brain-computer-interface

BrainFlow is a library intended to obtain, parse and analyze EEG, EMG, ECG and other kinds of data from biosensors

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

Mother of All BCI Benchmarks

Machine learning for multivariate data through the Riemannian geometry of positive definite matrices in Python

CTNet: A Convolutional Transformer Network for EEG-Based Motor Imagery Classification

[Old version] PyTorch implementation of EEGNet: A Compact Convolutional Network for EEG-based Brain-Computer Interfaces - https://arxiv.org/pdf/1611.08024.pdf

Brain-Computer interface stuff

Muse 2016 EEG Headset JavaScript Library (using Web Bluetooth)

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

code for AAAI2022 paper "Open Vocabulary Electroencephalography-To-Text Decoding and Zero-shot Sentiment Classification"

The programming interface for your body and mind

A new approach based on a 10-layer one-dimensional convolution neural network (1D-CNN) to classify five brain states (four MI classes plus a 'baseline' class) using a data augmentation algorithm and a limited number of EEG channels. Paper: https://doi.org/10.1088/1741-2552/ac4430

Low Cost Electroencephalogram Based Brain-Computer-Interface

Python Brain-Computer Interface Software

Must-read papers on machine learning, deep learning, reinforcement learning and other learning methods for brain-computer interfaces.

Implementation of ConvLSTM in pytorch applied for BCI (Brain Machine Interface) following paper: Convolutional LSTM Network-A Machine Learning Approach for Precipitation Nowcasting

A wheelchair controlled by EEG brain signals and enhanced with assisted driving

End-to-End Multi-Task Learning for Subject-Independent Motor Imagery EEG Classification (IEEE Transactions on Biomedical Engineering)

A MATLAB package for modelling multivariate stimulus-response data

Human Infra: docs-as-code knowledge base for human runtime infrastructure, longevity evidence, future-waiting paths, disembodied CNS concepts, and memory-editing boundaries.