NEUROFEEDBACK USING MNE PYTHON

This is a software for realtime neurofeeback training based on MNE and PsychoPy for visualization. It uses modules and libraries from MNE to stream EEG signal in real time through LSL (Lab Streaming Layer), do basic preprocessing, automatically rejection bad channels and perform ICA for baseline calibration. The feedback is visualized through PsychoPy tools. The paradigms include: Relative power based-single frequency circle, relative power based-two frequencies quadrant, PAC-based circle

Dependencies

1. Python version 3.9
2. MNE Python version 1.7 or higher (https://mne.tools/stable/install/index.html)
   • mne-realtime (https://mne.tools/mne-realtime/)
   • numpy 1.24.4
   • scipy 1.11.4
   • matplotlib 3.8.4 (backend=QtAgg)
   • mne-icalabel 0.7.0 (requires sklearn version 1.5.1)
3. LSL (https://github.com/sccn/liblsl) and pylsl (https://github.com/chkothe/pylsl)
4. PsychoPy version 2024.2.1 (https://www.psychopy.org/download.html)
5. Python preprocessing module pyprep (https://github.com/sappelhoff/pyprep) for bad channel rejection
6. Artifact subspace reconstruction (ASR) asrpy (https://github.com/DiGyt/asrpy)

Folders

1. Offline codes:
   • Contains the codes for realtime mock LSL streaming of raw EEG, PSD and relative PSD. No prepropressing present.
   • Also contains the basic paradigms running on mock LSL stream
2. Final codes:
   • contains the paradigm codes that work on Real-time LSL stream of EEG signal with preprocessing
   • separate file for baseline recording and calibration