Electroencephalography (EEG) is a non-invasive method for recording electrical activity in the brain, first performed on humans by Hans Berger in 1924 (Berger, 1929).
MNE: Magnetoencephalography (MEG) and Electroencephalography (EEG) in Python
NeuroKit2: The Python Toolbox for Neurophysiological Signal Processing
BrainFlow is a library intended to obtain, parse and analyze EEG, EMG, ECG and other kinds of data from biosensors
A curated list of awesome neuroscience libraries, software and any content related to the domain.
This is the Army Research Laboratory (ARL) EEGModels Project: A Collection of Convolutional Neural Network (CNN) models for EEG signal classification, using Keras and Tensorflow
Deep learning software to decode EEG, ECG or MEG signals
The MATLAB toolbox for MEG, EEG and iEEG analysis
Not supported. Measure 8 EEG channels with Shield PiEEG and RaspberryPi in C library
Mother of All BCI Benchmarks
A set of functions for supervised feature learning/classification of mental states from EEG based on "EEG images" idea.
Machine learning for multivariate data through the Riemannian geometry of positive definite matrices in Python
EEGLAB is an open source signal processing environment for electrophysiological signals running on Matlab and developed at the SCCN/UCSD
EEG Transformer 2.0. i. Convolutional Transformer for EEG Decoding. ii. Novel visualization - Class Activation Topography.
A unified multi-task time series model.
Wearable (BLE) Brain-Computer Interface, ADS1299 and STM32 with SDK for mobile application
A list of openly available datasets in (mostly human) electrophysiology.
DeepSleepNet: a Model for Automatic Sleep Stage Scoring based on Raw Single-Channel EEG
[IJCAI-21] "Time-Series Representation Learning via Temporal and Contextual Contrasting"
Brainstorm software: MEG, EEG, fNIRS, ECoG, sEEG and electrophysiology
An open software package to develop BCI based brain and cognitive computing technology for recognizing user's intention using deep learning