Axial to planar gradiometer transformation

examples/preprocessing/interpolate_to.py

@@ -2,21 +2,30 @@
 .. _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 <[email protected]>
+#          Konstantinos Tsilimparis <[email protected]>
 # License: BSD-3-Clause
 # Copyright the MNE-Python contributors.
 
@@ -30,6 +39,9 @@
 ylim = (-10, 10)
 
 # %%
+# Part 1: EEG System Transformation
+# ==================================
+
 # Load EEG data
 data_path = sample.data_path()
 eeg_file_path = data_path / "MEG" / "sample" / "sample_audvis-ave.fif"
@@ -75,6 +87,63 @@
 )
 axs[2].set_title("Interpolated to Standard 1020 Montage using MNE interpolation")
 
+# %%
+# Part 2: MEG System Transformation
+# ==================================
+# 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(
+    eeg_file_path, condition="Left Auditory", baseline=(None, 0)
+)
+evoked_meg.pick("meg")
+
+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'))}")
+
+# %%
+# 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: {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 Planar 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
 # ----------