There are 29 repositories under bci topic.
AI wearables. Put it on, speak, transcribe, automatically
🤖 wukong-robot 是一个简单、灵活、优雅的中文语音对话机器人/智能音箱项目,支持ChatGPT多轮对话能力,还可能是首个支持脑机交互的开源智能音箱项目。
Scouter is an open source APM (Application Performance Management) tool.
BrainFlow is a library intended to obtain, parse and analyze EEG, EMG, ECG and other kinds of data from biosensors
Not supported. Measure 8 EEG channels with Shield PiEEG and RaspberryPi in C library
Mother of All BCI Benchmarks
Wearable (BLE) Brain-Computer Interface, ADS1299 and STM32 with SDK for mobile application
MetaBCI: China’s first open-source platform for non-invasive brain computer interface. The project of MetaBCI is led by Prof. Minpeng Xu from Tianjin University, China.
An open software package to develop BCI based brain and cognitive computing technology for recognizing user's intention using deep learning
[Old version] PyTorch implementation of EEGNet: A Compact Convolutional Network for EEG-based Brain-Computer Interfaces - https://arxiv.org/pdf/1611.08024.pdf
BrainFlow code that sends your brain's relaxation, focus metrics, and machine learned thought commands to vrchat avatar paramaters via OSC.
CTNet: A Convolutional Transformer Network for EEG-Based Motor Imagery Classification
Resources for Book: Deep Learning for EEG-based Brain-Computer Interface: Representations, Algorithms and Applications
Interactive neuroscience tutorial app using Muse and React Native to teach EEG and BCI basics.
Interactive Brain Playground - Browser based tutorials on EEG with webbluetooth and muse
A ViT based transformer applied on multi-channel time-series EEG data for motor imagery classification
A P300 online spelling mechanism for Emotiv headsets. It's completely written in Node.js, and the GUI is based on Electron and Vue.
This project focuses on implementing CNN model based on the EEGNet architecture with Pytorch library for classifying motor imagery tasks using EEG data.
Low Cost Electroencephalogram Based Brain-Computer-Interface
Python Brain-Computer Interface Software
Digital signal processing utilities as RxJS operators for working with EEG data in Node and the Browser
Implementation of ConvLSTM in pytorch applied for BCI (Brain Machine Interface) following paper: Convolutional LSTM Network-A Machine Learning Approach for Precipitation Nowcasting
End-to-End Multi-Task Learning for Subject-Independent Motor Imagery EEG Classification (IEEE Transactions on Biomedical Engineering)
Must-read papers on machine learning, deep learning, reinforcement learning and other learning methods for brain-computer interfaces.
A MATLAB package for modelling multivariate stimulus-response data
[IEEE J-BHI-2024] A Convolutional Transformer to decode mental states from Electroencephalography (EEG) for Brain-Computer Interfaces (BCI)
Material for the BCI Workshop held at District 3 in May 2015 by BCI Montréal.
This project explores the impact of Multi-Scale CNNs on the classification of EEG signals in Brain-Computer Interface (BCI) systems. By comparing the performance of two models, EEGNet and MSTANN, the study demonstrates how richer temporal feature extractions can enhance CNN models in classifying EEG signals
A research repository of deep learning on electroencephalographic (EEG) for Motor imagery(MI), including eeg data processing(visualization & analysis), papers(research and summary), deep learning models(reproduction and experiments).