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arl-eegmodels

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

awesome-graph-neural-networks

Paper Lists for Graph Neural Networks

bci-2021-riemannian-geometry-workshop

Riemannian Geometry workshop at vBCI Meeting 2021

bci_mi_wavelet_cnn

Using wavelet transform to extract time-frequency features of motor imagery EEG signals, and classify it by convolutional neural network

benchmarking-gnns

Repository for benchmarking graph neural networks

biomedical_filter_design

Using the MATLAB filter designer, we created IIR and FIR filters for both EEG and VGRF signals

capsgnn

A PyTorch implementation of "Capsule Graph Neural Network" (ICLR 2019).

cheblienet

ChebLieNet, a spectral graph neural network turned equivariant by Riemannian geometry on Lie groups.

cm-graph

The Python Toolbox for multichannel EEG-EMG connectivity analysis. This package is an extention of mne-tool with the focus on the application of the newest graph and network theory. It is first developped to investigate stroke and autism spectral disorder(ASD) via EEG-EMG coherence marker

cuadrados-medios

Generación de números pseudoaleatorios implementando el método de cuadrados medios

deep-learning-for-bci

Resources for Book: Deep Learning for EEG-based Brain-Computer Interface: Representations, Algorithms and Applications

deepeeg

Deep Learning with Tensor Flow for EEG MNE Epoch Objects

eeg-dl

A Deep Learning library for EEG Tasks (Signals) Classification, based on TensorFlow.

eeg-motor-imagery-classification-cnns-tensorflow

EEG Motor Imagery Tasks Classification (by Channels) via Convolutional Neural Networks (CNNs) based on TensorFlow

eeg-notebooks

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eeg-pipeline

End-to-End EEG Pipeline for cleaning, filtering, ICA, mass-univariate, and decoding analysis using MNE python

eegpreproc

Preprocessing EEG data: Matlab code pipeline and pdf manual

genbci

[Work in progress] A library for EEG BCI data simulation.

geometric-gnn-dojo

Geometric GNN Dojo provides unified implementations and experiments to explore the design space of Geometric Graph Neural Networks.

gnnpapers

Must-read papers on graph neural networks (GNN)

hypermusic

Masters project. Our objective is to characterize a mapping of the neural substrates of social interaction using advanced signal processing and graph/information theory

medusa-kernel

mne-python

MNE: Magnetoencephalography (MEG) and Electroencephalography (EEG) in Python

mushu

BCI signal acquisition

neural-networks-for-time-series-analysis

Compare how ANNs, RNNs, LSTMs, and LSTMs with attention perform on time-series analysis

nsp-course

Python implementations for the "Complete neural signal processing and analysis: Zero to hero" course

powerful-gnns

How Powerful are Graph Neural Networks?

pyeeg

Python + EEG/MEG = PyEEG

pyica

python code for Independent Component Analysis

pysimmibci

Codes for employing PySimMIBCI for MI-EEG data generation and for using such data with FBCNetToolbox models