary RawArray. - if _has_eeg_average_ref_proj(self.info) and not _has_eeg_average_ref_proj( - info_interp - ): - # Create dummy data: shape (n_channels, 1) - temp_data = np.zeros((len(info_interp["ch_names"]), 1)) - temp_raw = RawArray(temp_data, info_interp, first_samp=0) - # Using the public API, add an average reference projector. - temp_raw.set_eeg_reference( - ref_channels="average", projection=True, verbose=False - ) - # Extract the updated info. - info_interp = temp_raw.info + # Compute field interpolation mapping + origin_val = _check_origin(origin, self.info) mapping = _map_meg_or_eeg_channels( - info_eeg, info_interp, mode="accurate", origin=origin + info_from, info_to, mode=mode, origin=origin_val ) - # Interpolate EEG data - data_good = self.get_data(picks=picks_good_eeg) - data_interp = mapping @ data_good + # Apply mapping to MEG data + data_meg = self.get_data(picks=picks_meg) + data_interp = mapping @ data_meg - # Create a new instance for the interpolated EEG channels - # TODO: Creating a new instance leads to a loss of information. - # We should consider updating the existing instance in the future - # by 1) drop channels, 2) add channels, 3) re-order channels. - if isinstance(self, BaseRaw): - inst_interp = RawArray(data_interp, info_interp, first_samp=self.first_samp) - elif isinstance(self, BaseEpochs): - inst_interp = EpochsArray(data_interp, info_interp) - else: - assert isinstance(self, Evoked) - inst_interp = EvokedArray(data_interp, info_interp) - - # Merge only if non-EEG channels exist - if not non_eeg_names_ordered: - return inst_interp - - inst_non_eeg = self.copy().pick(non_eeg_names_ordered).load_data() - inst_out = inst_non_eeg.add_channels([inst_interp], force_update_info=True) - - # Reorder channels - # Insert the entire new EEG block at the position of the first EEG channel. - orig_names_arr = np.array(orig_names) - mask_eeg = np.isin(orig_names_arr, eeg_names_orig) - if mask_eeg.any(): - first_eeg_index = np.where(mask_eeg)[0][0] - pre = orig_names_arr[:first_eeg_index] - new_eeg = np.array(info_interp["ch_names"]) - post = orig_names_arr[first_eeg_index:] - post = post[~np.isin(orig_names_arr[first_eeg_index:], eeg_names_orig)] - new_order = np.concatenate((pre, new_eeg, post)).tolist() - else: - new_order = orig_names - inst_out.reorder_channels(new_order) - return inst_out + # Create new instance with interpolated MEG data + if isinstance(self, BaseRaw): + inst_out = RawArray( + data_interp, info_to, first_samp=self.first_samp + ) + elif isinstance(self, BaseEpochs): + inst_out = EpochsArray(data_interp, info_to) + else: + assert isinstance(self, Evoked) + inst_out = EvokedArray(data_interp, info_to) + + # Add non-MEG channels if they exist + non_meg_picks = pick_types( + self.info, meg=False, exclude=[], ref_meg=False + ) + if len(non_meg_picks) > 0: + non_meg_names = [ + self.info["ch_names"][i] for i in non_meg_picks + ] + inst_non_meg = self.copy().pick(non_meg_names).load_data() + inst_out = inst_out.add_channels( + [inst_non_meg], force_update_info=True + ) + return inst_out @verbose def rename_channels(info, mapping, allow_duplicates=False, *, verbose=None): From a589a4eb61f12a74d620d732344f27b5e52ff78b Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Sun, 12 Oct 2025 14:50:28 +0000 Subject: [PATCH 17/31] [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --- mne/channels/channels.py | 50 ++++++++++++++++++---------------------- 1 file changed, 23 insertions(+), 27 deletions(-) diff --git a/mne/channels/channels.py b/mne/channels/channels.py index 79b2c52dd4b..9d03ddbafa0 100644 --- a/mne/channels/channels.py +++ b/mne/channels/channels.py @@ -1040,7 +1040,6 @@ def interpolate_to( is_eeg_interpolation = isinstance(sensors, DigMontage) if is_eeg_interpolation: - # Check that the method option is valid. _validate_type(sensors, DigMontage, "sensors") # TODO: Handle the error: sensors must be an instance of DigMontage, got .CustomMontage'> instead. @@ -1086,7 +1085,9 @@ def interpolate_to( eeg_names_orig = [orig_names[i] for i in picks_remove_eeg] # Identify non-EEG channels in original order - non_eeg_names_ordered = [ch for ch in orig_names if ch not in eeg_names_orig] + non_eeg_names_ordered = [ + ch for ch in orig_names if ch not in eeg_names_orig + ] # Create destination info for new EEG channels sfreq = self.info["sfreq"] @@ -1096,7 +1097,9 @@ def interpolate_to( ch_types=["eeg"] * len(target_ch_names), ) info_interp.set_montage(sensors) - info_interp["bads"] = [ch for ch in self.info["bads"] if ch in target_ch_names] + info_interp["bads"] = [ + ch for ch in self.info["bads"] if ch in target_ch_names + ] # Do not assign "projs" directly. # Compute the interpolation mapping @@ -1109,7 +1112,9 @@ def _check_pos_sphere(pos): d = np.linalg.norm(pos, axis=-1) d_norm = np.mean(d / np.mean(d)) if np.abs(1.0 - d_norm) > 0.1: - warn("Your spherical fit is poor; interpolation may be inaccurate.") + warn( + "Your spherical fit is poor; interpolation may be inaccurate." + ) _check_pos_sphere(pos_from) _check_pos_sphere(pos_to) @@ -1121,9 +1126,9 @@ def _check_pos_sphere(pos): # If the original info has an average EEG reference projector but # the destination info does not, # update info_interp via a temporary RawArray. - if _has_eeg_average_ref_proj(self.info) and not _has_eeg_average_ref_proj( - info_interp - ): + if _has_eeg_average_ref_proj( + self.info + ) and not _has_eeg_average_ref_proj(info_interp): # Create dummy data: shape (n_channels, 1) temp_data = np.zeros((len(info_interp["ch_names"]), 1)) temp_raw = RawArray(temp_data, info_interp, first_samp=0) @@ -1146,7 +1151,9 @@ def _check_pos_sphere(pos): # We should consider updating the existing instance in the future # by 1) drop channels, 2) add channels, 3) re-order channels. if isinstance(self, BaseRaw): - inst_interp = RawArray(data_interp, info_interp, first_samp=self.first_samp) + inst_interp = RawArray( + data_interp, info_interp, first_samp=self.first_samp + ) elif isinstance(self, BaseEpochs): inst_interp = EpochsArray(data_interp, info_interp) else: @@ -1177,19 +1184,15 @@ def _check_pos_sphere(pos): return inst_out elif is_meg_interpolation: - # MEG interpolation to canonical sensor configuration + # MEG interpolation to canonical sensor configuration _check_option("sensors", sensors, ["neuromag", "ctf151", "ctf275"]) _check_option("method", method, ["MNE"]) _check_option("mode", mode, ["accurate", "fast"]) # Get MEG channels from source - picks_meg = pick_types( - self.info, meg=True, ref_meg=False, exclude="bads" - ) + picks_meg = pick_types(self.info, meg=True, ref_meg=False, exclude="bads") if len(picks_meg) == 0: - raise ValueError( - "No good MEG channels available for interpolation." - ) + raise ValueError("No good MEG channels available for interpolation.") # Load target sensor configuration info_to = read_meg_montage(sensors) @@ -1213,9 +1216,7 @@ def _check_pos_sphere(pos): # Create new instance with interpolated MEG data if isinstance(self, BaseRaw): - inst_out = RawArray( - data_interp, info_to, first_samp=self.first_samp - ) + inst_out = RawArray(data_interp, info_to, first_samp=self.first_samp) elif isinstance(self, BaseEpochs): inst_out = EpochsArray(data_interp, info_to) else: @@ -1223,20 +1224,15 @@ def _check_pos_sphere(pos): inst_out = EvokedArray(data_interp, info_to) # Add non-MEG channels if they exist - non_meg_picks = pick_types( - self.info, meg=False, exclude=[], ref_meg=False - ) + non_meg_picks = pick_types(self.info, meg=False, exclude=[], ref_meg=False) if len(non_meg_picks) > 0: - non_meg_names = [ - self.info["ch_names"][i] for i in non_meg_picks - ] + non_meg_names = [self.info["ch_names"][i] for i in non_meg_picks] inst_non_meg = self.copy().pick(non_meg_names).load_data() - inst_out = inst_out.add_channels( - [inst_non_meg], force_update_info=True - ) + inst_out = inst_out.add_channels([inst_non_meg], force_update_info=True) return inst_out + @verbose def rename_channels(info, mapping, allow_duplicates=False, *, verbose=None): """Rename channels. From 1b3855db10edd014f13da8209d9d61a51ccc31f4 Mon Sep 17 00:00:00 2001 From: Konstantinos Tsilimparis Date: Sun, 12 Oct 2025 16:58:45 +0200 Subject: [PATCH 18/31] ENH: Add MEG interpolation example --- examples/preprocessing/interpolate_to.py | 64 +++++++++++++++++++++++- mne/channels/channels.py | 5 +- 2 files changed, 65 insertions(+), 4 deletions(-) diff --git a/examples/preprocessing/interpolate_to.py b/examples/preprocessing/interpolate_to.py index aa39e16b422..b6b9802d782 100644 --- a/examples/preprocessing/interpolate_to.py +++ b/examples/preprocessing/interpolate_to.py @@ -16,7 +16,7 @@ interpolated onto the positions defined by the "biosemi16" montage. """ -# Authors: Antoine Collas +# Authors: Antoine Collas , Konstantinos Tsilimparis # License: BSD-3-Clause # Copyright the MNE-Python contributors. @@ -33,7 +33,7 @@ # Load the MEG data data_path = sample.data_path() fif_file_path = data_path / "MEG" / "sample" / "sample_audvis_raw.fif" -raw_meg = mne.io.read_raw_fif(fif_file_path) +raw_meg = mne.io.read_raw_fif(fif_file_path, preload=True) raw_meg.pick("meg") @@ -93,6 +93,66 @@ ) axs[2].set_title("Interpolated to Standard 1020 Montage using MNE interpolation") +# %% +# Part 2: MEG System Transformation +# ================================== +# Now we demonstrate transforming MEG data from Neuromag (axial gradiometers +# and magnetometers) to CTF (radial gradiometers) sensor configuration. + +# Load the full evoked data with MEG channels +evoked_meg = mne.read_evokeds( + eeg_file_path, condition="Left Auditory", baseline=(None, 0) +) +evoked_meg.pick("meg") + +print(f"Original Neuromag system:") +print(f" Number of magnetometers: {len(mne.pick_types(evoked_meg.info, meg='mag'))}") +print(f" Number of gradiometers: {len(mne.pick_types(evoked_meg.info, meg='grad'))}") + +# %% +# Transform to CTF sensor configuration +# ====================================== + +# Interpolate Neuromag to CTF +evoked_ctf = evoked_meg.copy().interpolate_to("ctf275", mode="accurate") + +print("\nTransformed to CTF system:") +print( + f" Number of MEG channels: " + f"{len(mne.pick_types(evoked_ctf.info, meg=True))}" +) +print(f" Bad channels in original: {evoked_meg.info['bads']}") + +# %% +# Compare evoked responses: Original Neuromag vs Transformed CTF +# The data should be similar but projected onto different sensor arrays + +# Set consistent y-limits for comparison +ylim_meg = dict(grad=[-300, 300], mag=[-600, 600], meg=[-300, 300]) + +fig, axes = plt.subplots(3, 1, figsize=(10, 8), layout='constrained') + +# Plot original Neuromag gradiometers +evoked_meg.copy().pick('grad').plot( + axes=axes[0], show=False, spatial_colors=True, ylim=ylim_meg, time_unit='s' +) +axes[0].set_title('Original Neuromag Gradiometers', fontsize=14) + + +# Plot original Neuromag magnetometers +evoked_meg.copy().pick('mag').plot( + axes=axes[1], show=False, spatial_colors=True, ylim=ylim_meg, time_unit='s' +) +axes[1].set_title('Original Neuromag Magnetometers', fontsize=14) + +# Plot transformed CTF gradiometers +evoked_ctf.plot( + axes=axes[2], show=False, spatial_colors=True, ylim=ylim_meg, time_unit='s' +) +axes[2].set_title('Transformed to CTF275 Axial Gradiometers', fontsize=14) + +plt.show() + # %% # References # ---------- diff --git a/mne/channels/channels.py b/mne/channels/channels.py index 79b2c52dd4b..53dbbec70a3 100644 --- a/mne/channels/channels.py +++ b/mne/channels/channels.py @@ -1100,7 +1100,7 @@ def interpolate_to( # Do not assign "projs" directly. # Compute the interpolation mapping - if method == "spline": + if method["eeg"] == "spline": origin_val = _check_origin(origin, self.info) pos_from = self.info._get_channel_positions(picks_good_eeg) - origin_val pos_to = np.stack(list(ch_pos.values()), axis=0) @@ -1116,7 +1116,7 @@ def _check_pos_sphere(pos): mapping = _make_interpolation_matrix(pos_from, pos_to, alpha=reg) else: - assert method == "MNE" + assert method["eeg"] == "MNE" info_eeg = pick_info(self.info, picks_good_eeg) # If the original info has an average EEG reference projector but # the destination info does not, @@ -1177,6 +1177,7 @@ def _check_pos_sphere(pos): return inst_out elif is_meg_interpolation: + assert method["meg"] == "MNE" # MEG interpolation to canonical sensor configuration _check_option("sensors", sensors, ["neuromag", "ctf151", "ctf275"]) _check_option("method", method, ["MNE"]) From e2dd6cd2aaa8349c7cc6618c00c04a17a57d61e3 Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Sun, 12 Oct 2025 14:59:49 +0000 Subject: [PATCH 19/31] [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --- examples/preprocessing/interpolate_to.py | 25 +++++++++++------------- 1 file changed, 11 insertions(+), 14 deletions(-) diff --git a/examples/preprocessing/interpolate_to.py b/examples/preprocessing/interpolate_to.py index b6b9802d782..c57c6eca67b 100644 --- a/examples/preprocessing/interpolate_to.py +++ b/examples/preprocessing/interpolate_to.py @@ -105,7 +105,7 @@ ) evoked_meg.pick("meg") -print(f"Original Neuromag system:") +print("Original Neuromag system:") print(f" Number of magnetometers: {len(mne.pick_types(evoked_meg.info, meg='mag'))}") print(f" Number of gradiometers: {len(mne.pick_types(evoked_meg.info, meg='grad'))}") @@ -117,10 +117,7 @@ evoked_ctf = evoked_meg.copy().interpolate_to("ctf275", mode="accurate") print("\nTransformed to CTF system:") -print( - f" Number of MEG channels: " - f"{len(mne.pick_types(evoked_ctf.info, meg=True))}" -) +print(f" Number of MEG channels: {len(mne.pick_types(evoked_ctf.info, meg=True))}") print(f" Bad channels in original: {evoked_meg.info['bads']}") # %% @@ -130,26 +127,26 @@ # Set consistent y-limits for comparison ylim_meg = dict(grad=[-300, 300], mag=[-600, 600], meg=[-300, 300]) -fig, axes = plt.subplots(3, 1, figsize=(10, 8), layout='constrained') +fig, axes = plt.subplots(3, 1, figsize=(10, 8), layout="constrained") # Plot original Neuromag gradiometers -evoked_meg.copy().pick('grad').plot( - axes=axes[0], show=False, spatial_colors=True, ylim=ylim_meg, time_unit='s' +evoked_meg.copy().pick("grad").plot( + axes=axes[0], show=False, spatial_colors=True, ylim=ylim_meg, time_unit="s" ) -axes[0].set_title('Original Neuromag Gradiometers', fontsize=14) +axes[0].set_title("Original Neuromag Gradiometers", fontsize=14) # Plot original Neuromag magnetometers -evoked_meg.copy().pick('mag').plot( - axes=axes[1], show=False, spatial_colors=True, ylim=ylim_meg, time_unit='s' +evoked_meg.copy().pick("mag").plot( + axes=axes[1], show=False, spatial_colors=True, ylim=ylim_meg, time_unit="s" ) -axes[1].set_title('Original Neuromag Magnetometers', fontsize=14) +axes[1].set_title("Original Neuromag Magnetometers", fontsize=14) # Plot transformed CTF gradiometers evoked_ctf.plot( - axes=axes[2], show=False, spatial_colors=True, ylim=ylim_meg, time_unit='s' + axes=axes[2], show=False, spatial_colors=True, ylim=ylim_meg, time_unit="s" ) -axes[2].set_title('Transformed to CTF275 Axial Gradiometers', fontsize=14) +axes[2].set_title("Transformed to CTF275 Axial Gradiometers", fontsize=14) plt.show() From 6d0fc69b10750be3a00e225eaa4c860c1bf9cfe7 Mon Sep 17 00:00:00 2001 From: Konstantinos Tsilimparis Date: Sun, 12 Oct 2025 17:09:14 +0200 Subject: [PATCH 20/31] COS: Update example script docstrings to include MEG --- examples/preprocessing/interpolate_to.py | 20 ++++++++++++++------ mne/channels/channels.py | 1 - 2 files changed, 14 insertions(+), 7 deletions(-) diff --git a/examples/preprocessing/interpolate_to.py b/examples/preprocessing/interpolate_to.py index b6b9802d782..5eadd02c16b 100644 --- a/examples/preprocessing/interpolate_to.py +++ b/examples/preprocessing/interpolate_to.py @@ -2,18 +2,26 @@ .. _ex-interpolate-to-any-montage: ====================================================== -Interpolate EEG data to any montage +Interpolate MEG or EEG data to any montage ====================================================== -This example demonstrates how to interpolate EEG channels to match a given montage. -This can be useful for standardizing +This example demonstrates both EEG montage interpolation and MEG system +transformation. + +For EEG, this can be useful for standardizing EEG channel layouts across different datasets (see :footcite:`MellotEtAl2024`). - Using the field interpolation for EEG data. - Using the target montage "biosemi16". +- Using the MNE interpolation for MEG data to transform from Neuromag + (planar gradiometers and magnetometers) to CTF (axial gradiometers). + -In this example, the data from the original EEG channels will be +In the first example, the data from the original EEG channels will be interpolated onto the positions defined by the "biosemi16" montage. + +In the second example, we will interpolate MEG data from a 306-sensor Neuromag +to 275-sensor CTF system. """ # Authors: Antoine Collas , Konstantinos Tsilimparis @@ -96,7 +104,7 @@ # %% # Part 2: MEG System Transformation # ================================== -# Now we demonstrate transforming MEG data from Neuromag (axial gradiometers +# Now we demonstrate transforming MEG data from Neuromag (planar gradiometers # and magnetometers) to CTF (radial gradiometers) sensor configuration. # Load the full evoked data with MEG channels @@ -136,7 +144,7 @@ evoked_meg.copy().pick('grad').plot( axes=axes[0], show=False, spatial_colors=True, ylim=ylim_meg, time_unit='s' ) -axes[0].set_title('Original Neuromag Gradiometers', fontsize=14) +axes[0].set_title('Original Neuromag Planar Gradiometers', fontsize=14) # Plot original Neuromag magnetometers diff --git a/mne/channels/channels.py b/mne/channels/channels.py index 3bc85f6b567..eeedb2dcdb8 100644 --- a/mne/channels/channels.py +++ b/mne/channels/channels.py @@ -1184,7 +1184,6 @@ def _check_pos_sphere(pos): return inst_out elif is_meg_interpolation: - assert method["meg"] == "MNE" # MEG interpolation to canonical sensor configuration _check_option("sensors", sensors, ["neuromag", "ctf151", "ctf275"]) _check_option("method", method, ["MNE"]) From 221c357bd2245d9178f600765354c769eeedb1d0 Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Sun, 12 Oct 2025 15:10:47 +0000 Subject: [PATCH 21/31] [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --- examples/preprocessing/interpolate_to.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/examples/preprocessing/interpolate_to.py b/examples/preprocessing/interpolate_to.py index 5aaecc80d93..765e1219365 100644 --- a/examples/preprocessing/interpolate_to.py +++ b/examples/preprocessing/interpolate_to.py @@ -141,7 +141,7 @@ evoked_meg.copy().pick("grad").plot( axes=axes[0], show=False, spatial_colors=True, ylim=ylim_meg, time_unit="s" ) -axes[0].set_title('Original Neuromag Planar Gradiometers', fontsize=14) +axes[0].set_title("Original Neuromag Planar Gradiometers", fontsize=14) # Plot original Neuromag magnetometers From 5eb71b31890c4255eb63483e6e54da7722995b00 Mon Sep 17 00:00:00 2001 From: Eric Larson Date: Wed, 15 Oct 2025 12:57:52 -0400 Subject: [PATCH 22/31] FIX: Sty --- examples/preprocessing/interpolate_to.py | 3 ++- mne/channels/_standard_montage_utils.py | 4 ++-- mne/channels/channels.py | 10 +++++++--- 3 files changed, 11 insertions(+), 6 deletions(-) diff --git a/examples/preprocessing/interpolate_to.py b/examples/preprocessing/interpolate_to.py index 765e1219365..48d7da97766 100644 --- a/examples/preprocessing/interpolate_to.py +++ b/examples/preprocessing/interpolate_to.py @@ -24,7 +24,8 @@ to 275-sensor CTF system. """ -# Authors: Antoine Collas , Konstantinos Tsilimparis +# Authors: Antoine Collas +# Konstantinos Tsilimparis # License: BSD-3-Clause # Copyright the MNE-Python contributors. diff --git a/mne/channels/_standard_montage_utils.py b/mne/channels/_standard_montage_utils.py index acca4a36736..f5f21312b18 100644 --- a/mne/channels/_standard_montage_utils.py +++ b/mne/channels/_standard_montage_utils.py @@ -130,7 +130,7 @@ class CustomMontage: # Read the file with open(fname) as f: # Skip header line - header = f.readline().strip().split() + _header = f.readline().strip().split() ch_names = [] ch_types = [] @@ -155,7 +155,7 @@ class CustomMontage: # For custom montages without fiducials, this parameter must be set # to ``'head'``. -> # "kind": FIFF.FIFFV_POINT_EEG, -> - # dig names each chennel as EEG #1 ... + # dig names each channel as EEG #1 ... # These aren't standard DigMontage attributes but can be useful montage.ch_types = ch_types diff --git a/mne/channels/channels.py b/mne/channels/channels.py index 3e8c101761e..43d75dd9022 100644 --- a/mne/channels/channels.py +++ b/mne/channels/channels.py @@ -1048,7 +1048,9 @@ def interpolate_to( if is_eeg_interpolation: # Check that the method option is valid. _validate_type(sensors, DigMontage, "sensors") - # TODO: Handle the error: sensors must be an instance of DigMontage, got .CustomMontage'> instead. + # TODO: Handle the error: sensors must be an instance of DigMontage, got + # .CustomMontage'> + # instead. method = _handle_default("interpolation_method", method) @@ -1071,7 +1073,8 @@ def interpolate_to( logger.info("Setting channel interpolation method to %s.", method) # Get target positions from the montage - # TODO: handle the error: AttributeError: 'CustomMontage' object has no attribute 'get_positions' + # TODO: handle the error: AttributeError: 'CustomMontage' object has no + # attribute 'get_positions' ch_pos = sensors.get_positions().get("ch_pos", {}) target_ch_names = list(ch_pos.keys()) if not target_ch_names: @@ -1119,7 +1122,8 @@ def _check_pos_sphere(pos): d_norm = np.mean(d / np.mean(d)) if np.abs(1.0 - d_norm) > 0.1: warn( - "Your spherical fit is poor; interpolation may be inaccurate." + "Your spherical fit is poor; interpolation may be " + "inaccurate." ) _check_pos_sphere(pos_from) From 0dc97c265733d500d308e6dce35357603f81be89 Mon Sep 17 00:00:00 2001 From: Konstantinos Tsilimparis <105793148+contsili@users.noreply.github.com> Date: Thu, 16 Oct 2025 00:09:04 +0200 Subject: [PATCH 23/31] fix - do not handle ctf and neuromag as custom montages --- mne/channels/_standard_montage_utils.py | 469 ------------------------ 1 file changed, 469 deletions(-) delete mode 100644 mne/channels/_standard_montage_utils.py diff --git a/mne/channels/_standard_montage_utils.py b/mne/channels/_standard_montage_utils.py deleted file mode 100644 index f5f21312b18..00000000000 --- a/mne/channels/_standard_montage_utils.py +++ /dev/null @@ -1,469 +0,0 @@ -# Authors: The MNE-Python contributors. -# License: BSD-3-Clause -# Copyright the MNE-Python contributors. - -import csv -import os.path as op -from collections import OrderedDict -from functools import partial - -import numpy as np - -from .._freesurfer import get_mni_fiducials -from ..transforms import _sph_to_cart -from ..utils import _pl, _soft_import, warn -from . import __file__ as _CHANNELS_INIT_FILE -from .montage import make_dig_montage - -MONTAGE_PATH = op.join(op.dirname(_CHANNELS_INIT_FILE), "data", "montages") - -_str = "U100" - - -# In standard_1020, T9=LPA, T10=RPA, Nasion is the same as Iz with a -# sign-flipped Y value - - -def _egi_256(head_size): - fname = op.join(MONTAGE_PATH, "EGI_256.csd") - montage = _read_csd(fname, head_size) - ch_pos = montage._get_ch_pos() - - # For this cap, the Nasion is the frontmost electrode, - # LPA/RPA we approximate by putting 75% of the way (toward the front) - # between the two electrodes that are halfway down the ear holes - nasion = ch_pos["E31"] - lpa = 0.75 * ch_pos["E67"] + 0.25 * ch_pos["E94"] - rpa = 0.75 * ch_pos["E219"] + 0.25 * ch_pos["E190"] - - fids_montage = make_dig_montage( - coord_frame="unknown", - nasion=nasion, - lpa=lpa, - rpa=rpa, - ) - - montage += fids_montage # add fiducials to montage - - return montage - - -def _easycap(basename, head_size): - fname = op.join(MONTAGE_PATH, basename) - montage = _read_theta_phi_in_degrees(fname, head_size, add_fiducials=True) - return montage - - -def _hydrocel(basename, head_size): - fname = op.join(MONTAGE_PATH, basename) - return _read_sfp(fname, head_size) - - -def _str_names(ch_names): - return [str(ch_name) for ch_name in ch_names] - - -def _safe_np_loadtxt(fname, **kwargs): - out = np.genfromtxt(fname, **kwargs) - ch_names = _str_names(out["f0"]) - others = tuple(out[f"f{ii}"] for ii in range(1, len(out.dtype.fields))) - return (ch_names,) + others - - -def _biosemi(basename, head_size): - fname = op.join(MONTAGE_PATH, basename) - fid_names = ("Nz", "LPA", "RPA") - return _read_theta_phi_in_degrees(fname, head_size, fid_names) - - -def _mgh_or_standard(basename, head_size, coord_frame="unknown"): - fid_names = ("Nz", "LPA", "RPA") - fname = op.join(MONTAGE_PATH, basename) - - ch_names_, pos = [], [] - with open(fname) as fid: - # Ignore units as we will scale later using the norms anyway - for line in fid: - if "Positions\n" in line: - break - pos = [] - for line in fid: - if "Labels\n" in line: - break - pos.append(list(map(float, line.split()))) - for line in fid: - if not line or not set(line) - {" "}: - break - ch_names_.append(line.strip(" ").strip("\n")) - - pos = np.array(pos) / 1000.0 - ch_pos = _check_dupes_odict(ch_names_, pos) - nasion, lpa, rpa = (ch_pos.pop(n) for n in fid_names) - if head_size is None: - scale = 1.0 - else: - scale = head_size / np.median(np.linalg.norm(pos, axis=1)) - for value in ch_pos.values(): - value *= scale - # if we are in MRI/MNI coordinates, we need to replace nasion, LPA, and RPA - # with those of fsaverage for ``trans='fsaverage'`` to work - if coord_frame == "mri": - lpa, nasion, rpa = (x["r"].copy() for x in get_mni_fiducials("fsaverage")) - nasion *= scale - lpa *= scale - rpa *= scale - - return make_dig_montage( - ch_pos=ch_pos, coord_frame=coord_frame, nasion=nasion, lpa=lpa, rpa=rpa - ) - - -def _meg(basename, head_size): - fname = op.join(MONTAGE_PATH, basename) - - # Create a simple class instance instead of a list - class CustomMontage: - pass - - montage = CustomMontage() - - # Read the file - with open(fname) as f: - # Skip header line - _header = f.readline().strip().split() - - ch_names = [] - ch_types = [] - pos = [] - ori = [] - - for line in f: - parts = line.strip().split() - ch_names.append(parts[0]) - ch_types.append(parts[1]) - pos.append([float(parts[2]), float(parts[3]), float(parts[4])]) - ori.append([float(parts[5]), float(parts[6]), float(parts[7])]) - - pos = np.array(pos) - - montage.ch_names = ch_names - - # # Create a dictionary mapping channel names to positions - # montage.ch_pos = dict(zip(ch_names, pos)) - montage.ch_pos = pos - # # TODO - make_dig_montage(): - # For custom montages without fiducials, this parameter must be set - # to ``'head'``. -> - # "kind": FIFF.FIFFV_POINT_EEG, -> - # dig names each channel as EEG #1 ... - - # These aren't standard DigMontage attributes but can be useful - montage.ch_types = ch_types - montage.ori = ori - - return montage - - -standard_montage_look_up_table = { - "EGI_256": _egi_256, - "easycap-M1": partial(_easycap, basename="easycap-M1.txt"), - "easycap-M10": partial(_easycap, basename="easycap-M10.txt"), - "easycap-M43": partial(_easycap, basename="easycap-M43.txt"), - "GSN-HydroCel-128": partial(_hydrocel, basename="GSN-HydroCel-128.sfp"), - "GSN-HydroCel-129": partial(_hydrocel, basename="GSN-HydroCel-129.sfp"), - "GSN-HydroCel-256": partial(_hydrocel, basename="GSN-HydroCel-256.sfp"), - "GSN-HydroCel-257": partial(_hydrocel, basename="GSN-HydroCel-257.sfp"), - "GSN-HydroCel-32": partial(_hydrocel, basename="GSN-HydroCel-32.sfp"), - "GSN-HydroCel-64_1.0": partial(_hydrocel, basename="GSN-HydroCel-64_1.0.sfp"), - "GSN-HydroCel-65_1.0": partial(_hydrocel, basename="GSN-HydroCel-65_1.0.sfp"), - "biosemi128": partial(_biosemi, basename="biosemi128.txt"), - "biosemi16": partial(_biosemi, basename="biosemi16.txt"), - "biosemi160": partial(_biosemi, basename="biosemi160.txt"), - "biosemi256": partial(_biosemi, basename="biosemi256.txt"), - "biosemi32": partial(_biosemi, basename="biosemi32.txt"), - "biosemi64": partial(_biosemi, basename="biosemi64.txt"), - "mgh60": partial(_mgh_or_standard, basename="mgh60.elc", coord_frame="mri"), - "mgh70": partial(_mgh_or_standard, basename="mgh70.elc", coord_frame="mri"), - "standard_1005": partial( - _mgh_or_standard, basename="standard_1005.elc", coord_frame="mri" - ), - "standard_1020": partial( - _mgh_or_standard, basename="standard_1020.elc", coord_frame="mri" - ), - "standard_alphabetic": partial( - _mgh_or_standard, basename="standard_alphabetic.elc", coord_frame="mri" - ), - "standard_postfixed": partial( - _mgh_or_standard, basename="standard_postfixed.elc", coord_frame="mri" - ), - "standard_prefixed": partial( - _mgh_or_standard, basename="standard_prefixed.elc", coord_frame="mri" - ), - "standard_primed": partial( - _mgh_or_standard, basename="standard_primed.elc", coord_frame="mri" - ), - "artinis-octamon": partial( - _mgh_or_standard, coord_frame="mri", basename="artinis-octamon.elc" - ), - "artinis-brite23": partial( - _mgh_or_standard, coord_frame="mri", basename="artinis-brite23.elc" - ), - "brainproducts-RNP-BA-128": partial( - _easycap, basename="brainproducts-RNP-BA-128.txt" - ), - "ctf275": partial(_meg, basename="ctf275.txt"), - "neuromag306": partial(_meg, basename="neuromag306.txt"), -} - - -def _read_sfp(fname, head_size): - """Read .sfp BESA/EGI files.""" - # fname has been already checked - fid_names = ("FidNz", "FidT9", "FidT10") - options = dict(dtype=(_str, "f4", "f4", "f4")) - ch_names, xs, ys, zs = _safe_np_loadtxt(fname, **options) - # deal with "headshape" - mask = np.array([ch_name == "headshape" for ch_name in ch_names], bool) - hsp = np.stack([xs[mask], ys[mask], zs[mask]], axis=-1) - mask = ~mask - pos = np.stack([xs[mask], ys[mask], zs[mask]], axis=-1) - ch_names = [ch_name for ch_name, m in zip(ch_names, mask) if m] - ch_pos = _check_dupes_odict(ch_names, pos) - del xs, ys, zs, ch_names - # no one grants that fid names are there. - nasion, lpa, rpa = (ch_pos.pop(n, None) for n in fid_names) - - if head_size is not None: - scale = head_size / np.median(np.linalg.norm(pos, axis=-1)) - for value in ch_pos.values(): - value *= scale - nasion = nasion * scale if nasion is not None else None - lpa = lpa * scale if lpa is not None else None - rpa = rpa * scale if rpa is not None else None - - return make_dig_montage( - ch_pos=ch_pos, coord_frame="unknown", nasion=nasion, rpa=rpa, lpa=lpa, hsp=hsp - ) - - -def _read_csd(fname, head_size): - # Label, Theta, Phi, Radius, X, Y, Z, off sphere surface - options = dict( - comments="//", dtype=(_str, "f4", "f4", "f4", "f4", "f4", "f4", "f4") - ) - ch_names, _, _, _, xs, ys, zs, _ = _safe_np_loadtxt(fname, **options) - pos = np.stack([xs, ys, zs], axis=-1) - - if head_size is not None: - pos *= head_size / np.median(np.linalg.norm(pos, axis=1)) - - return make_dig_montage(ch_pos=_check_dupes_odict(ch_names, pos)) - - -def _check_dupes_odict(ch_names, pos): - """Warn if there are duplicates, then turn to ordered dict.""" - ch_names = list(ch_names) - dups = OrderedDict((ch_name, ch_names.count(ch_name)) for ch_name in ch_names) - dups = OrderedDict((ch_name, count) for ch_name, count in dups.items() if count > 1) - n = len(dups) - if n: - dups = ", ".join(f"{ch_name} ({count})" for ch_name, count in dups.items()) - warn( - f"Duplicate channel position{_pl(n)} found, the last will be " - f"used for {dups}" - ) - return OrderedDict(zip(ch_names, pos)) - - -def _read_elc(fname, head_size): - """Read .elc files. - - The `.elc` files are so-called "asa electrode files". ASA here stands for - Advances Source Analysis, and is a software package developed and sold by - the ANT Neuro company. They provide a device for sensor digitization, called - 'xensor', which produces the `.elc` files. - - Parameters - ---------- - fname : str - File extension is expected to be '.elc'. - head_size : float | None - The size of the head in [m]. If none, returns the values read from the - file with no modification. - - Returns - ------- - montage : instance of DigMontage - The montage units are [m]. - """ - fid_names = ("Nz", "LPA", "RPA") - - with open(fname) as fid: - # Read units - # _read_elc does require to detect the units. (see _mgh_or_standard) - for line in fid: - if "UnitPosition" in line: - units = line.split()[1] - scale = dict(m=1.0, mm=1e-3)[units] - break - else: - raise RuntimeError(f"Could not detect units in file {fname}") - for line in fid: - if "Positions\n" in line: - break - - # Read positions - new_style = False - pos = [] - for line in fid: - if "Labels\n" in line: - break - if ":" in line: - # Of the 'new' format: `E01 : 5.288 -3.658 119.693` - pos.append(list(map(float, line.split(":")[1].split()))) - new_style = True - else: - # Of the 'old' format: `5.288 -3.658 119.693` - pos.append(list(map(float, line.split()))) - - # Read labels - ch_names_ = [] - for line in fid: - if not line or not set(line) - {" "}: - break - if new_style: - # Not sure how this format would deal with spaces in channel labels, - # but none of my test files had this, so let's wait until it comes up. - parsed = line.strip(" ").strip("\n").split() - else: - parsed = [line.strip(" ").strip("\n")] - ch_names_.extend(parsed) - - pos = np.array(pos) * scale - if head_size is not None: - pos *= head_size / np.median(np.linalg.norm(pos, axis=1)) - - ch_pos = _check_dupes_odict(ch_names_, pos) - nasion, lpa, rpa = (ch_pos.pop(n, None) for n in fid_names) - - return make_dig_montage( - ch_pos=ch_pos, coord_frame="unknown", nasion=nasion, lpa=lpa, rpa=rpa - ) - - -def _read_theta_phi_in_degrees(fname, head_size, fid_names=None, add_fiducials=False): - ch_names, theta, phi = _safe_np_loadtxt( - fname, skip_header=1, dtype=(_str, "i4", "i4") - ) - if add_fiducials: - # Add fiducials based on 10/20 spherical coordinate definitions - # http://chgd.umich.edu/wp-content/uploads/2014/06/ - # 10-20_system_positioning.pdf - # extrapolated from other sensor coordinates in the Easycap layouts - # https://www.easycap.de/wp-content/uploads/2018/02/ - # Easycap-Equidistant-Layouts.pdf - assert fid_names is None - fid_names = ["Nasion", "LPA", "RPA"] - ch_names.extend(fid_names) - theta = np.append(theta, [115, -115, 115]) - phi = np.append(phi, [90, 0, 0]) - - radii = np.full(len(phi), head_size) - pos = _sph_to_cart(np.array([radii, np.deg2rad(phi), np.deg2rad(theta)]).T) - ch_pos = _check_dupes_odict(ch_names, pos) - - nasion, lpa, rpa = None, None, None - if fid_names is not None: - nasion, lpa, rpa = (ch_pos.pop(n, None) for n in fid_names) - - return make_dig_montage( - ch_pos=ch_pos, coord_frame="unknown", nasion=nasion, lpa=lpa, rpa=rpa - ) - - -def _read_elp_besa(fname, head_size): - # This .elp is not the same as polhemus elp. see _read_isotrak_elp_points - dtype = np.dtype("S8, S8, f8, f8, f8") - data = np.loadtxt(fname, dtype=dtype) - - ch_names = data["f1"].astype(str).tolist() - az = data["f2"] - horiz = data["f3"] - radius = np.abs(az / 180.0) - az = np.deg2rad(np.array([h if a >= 0.0 else 180 + h for h, a in zip(horiz, az)])) - pol = radius * np.pi - rad = data["f4"] / 100 - pos = _sph_to_cart(np.array([rad, az, pol]).T) - - if head_size is not None: - pos *= head_size / np.median(np.linalg.norm(pos, axis=1)) - - ch_pos = _check_dupes_odict(ch_names, pos) - - fid_names = ("Nz", "LPA", "RPA") - # No one grants that the fid names actually exist. - nasion, lpa, rpa = (ch_pos.pop(n, None) for n in fid_names) - - return make_dig_montage(ch_pos=ch_pos, nasion=nasion, lpa=lpa, rpa=rpa) - - -def _read_brainvision(fname, head_size): - # 'BrainVision Electrodes File' format - # Based on BrainVision Analyzer coordinate system: Defined between - # standard electrode positions: X-axis from T7 to T8, Y-axis from Oz to - # Fpz, Z-axis orthogonal from XY-plane through Cz, fit to a sphere if - # idealized (when radius=1), specified in millimeters - defusedxml = _soft_import("defusedxml", "reading BrainVision montages") - root = defusedxml.ElementTree.parse(fname).getroot() - ch_names = [s.text for s in root.findall("./Electrode/Name")] - theta = [float(s.text) for s in root.findall("./Electrode/Theta")] - pol = np.deg2rad(np.array(theta)) - phi = [float(s.text) for s in root.findall("./Electrode/Phi")] - az = np.deg2rad(np.array(phi)) - rad = [float(s.text) for s in root.findall("./Electrode/Radius")] - rad = np.array(rad) # specified in mm - pos = _sph_to_cart(np.array([rad, az, pol]).T) - - if head_size is not None: - pos *= head_size / np.median(np.linalg.norm(pos, axis=1)) - - return make_dig_montage(ch_pos=_check_dupes_odict(ch_names, pos)) - - -def _read_xyz(fname): - """Import EEG channel locations from CSV, TSV, or XYZ files. - - CSV and TSV files should have columns 4 columns containing - ch_name, x, y, and z. Each row represents one channel. - XYZ files should have 5 columns containing - count, x, y, z, and ch_name. Each row represents one channel - CSV files should be separated by commas, TSV and XYZ files should be - separated by tabs. - - Parameters - ---------- - fname : str - Name of the file to read channel locations from. - - Returns - ------- - montage : instance of DigMontage - The montage. - """ - ch_names = [] - pos = [] - file_format = op.splitext(fname)[1].lower() - with open(fname) as f: - if file_format != ".xyz": - f.readline() # skip header - delimiter = "," if file_format == ".csv" else "\t" - for row in csv.reader(f, delimiter=delimiter): - if file_format == ".xyz": - _, x, y, z, ch_name, *_ = row - ch_name = ch_name.strip() # deals with variable tab size - else: - ch_name, x, y, z, *_ = row - ch_names.append(ch_name) - pos.append((x, y, z)) - d = _check_dupes_odict(ch_names, np.array(pos, dtype=float)) - return make_dig_montage(ch_pos=d) From c8ea36e5cf02ad71bdcebd1dc01ad152bad6c996 Mon Sep 17 00:00:00 2001 From: Konstantinos Tsilimparis Date: Thu, 16 Oct 2025 00:11:44 +0200 Subject: [PATCH 24/31] Revert "fix - do not handle ctf and neuromag as custom montages" This reverts commit 0dc97c265733d500d308e6dce35357603f81be89. --- mne/channels/_standard_montage_utils.py | 469 ++++++++++++++++++++++++ 1 file changed, 469 insertions(+) create mode 100644 mne/channels/_standard_montage_utils.py diff --git a/mne/channels/_standard_montage_utils.py b/mne/channels/_standard_montage_utils.py new file mode 100644 index 00000000000..f5f21312b18 --- /dev/null +++ b/mne/channels/_standard_montage_utils.py @@ -0,0 +1,469 @@ +# Authors: The MNE-Python contributors. +# License: BSD-3-Clause +# Copyright the MNE-Python contributors. + +import csv +import os.path as op +from collections import OrderedDict +from functools import partial + +import numpy as np + +from .._freesurfer import get_mni_fiducials +from ..transforms import _sph_to_cart +from ..utils import _pl, _soft_import, warn +from . import __file__ as _CHANNELS_INIT_FILE +from .montage import make_dig_montage + +MONTAGE_PATH = op.join(op.dirname(_CHANNELS_INIT_FILE), "data", "montages") + +_str = "U100" + + +# In standard_1020, T9=LPA, T10=RPA, Nasion is the same as Iz with a +# sign-flipped Y value + + +def _egi_256(head_size): + fname = op.join(MONTAGE_PATH, "EGI_256.csd") + montage = _read_csd(fname, head_size) + ch_pos = montage._get_ch_pos() + + # For this cap, the Nasion is the frontmost electrode, + # LPA/RPA we approximate by putting 75% of the way (toward the front) + # between the two electrodes that are halfway down the ear holes + nasion = ch_pos["E31"] + lpa = 0.75 * ch_pos["E67"] + 0.25 * ch_pos["E94"] + rpa = 0.75 * ch_pos["E219"] + 0.25 * ch_pos["E190"] + + fids_montage = make_dig_montage( + coord_frame="unknown", + nasion=nasion, + lpa=lpa, + rpa=rpa, + ) + + montage += fids_montage # add fiducials to montage + + return montage + + +def _easycap(basename, head_size): + fname = op.join(MONTAGE_PATH, basename) + montage = _read_theta_phi_in_degrees(fname, head_size, add_fiducials=True) + return montage + + +def _hydrocel(basename, head_size): + fname = op.join(MONTAGE_PATH, basename) + return _read_sfp(fname, head_size) + + +def _str_names(ch_names): + return [str(ch_name) for ch_name in ch_names] + + +def _safe_np_loadtxt(fname, **kwargs): + out = np.genfromtxt(fname, **kwargs) + ch_names = _str_names(out["f0"]) + others = tuple(out[f"f{ii}"] for ii in range(1, len(out.dtype.fields))) + return (ch_names,) + others + + +def _biosemi(basename, head_size): + fname = op.join(MONTAGE_PATH, basename) + fid_names = ("Nz", "LPA", "RPA") + return _read_theta_phi_in_degrees(fname, head_size, fid_names) + + +def _mgh_or_standard(basename, head_size, coord_frame="unknown"): + fid_names = ("Nz", "LPA", "RPA") + fname = op.join(MONTAGE_PATH, basename) + + ch_names_, pos = [], [] + with open(fname) as fid: + # Ignore units as we will scale later using the norms anyway + for line in fid: + if "Positions\n" in line: + break + pos = [] + for line in fid: + if "Labels\n" in line: + break + pos.append(list(map(float, line.split()))) + for line in fid: + if not line or not set(line) - {" "}: + break + ch_names_.append(line.strip(" ").strip("\n")) + + pos = np.array(pos) / 1000.0 + ch_pos = _check_dupes_odict(ch_names_, pos) + nasion, lpa, rpa = (ch_pos.pop(n) for n in fid_names) + if head_size is None: + scale = 1.0 + else: + scale = head_size / np.median(np.linalg.norm(pos, axis=1)) + for value in ch_pos.values(): + value *= scale + # if we are in MRI/MNI coordinates, we need to replace nasion, LPA, and RPA + # with those of fsaverage for ``trans='fsaverage'`` to work + if coord_frame == "mri": + lpa, nasion, rpa = (x["r"].copy() for x in get_mni_fiducials("fsaverage")) + nasion *= scale + lpa *= scale + rpa *= scale + + return make_dig_montage( + ch_pos=ch_pos, coord_frame=coord_frame, nasion=nasion, lpa=lpa, rpa=rpa + ) + + +def _meg(basename, head_size): + fname = op.join(MONTAGE_PATH, basename) + + # Create a simple class instance instead of a list + class CustomMontage: + pass + + montage = CustomMontage() + + # Read the file + with open(fname) as f: + # Skip header line + _header = f.readline().strip().split() + + ch_names = [] + ch_types = [] + pos = [] + ori = [] + + for line in f: + parts = line.strip().split() + ch_names.append(parts[0]) + ch_types.append(parts[1]) + pos.append([float(parts[2]), float(parts[3]), float(parts[4])]) + ori.append([float(parts[5]), float(parts[6]), float(parts[7])]) + + pos = np.array(pos) + + montage.ch_names = ch_names + + # # Create a dictionary mapping channel names to positions + # montage.ch_pos = dict(zip(ch_names, pos)) + montage.ch_pos = pos + # # TODO - make_dig_montage(): + # For custom montages without fiducials, this parameter must be set + # to ``'head'``. -> + # "kind": FIFF.FIFFV_POINT_EEG, -> + # dig names each channel as EEG #1 ... + + # These aren't standard DigMontage attributes but can be useful + montage.ch_types = ch_types + montage.ori = ori + + return montage + + +standard_montage_look_up_table = { + "EGI_256": _egi_256, + "easycap-M1": partial(_easycap, basename="easycap-M1.txt"), + "easycap-M10": partial(_easycap, basename="easycap-M10.txt"), + "easycap-M43": partial(_easycap, basename="easycap-M43.txt"), + "GSN-HydroCel-128": partial(_hydrocel, basename="GSN-HydroCel-128.sfp"), + "GSN-HydroCel-129": partial(_hydrocel, basename="GSN-HydroCel-129.sfp"), + "GSN-HydroCel-256": partial(_hydrocel, basename="GSN-HydroCel-256.sfp"), + "GSN-HydroCel-257": partial(_hydrocel, basename="GSN-HydroCel-257.sfp"), + "GSN-HydroCel-32": partial(_hydrocel, basename="GSN-HydroCel-32.sfp"), + "GSN-HydroCel-64_1.0": partial(_hydrocel, basename="GSN-HydroCel-64_1.0.sfp"), + "GSN-HydroCel-65_1.0": partial(_hydrocel, basename="GSN-HydroCel-65_1.0.sfp"), + "biosemi128": partial(_biosemi, basename="biosemi128.txt"), + "biosemi16": partial(_biosemi, basename="biosemi16.txt"), + "biosemi160": partial(_biosemi, basename="biosemi160.txt"), + "biosemi256": partial(_biosemi, basename="biosemi256.txt"), + "biosemi32": partial(_biosemi, basename="biosemi32.txt"), + "biosemi64": partial(_biosemi, basename="biosemi64.txt"), + "mgh60": partial(_mgh_or_standard, basename="mgh60.elc", coord_frame="mri"), + "mgh70": partial(_mgh_or_standard, basename="mgh70.elc", coord_frame="mri"), + "standard_1005": partial( + _mgh_or_standard, basename="standard_1005.elc", coord_frame="mri" + ), + "standard_1020": partial( + _mgh_or_standard, basename="standard_1020.elc", coord_frame="mri" + ), + "standard_alphabetic": partial( + _mgh_or_standard, basename="standard_alphabetic.elc", coord_frame="mri" + ), + "standard_postfixed": partial( + _mgh_or_standard, basename="standard_postfixed.elc", coord_frame="mri" + ), + "standard_prefixed": partial( + _mgh_or_standard, basename="standard_prefixed.elc", coord_frame="mri" + ), + "standard_primed": partial( + _mgh_or_standard, basename="standard_primed.elc", coord_frame="mri" + ), + "artinis-octamon": partial( + _mgh_or_standard, coord_frame="mri", basename="artinis-octamon.elc" + ), + "artinis-brite23": partial( + _mgh_or_standard, coord_frame="mri", basename="artinis-brite23.elc" + ), + "brainproducts-RNP-BA-128": partial( + _easycap, basename="brainproducts-RNP-BA-128.txt" + ), + "ctf275": partial(_meg, basename="ctf275.txt"), + "neuromag306": partial(_meg, basename="neuromag306.txt"), +} + + +def _read_sfp(fname, head_size): + """Read .sfp BESA/EGI files.""" + # fname has been already checked + fid_names = ("FidNz", "FidT9", "FidT10") + options = dict(dtype=(_str, "f4", "f4", "f4")) + ch_names, xs, ys, zs = _safe_np_loadtxt(fname, **options) + # deal with "headshape" + mask = np.array([ch_name == "headshape" for ch_name in ch_names], bool) + hsp = np.stack([xs[mask], ys[mask], zs[mask]], axis=-1) + mask = ~mask + pos = np.stack([xs[mask], ys[mask], zs[mask]], axis=-1) + ch_names = [ch_name for ch_name, m in zip(ch_names, mask) if m] + ch_pos = _check_dupes_odict(ch_names, pos) + del xs, ys, zs, ch_names + # no one grants that fid names are there. + nasion, lpa, rpa = (ch_pos.pop(n, None) for n in fid_names) + + if head_size is not None: + scale = head_size / np.median(np.linalg.norm(pos, axis=-1)) + for value in ch_pos.values(): + value *= scale + nasion = nasion * scale if nasion is not None else None + lpa = lpa * scale if lpa is not None else None + rpa = rpa * scale if rpa is not None else None + + return make_dig_montage( + ch_pos=ch_pos, coord_frame="unknown", nasion=nasion, rpa=rpa, lpa=lpa, hsp=hsp + ) + + +def _read_csd(fname, head_size): + # Label, Theta, Phi, Radius, X, Y, Z, off sphere surface + options = dict( + comments="//", dtype=(_str, "f4", "f4", "f4", "f4", "f4", "f4", "f4") + ) + ch_names, _, _, _, xs, ys, zs, _ = _safe_np_loadtxt(fname, **options) + pos = np.stack([xs, ys, zs], axis=-1) + + if head_size is not None: + pos *= head_size / np.median(np.linalg.norm(pos, axis=1)) + + return make_dig_montage(ch_pos=_check_dupes_odict(ch_names, pos)) + + +def _check_dupes_odict(ch_names, pos): + """Warn if there are duplicates, then turn to ordered dict.""" + ch_names = list(ch_names) + dups = OrderedDict((ch_name, ch_names.count(ch_name)) for ch_name in ch_names) + dups = OrderedDict((ch_name, count) for ch_name, count in dups.items() if count > 1) + n = len(dups) + if n: + dups = ", ".join(f"{ch_name} ({count})" for ch_name, count in dups.items()) + warn( + f"Duplicate channel position{_pl(n)} found, the last will be " + f"used for {dups}" + ) + return OrderedDict(zip(ch_names, pos)) + + +def _read_elc(fname, head_size): + """Read .elc files. + + The `.elc` files are so-called "asa electrode files". ASA here stands for + Advances Source Analysis, and is a software package developed and sold by + the ANT Neuro company. They provide a device for sensor digitization, called + 'xensor', which produces the `.elc` files. + + Parameters + ---------- + fname : str + File extension is expected to be '.elc'. + head_size : float | None + The size of the head in [m]. If none, returns the values read from the + file with no modification. + + Returns + ------- + montage : instance of DigMontage + The montage units are [m]. + """ + fid_names = ("Nz", "LPA", "RPA") + + with open(fname) as fid: + # Read units + # _read_elc does require to detect the units. (see _mgh_or_standard) + for line in fid: + if "UnitPosition" in line: + units = line.split()[1] + scale = dict(m=1.0, mm=1e-3)[units] + break + else: + raise RuntimeError(f"Could not detect units in file {fname}") + for line in fid: + if "Positions\n" in line: + break + + # Read positions + new_style = False + pos = [] + for line in fid: + if "Labels\n" in line: + break + if ":" in line: + # Of the 'new' format: `E01 : 5.288 -3.658 119.693` + pos.append(list(map(float, line.split(":")[1].split()))) + new_style = True + else: + # Of the 'old' format: `5.288 -3.658 119.693` + pos.append(list(map(float, line.split()))) + + # Read labels + ch_names_ = [] + for line in fid: + if not line or not set(line) - {" "}: + break + if new_style: + # Not sure how this format would deal with spaces in channel labels, + # but none of my test files had this, so let's wait until it comes up. + parsed = line.strip(" ").strip("\n").split() + else: + parsed = [line.strip(" ").strip("\n")] + ch_names_.extend(parsed) + + pos = np.array(pos) * scale + if head_size is not None: + pos *= head_size / np.median(np.linalg.norm(pos, axis=1)) + + ch_pos = _check_dupes_odict(ch_names_, pos) + nasion, lpa, rpa = (ch_pos.pop(n, None) for n in fid_names) + + return make_dig_montage( + ch_pos=ch_pos, coord_frame="unknown", nasion=nasion, lpa=lpa, rpa=rpa + ) + + +def _read_theta_phi_in_degrees(fname, head_size, fid_names=None, add_fiducials=False): + ch_names, theta, phi = _safe_np_loadtxt( + fname, skip_header=1, dtype=(_str, "i4", "i4") + ) + if add_fiducials: + # Add fiducials based on 10/20 spherical coordinate definitions + # http://chgd.umich.edu/wp-content/uploads/2014/06/ + # 10-20_system_positioning.pdf + # extrapolated from other sensor coordinates in the Easycap layouts + # https://www.easycap.de/wp-content/uploads/2018/02/ + # Easycap-Equidistant-Layouts.pdf + assert fid_names is None + fid_names = ["Nasion", "LPA", "RPA"] + ch_names.extend(fid_names) + theta = np.append(theta, [115, -115, 115]) + phi = np.append(phi, [90, 0, 0]) + + radii = np.full(len(phi), head_size) + pos = _sph_to_cart(np.array([radii, np.deg2rad(phi), np.deg2rad(theta)]).T) + ch_pos = _check_dupes_odict(ch_names, pos) + + nasion, lpa, rpa = None, None, None + if fid_names is not None: + nasion, lpa, rpa = (ch_pos.pop(n, None) for n in fid_names) + + return make_dig_montage( + ch_pos=ch_pos, coord_frame="unknown", nasion=nasion, lpa=lpa, rpa=rpa + ) + + +def _read_elp_besa(fname, head_size): + # This .elp is not the same as polhemus elp. see _read_isotrak_elp_points + dtype = np.dtype("S8, S8, f8, f8, f8") + data = np.loadtxt(fname, dtype=dtype) + + ch_names = data["f1"].astype(str).tolist() + az = data["f2"] + horiz = data["f3"] + radius = np.abs(az / 180.0) + az = np.deg2rad(np.array([h if a >= 0.0 else 180 + h for h, a in zip(horiz, az)])) + pol = radius * np.pi + rad = data["f4"] / 100 + pos = _sph_to_cart(np.array([rad, az, pol]).T) + + if head_size is not None: + pos *= head_size / np.median(np.linalg.norm(pos, axis=1)) + + ch_pos = _check_dupes_odict(ch_names, pos) + + fid_names = ("Nz", "LPA", "RPA") + # No one grants that the fid names actually exist. + nasion, lpa, rpa = (ch_pos.pop(n, None) for n in fid_names) + + return make_dig_montage(ch_pos=ch_pos, nasion=nasion, lpa=lpa, rpa=rpa) + + +def _read_brainvision(fname, head_size): + # 'BrainVision Electrodes File' format + # Based on BrainVision Analyzer coordinate system: Defined between + # standard electrode positions: X-axis from T7 to T8, Y-axis from Oz to + # Fpz, Z-axis orthogonal from XY-plane through Cz, fit to a sphere if + # idealized (when radius=1), specified in millimeters + defusedxml = _soft_import("defusedxml", "reading BrainVision montages") + root = defusedxml.ElementTree.parse(fname).getroot() + ch_names = [s.text for s in root.findall("./Electrode/Name")] + theta = [float(s.text) for s in root.findall("./Electrode/Theta")] + pol = np.deg2rad(np.array(theta)) + phi = [float(s.text) for s in root.findall("./Electrode/Phi")] + az = np.deg2rad(np.array(phi)) + rad = [float(s.text) for s in root.findall("./Electrode/Radius")] + rad = np.array(rad) # specified in mm + pos = _sph_to_cart(np.array([rad, az, pol]).T) + + if head_size is not None: + pos *= head_size / np.median(np.linalg.norm(pos, axis=1)) + + return make_dig_montage(ch_pos=_check_dupes_odict(ch_names, pos)) + + +def _read_xyz(fname): + """Import EEG channel locations from CSV, TSV, or XYZ files. + + CSV and TSV files should have columns 4 columns containing + ch_name, x, y, and z. Each row represents one channel. + XYZ files should have 5 columns containing + count, x, y, z, and ch_name. Each row represents one channel + CSV files should be separated by commas, TSV and XYZ files should be + separated by tabs. + + Parameters + ---------- + fname : str + Name of the file to read channel locations from. + + Returns + ------- + montage : instance of DigMontage + The montage. + """ + ch_names = [] + pos = [] + file_format = op.splitext(fname)[1].lower() + with open(fname) as f: + if file_format != ".xyz": + f.readline() # skip header + delimiter = "," if file_format == ".csv" else "\t" + for row in csv.reader(f, delimiter=delimiter): + if file_format == ".xyz": + _, x, y, z, ch_name, *_ = row + ch_name = ch_name.strip() # deals with variable tab size + else: + ch_name, x, y, z, *_ = row + ch_names.append(ch_name) + pos.append((x, y, z)) + d = _check_dupes_odict(ch_names, np.array(pos, dtype=float)) + return make_dig_montage(ch_pos=d) From 6bd0bc7f2f7c32535575bac73ab0ea70e18c5a79 Mon Sep 17 00:00:00 2001 From: Konstantinos Tsilimparis Date: Thu, 16 Oct 2025 00:15:44 +0200 Subject: [PATCH 25/31] Revert "ENH: allow make_standard_montage to accept and parse the new meg montages" --- mne/channels/_standard_montage_utils.py | 48 ------------------------- 1 file changed, 48 deletions(-) diff --git a/mne/channels/_standard_montage_utils.py b/mne/channels/_standard_montage_utils.py index f5f21312b18..eb3dc10d10e 100644 --- a/mne/channels/_standard_montage_utils.py +++ b/mne/channels/_standard_montage_utils.py @@ -118,52 +118,6 @@ def _mgh_or_standard(basename, head_size, coord_frame="unknown"): ) -def _meg(basename, head_size): - fname = op.join(MONTAGE_PATH, basename) - - # Create a simple class instance instead of a list - class CustomMontage: - pass - - montage = CustomMontage() - - # Read the file - with open(fname) as f: - # Skip header line - _header = f.readline().strip().split() - - ch_names = [] - ch_types = [] - pos = [] - ori = [] - - for line in f: - parts = line.strip().split() - ch_names.append(parts[0]) - ch_types.append(parts[1]) - pos.append([float(parts[2]), float(parts[3]), float(parts[4])]) - ori.append([float(parts[5]), float(parts[6]), float(parts[7])]) - - pos = np.array(pos) - - montage.ch_names = ch_names - - # # Create a dictionary mapping channel names to positions - # montage.ch_pos = dict(zip(ch_names, pos)) - montage.ch_pos = pos - # # TODO - make_dig_montage(): - # For custom montages without fiducials, this parameter must be set - # to ``'head'``. -> - # "kind": FIFF.FIFFV_POINT_EEG, -> - # dig names each channel as EEG #1 ... - - # These aren't standard DigMontage attributes but can be useful - montage.ch_types = ch_types - montage.ori = ori - - return montage - - standard_montage_look_up_table = { "EGI_256": _egi_256, "easycap-M1": partial(_easycap, basename="easycap-M1.txt"), @@ -211,8 +165,6 @@ class CustomMontage: "brainproducts-RNP-BA-128": partial( _easycap, basename="brainproducts-RNP-BA-128.txt" ), - "ctf275": partial(_meg, basename="ctf275.txt"), - "neuromag306": partial(_meg, basename="neuromag306.txt"), } From cb7b55663a77e98188eef20e672f8ad12e1fcaf6 Mon Sep 17 00:00:00 2001 From: Konstantinos Tsilimparis Date: Thu, 16 Oct 2025 00:17:09 +0200 Subject: [PATCH 26/31] Revert "ENH: add neuromag and ctf to the builtin standard montages" This reverts commit 73a5042e76aedac346913e25bafff5373425dc03. --- mne/channels/montage.py | 9 --------- 1 file changed, 9 deletions(-) diff --git a/mne/channels/montage.py b/mne/channels/montage.py index 667239d9f63..d9394fcbfd6 100644 --- a/mne/channels/montage.py +++ b/mne/channels/montage.py @@ -188,15 +188,6 @@ class _BuiltinStandardMontage: name="brainproducts-RNP-BA-128", description="Brain Products with 10-10 electrode names (128 channels)", ), - _BuiltinStandardMontage( - name="ctf275", - description="CTF system with 275 sensors (275 axial gradiometers)", - ), - _BuiltinStandardMontage( - name="neuromag306", - description="Neuromag system with 306 sensors (204 gradiometers, " - "102 magnetometers)", - ), ] From 0c4633a7b3952ec4facd949420fb389f51481aac Mon Sep 17 00:00:00 2001 From: Konstantinos Tsilimparis Date: Thu, 16 Oct 2025 00:20:33 +0200 Subject: [PATCH 27/31] Revert "ENH: add ch_type in the Neuromag and CTF montages" This reverts commit 003a7ac3e9442dd0a8fc5824e5af1a4ffe2b4e4b. --- mne/channels/data/montages/ctf275.txt | 610 ++++++++++---------- mne/channels/data/montages/neuromag306.txt | 614 ++++++++++----------- 2 files changed, 612 insertions(+), 612 deletions(-) diff --git a/mne/channels/data/montages/ctf275.txt b/mne/channels/data/montages/ctf275.txt index cca4d274aa7..d694ffc58ca 100644 --- a/mne/channels/data/montages/ctf275.txt +++ b/mne/channels/data/montages/ctf275.txt @@ -1,305 +1,305 @@ -Name ch_type x y z ex ey ez -MLC11 grad 0.086 0.0103 0.1355 0.346 0.0418 0.9373 -MLC12 grad 0.0742 0.0286 0.1379 0.2638 0.1842 0.9468 -MLC13 grad 0.0659 0.0488 0.1335 0.2456 0.3738 0.8944 -MLC14 grad 0.0535 0.0678 0.1269 0.2278 0.5748 0.786 -MLC15 grad 0.0403 0.0822 0.1182 0.1537 0.6329 0.7588 -MLC16 grad 0.0243 0.0921 0.1072 0.1428 0.8536 0.5009 -MLC17 grad 0.0065 0.0961 0.0911 -0.1102 0.9547 0.2762 -MLC21 grad 0.053 0.0316 0.1426 0.2195 0.2189 0.9507 -MLC22 grad 0.0446 0.0513 0.1382 0.1941 0.3723 0.9076 -MLC23 grad 0.032 0.0677 0.1316 0.1201 0.5713 0.8119 -MLC24 grad 0.0165 0.0793 0.1247 0.1907 0.6616 0.7252 -MLC25 grad -0.0012 0.0872 0.1106 -0.13 0.8567 0.4991 -MLC31 grad 0.0125 0.0602 0.1373 0.0308 0.5018 0.8644 -MLC32 grad -0.0066 0.0744 0.1272 -0.2581 0.7297 0.6332 -MLC41 grad 0.0253 0.0433 0.1441 0.1194 0.2849 0.9511 -MLC42 grad -0.0088 0.0557 0.1389 -0.1183 0.4743 0.8724 -MLC51 grad 0.0496 0.0102 0.1458 0.2095 0.0486 0.9766 -MLC52 grad 0.0314 0.0222 0.1479 0.1552 0.1356 0.9785 -MLC53 grad 0.004 0.0396 0.1467 0.0081 0.2774 0.9607 -MLC54 grad -0.0194 0.0375 0.1451 -0.2143 0.2791 0.936 -MLC55 grad -0.0382 0.0271 0.141 -0.4309 0.1883 0.8825 -MLC61 grad 0.013 0.0105 0.1514 0.0946 0.0683 0.9932 -MLC62 grad -0.0065 0.0207 0.1505 -0.0577 0.1492 0.9871 -MLC63 grad -0.0266 0.01 0.1483 -0.2837 0.0854 0.9551 -MLF11 grad 0.1488 0.0232 0.0484 0.9666 0.2371 0.0971 -MLF12 grad 0.1404 0.0443 0.045 0.8868 0.4525 0.0937 -MLF13 grad 0.1288 0.0631 0.0394 0.7701 0.6319 0.0877 -MLF14 grad 0.1145 0.0777 0.0286 0.5474 0.8335 0.0753 -MLF21 grad 0.1478 0.0112 0.0683 0.969 0.1653 0.1839 -MLF22 grad 0.1426 0.0322 0.0671 0.9288 0.3318 0.1652 -MLF23 grad 0.1351 0.0523 0.0648 0.9218 0.3586 0.1476 -MLF24 grad 0.1204 0.0699 0.058 0.6083 0.7898 0.0787 -MLF25 grad 0.1051 0.0814 0.0481 0.4728 0.8783 0.0709 -MLF31 grad 0.1397 0.0221 0.0869 0.8939 0.2318 0.3838 -MLF32 grad 0.1335 0.0427 0.0846 0.8798 0.3543 0.3169 -MLF33 grad 0.1216 0.0631 0.08 0.6121 0.7156 0.3364 -MLF34 grad 0.1072 0.0777 0.0732 0.5112 0.8475 0.1429 -MLF35 grad 0.0899 0.0869 0.0632 0.3771 0.9225 0.0827 -MLF41 grad 0.1312 0.0108 0.1053 0.7974 0.093 0.5962 -MLF42 grad 0.1279 0.0322 0.1032 0.8482 0.1919 0.4937 -MLF43 grad 0.1173 0.0533 0.1007 0.5845 0.5609 0.5863 -MLF44 grad 0.1048 0.0701 0.0951 0.5442 0.6423 0.5397 -MLF45 grad 0.0895 0.0844 0.0842 0.3562 0.9248 0.1337 -MLF46 grad 0.0709 0.0938 0.0701 0.3579 0.9265 0.1164 -MLF51 grad 0.1171 0.0178 0.1205 0.6859 -0.0517 0.7258 -MLF52 grad 0.1099 0.0412 0.1172 0.4858 0.3986 0.7779 -MLF53 grad 0.0986 0.0599 0.1126 0.5132 0.557 0.653 -MLF54 grad 0.0845 0.0757 0.1058 0.3774 0.7262 0.5746 -MLF55 grad 0.0691 0.0897 0.092 0.2774 0.9278 0.2495 -MLF56 grad 0.051 0.1007 0.0769 0.2979 0.9297 0.2166 -MLF61 grad 0.097 0.0281 0.1293 0.4273 0.2156 0.878 -MLF62 grad 0.0872 0.0476 0.1279 0.3844 0.3885 0.8375 -MLF63 grad 0.0749 0.0647 0.1222 0.2889 0.5885 0.7551 -MLF64 grad 0.0634 0.0803 0.1116 0.2707 0.6822 0.6792 -MLF65 grad 0.0492 0.0931 0.0988 0.2263 0.9378 0.2634 -MLF66 grad 0.0329 0.1 0.0878 0.2508 0.9479 0.1966 -MLF67 grad 0.0152 0.1018 0.0726 -0.2068 0.9692 0.1336 -MLO11 grad -0.0968 0.0218 0.0571 -0.93 0.3418 0.1355 -MLO12 grad -0.0886 0.0429 0.0567 -0.9153 0.3722 0.1538 -MLO13 grad -0.0776 0.0627 0.0565 -0.8038 0.5875 0.0935 -MLO14 grad -0.0614 0.079 0.0557 -0.6252 0.7786 0.0539 -MLO21 grad -0.1003 0.0111 0.0377 -0.954 0.2983 0.0299 -MLO22 grad -0.0948 0.0327 0.0382 -0.9535 0.2977 0.0473 -MLO23 grad -0.0853 0.0538 0.037 -0.8622 0.5064 -0.0152 -MLO24 grad -0.0707 0.0714 0.037 -0.7144 0.6997 -0.0046 -MLO31 grad -0.0977 0.0215 0.0189 -0.9831 0.1812 -0.0252 -MLO32 grad -0.0906 0.043 0.0185 -0.9103 0.4136 -0.0189 -MLO33 grad -0.0784 0.0625 0.0184 -0.7942 0.6076 -0.0103 -MLO34 grad -0.0622 0.079 0.0185 -0.624 0.7814 0.0017 -MLO41 grad -0.0986 0.0104 -0.0003 -0.9945 0.095 -0.044 -MLO42 grad -0.0943 0.0324 -0.0008 -0.9531 0.2999 -0.0399 -MLO43 grad -0.0851 0.0532 -0.0011 -0.8615 0.5068 -0.0327 -MLO44 grad -0.0705 0.071 -0.0001 -0.7144 0.6997 -0.0046 -MLO51 grad -0.0962 0.0209 -0.0191 -0.9825 0.1814 -0.0426 -MLO52 grad -0.0897 0.0432 -0.0195 -0.9167 0.3979 -0.0369 -MLO53 grad -0.0777 0.0626 -0.0196 -0.8046 0.5937 -0.011 -MLP11 grad -0.0553 0.0169 0.1317 -0.6385 0.1138 0.7611 -MLP12 grad -0.0357 0.0483 0.1352 -0.413 0.4016 0.8174 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-0.998 diff --git a/mne/channels/data/montages/neuromag306.txt b/mne/channels/data/montages/neuromag306.txt index ddc37c6b246..41fa747f36a 100644 --- a/mne/channels/data/montages/neuromag306.txt +++ b/mne/channels/data/montages/neuromag306.txt @@ -1,307 +1,307 @@ -Name ch_type x y z ex ey ez -MEG0113 grad -0.1064 0.0529 -0.0203 -0.9829 0.1844 -0.0019 -MEG0112 grad -0.1064 0.0529 -0.0203 -0.9829 0.1844 -0.0019 -MEG0111 mag -0.1064 0.0529 -0.0203 -0.9829 0.1844 -0.0019 -MEG0122 grad -0.1019 0.0726 0.0129 -0.9658 0.2572 -0.0325 -MEG0123 grad -0.1019 0.0726 0.0129 -0.9658 0.2572 -0.0325 -MEG0121 mag -0.1019 0.0726 0.0129 -0.9658 0.2572 -0.0325 -MEG0132 grad -0.1083 0.0397 0.0148 -0.9965 0.0816 -0.0188 -MEG0133 grad -0.1083 0.0397 0.0148 -0.9965 0.0816 -0.0188 -MEG0131 mag -0.1083 0.0397 0.0148 -0.9965 0.0816 -0.0188 -MEG0143 grad -0.1096 0.0195 -0.0196 -0.9995 0.0264 0.0197 -MEG0142 grad -0.1096 0.0195 -0.0196 -0.9995 0.0264 0.0197 -MEG0141 mag -0.1096 0.0195 -0.0196 -0.9995 0.0264 0.0197 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-0.3265 0.0044 +MEG2641 0.1026 -0.0261 0.0198 0.9452 -0.3265 0.0044 \ No newline at end of file From 962db23769e8cf37c9746f261e1d79f4d61253dc Mon Sep 17 00:00:00 2001 From: Konstantinos Tsilimparis Date: Thu, 16 Oct 2025 00:20:58 +0200 Subject: [PATCH 28/31] Revert "ENH: add neuromag306 montage" This reverts commit 81a81d000d61b2187444167a6f1af05aa57b92fa. --- mne/channels/data/montages/neuromag306.txt | 307 --------------------- 1 file changed, 307 deletions(-) delete mode 100644 mne/channels/data/montages/neuromag306.txt diff --git a/mne/channels/data/montages/neuromag306.txt b/mne/channels/data/montages/neuromag306.txt deleted file mode 100644 index 41fa747f36a..00000000000 --- a/mne/channels/data/montages/neuromag306.txt +++ /dev/null @@ -1,307 +0,0 @@ -Name x y z ex ey ez -MEG0113 -0.1064 0.0529 -0.0203 -0.9829 0.1844 -0.0019 -MEG0112 -0.1064 0.0529 -0.0203 -0.9829 0.1844 -0.0019 -MEG0111 -0.1064 0.0529 -0.0203 -0.9829 0.1844 -0.0019 -MEG0122 -0.1019 0.0726 0.0129 -0.9658 0.2572 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Mon Sep 17 00:00:00 2001 From: Konstantinos Tsilimparis Date: Thu, 16 Oct 2025 00:21:21 +0200 Subject: [PATCH 29/31] Revert "ENH: add ctf275 montage" This reverts commit fecef0b05a6d7390fa46283d46045941dd29c3fb. --- mne/channels/data/montages/ctf275.txt | 305 -------------------------- 1 file changed, 305 deletions(-) delete mode 100644 mne/channels/data/montages/ctf275.txt diff --git a/mne/channels/data/montages/ctf275.txt b/mne/channels/data/montages/ctf275.txt deleted file mode 100644 index d694ffc58ca..00000000000 --- a/mne/channels/data/montages/ctf275.txt +++ /dev/null @@ -1,305 +0,0 @@ -Name x y z ex ey ez -MLC11 0.086 0.0103 0.1355 0.346 0.0418 0.9373 -MLC12 0.0742 0.0286 0.1379 0.2638 0.1842 0.9468 -MLC13 0.0659 0.0488 0.1335 0.2456 0.3738 0.8944 -MLC14 0.0535 0.0678 0.1269 0.2278 0.5748 0.786 -MLC15 0.0403 0.0822 0.1182 0.1537 0.6329 0.7588 -MLC16 0.0243 0.0921 0.1072 0.1428 0.8536 0.5009 -MLC17 0.0065 0.0961 0.0911 -0.1102 0.9547 0.2762 -MLC21 0.053 0.0316 0.1426 0.2195 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7578e87a6b58a4ff53e4910424efee1df603bb66 Mon Sep 17 00:00:00 2001 From: Konstantinos Tsilimparis Date: Thu, 16 Oct 2025 00:38:29 +0200 Subject: [PATCH 30/31] ENH: Refactor MEG interpolation example and update comments Reorganized the code to separate EEG and MEG system transformation sections. --- examples/preprocessing/interpolate_to.py | 23 ++++------------------- 1 file changed, 4 insertions(+), 19 deletions(-) diff --git a/examples/preprocessing/interpolate_to.py b/examples/preprocessing/interpolate_to.py index 48d7da97766..711b24190a5 100644 --- a/examples/preprocessing/interpolate_to.py +++ b/examples/preprocessing/interpolate_to.py @@ -39,24 +39,9 @@ ylim = (-10, 10) # %% -# Load the MEG data -data_path = sample.data_path() -fif_file_path = data_path / "MEG" / "sample" / "sample_audvis_raw.fif" -raw_meg = mne.io.read_raw_fif(fif_file_path, preload=True) - -raw_meg.pick("meg") - -# %% -# Define the target montage -standard_montage_meg = make_standard_montage("ctf275") - -# %% -# Use interpolate_to to project MEG data to the standard montage -raw_meg_interpolated_mne = raw_meg.copy().interpolate_to( - standard_montage_meg, method="MNE" -) +# Part 1: EEG System Transformation +# ================================== -# %% # Load EEG data data_path = sample.data_path() eeg_file_path = data_path / "MEG" / "sample" / "sample_audvis-ave.fif" @@ -105,8 +90,8 @@ # %% # Part 2: MEG System Transformation # ================================== -# Now we demonstrate transforming MEG data from Neuromag (planar gradiometers -# and magnetometers) to CTF (radial gradiometers) sensor configuration. +# We demonstrate transforming MEG data from Neuromag (planar gradiometers +# and magnetometers) to CTF (axial gradiometers) sensor configuration. # Load the full evoked data with MEG channels evoked_meg = mne.read_evokeds( From 017b9d5e9a1d14b1cf818669ad80945aaef6b5f7 Mon Sep 17 00:00:00 2001 From: Eric Larson Date: Thu, 16 Oct 2025 10:13:42 -0400 Subject: [PATCH 31/31] FIX: Import --- mne/channels/montage.py | 8 ++++++-- 1 file changed, 6 insertions(+), 2 deletions(-) diff --git a/mne/channels/montage.py b/mne/channels/montage.py index d9394fcbfd6..65d3120d18d 100644 --- a/mne/channels/montage.py +++ b/mne/channels/montage.py @@ -10,7 +10,6 @@ from pathlib import Path import numpy as np -import pandas as pd from .._fiff._digitization import ( _coord_frame_const, @@ -47,6 +46,7 @@ _check_option, _on_missing, _pl, + _soft_import, _validate_type, check_fname, copy_function_doc_to_method_doc, @@ -1684,7 +1684,7 @@ def read_custom_montage( @verbose -def read_meg_montage(system, verbose=None): +def read_meg_montage(system, *, verbose=None): """Load canonical MEG sensor definitions from CSV files. Parameters @@ -1703,7 +1703,10 @@ def read_meg_montage(system, verbose=None): This function loads pre-defined canonical sensor positions and orientations from CSV files stored in mne/channels/data/montages/. + .. versionadded:: 1.11 """ + pd = _soft_import("pandas") + # Validate system input _check_option("system", system, ["neuromag", "ctf151", "ctf275"]) @@ -1722,6 +1725,7 @@ def read_meg_montage(system, verbose=None): raise FileNotFoundError(f"Canonical sensor file not found: {csv_file}. ") # Read sensor definitions + # TODO: Refactor not to use Pandas (should be able to parse without it) df = pd.read_csv(csv_file) # Build channel info list