Python Intan

Overview

python-intan is a Python package for working with EMG data acquired from Intan Technologies systems. It provides utilities for:

• Reading .rhd and .dat data files
• Preprocessing and feature extraction
• Integration with external hardware for closed-loop applications

Whether you're analyzing offline data or streaming real-time EMG from the RHX system, python-intan aims to make the workflow seamless and modular.

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Contents

• Installation
• Getting Started
• Project Structure
• Future Improvements
• License

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Installation

Using pip:

pip install python-intan

or clone the repository from GitHub for the latest features:

git clone https://github.com/Neuro-Mechatronics-Interfaces/python-intan.git
cd python-intan
pip install -e .

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Getting Started

We can quickly load EMG signals from an Intan .rhd file.

import intan

result = intan.io.load_rhd_file() # Opens Popup window to select file, or pass filepath

.dat files are also supported

dat_folderpath = "path/to/your/dat/files"
result = intan.io.load_dat_file(dat_folderpath)

The result dictionary contains:

• amplifier_data: Raw EMG data (shape: channels x samples)
• amplifier_channels: Channel metadata (names, gain, etc.)
• frequency_parameters: Sampling rate information for the amplifier, aux, and digital inputs
• t_amplifier: Time vector for the amplifier data
• board_adc_data: ADC data from the board

Loading multiple .rhd files with the option to concatenate can be done with:

result_list = intan.io.load_rhd_files(concatenate=True)

Some quick filtering to remove DC offset and noise:

fs = result['frequency_parameters']['amplifier_sample_rate']
emg = result['amplifier_data']  # Shape: (channels, samples)

notch_emg = intan.processing.notch_filter(emg, fs, f0=60) # 60Hz notch filter
filt_emg = intan.processing.filter_emg(notch_emg, 'bandpass', fs, lowcut=10, highcut=500) # 10-500Hz bandpass filter

We can visualize the raw EMG data using the plotting module. Let's use a waterfall plot to look at the raw activity across a subset of channels:

t_s = result['t_amplifier']
chans = range(0, 64)  # Selecting channels to plot
intan.plotting.waterfall(filt_emg, chans, t_s, plot_title='Intan EMG data')

If there is a single channel of interest, we can plot it directly by name or index:

intan.plotting.plot_channel_by_name('A-005', result)  # By name (Be sure the channel name exists)
intan.plotting.plot_channel_by_index(8, result)  # By index

Some other processing features:

# Normalize the EMG data
norm_emg = intan.processing.normalize(filt_emg, method='zscore')  # Z-score normalization

# Downsample the EMG data
downsampled_emg = intan.processing.downsample(filt_emg, factor=2)  # Downsample by a factor of 2

# Rectify
rectified_emg = intan.processing.rectify(filt_emg)  # Rectify the EMG data

# Rolling RMS window
samples = int(0.1 * fs)  # 100ms window
rms_emg = intan.processing.window_rms(filt_emg, window_size=samples)  # 100ms RMS window

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Project Structure

intan/
├── io/                 # Load and parse Intan .rhd or .rhs files
├── processing/         # Filtering, normalization, and segmentation
├── control/            # Real-time control scripts for robot arm or other peripherals
├── plotting/      # Plotting and GUI scripts

load_rhd_demo.py        # Quick-start script for plotting waveform data
requirements.txt
README.md

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Future Improvements

• Real-time gesture decoding via TCP from RHX
• Serial control for wearable robotics (e.g. grippers, arms)
• CNN feature pipeline support
• GUI for real-time signal monitoring
• Public datasets with training/testing support
• Cross-platform support for macOS/Linux (currently optimized for Windows)

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Citation

This package can be cited as follows:

@software{Shulgach_Python_Intan_2025,
  author = {Shulgach, Jonathan, Murphy, Max and Foy, Adrian},
  title = {{Python Intan Package}},
  year = {2025},
  month = {05},
  version = {0.1.0},
  url = {https://github.com/Neuro-Mechatronics-Interfaces/python-intan},
  note = "{\tt [email protected]}"
}

License

This project is licensed under the MIT License — see the LICENSE file for details.