Popular GitHub Repositories for Bci

? 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。

AI wearables. Put it on, speak, transcribe, automatically

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

BCI: Breast Cancer Immunohistochemical Image Generation through Pyramid Pix2pix

A P300 online spelling mechanism for Emotiv headsets. It's completely written in Node.js, and the GUI is based on Electron and Vue.

Python Brain-Computer Interface Software

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

[IEEE J-BHI-2024] A Convolutional Transformer to decode mental states from Electroencephalography (EEG) for Brain-Computer Interfaces (BCI)

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).

? Brain-Computer Interfacing bootcamp course + projects @ Saturdays.AI (BCI + AI)

Code to accompany our International Conference on Pattern Recognition (ICPR) paper entitled - Leveraging Synthetic Subject Invariant EEG Signals for Zero Calibration BCI.

Using multi-task learning to capture signals simultaneously from the fovea efficiently and the neighboring targets in the peripheral vision generate a visual response map. A calibration-free user-independent solution, desirable for clinical diagnostics. A stepping stone for an objective assessment of glaucoma patients’ visual field.

This is a toolbox to construct adversarial examples of EEG signals. The traditional EEG extraction methods and classifiers are re-implemented in Tensorflow.

EEG Motor Imagery with Deep Learning

Towards Naturalistic BCI: Applying Deep Learning to Decode Brain Activity During Passive Listening to Speech

Source code for the paper "Online speech synthesis using a chronically implanted brain-computer interface in an individual with ALS" by Angrick et al.

This work aims to classify the occurrence of a feedback error, i.e., the perception of an error by a user interacting with a Brain-Computer Interface (BCI). To achieve that, Convolutional Neural Network (CNN) models are developed.

Mental Attention State Classification for the Human Brain using EEG-based BCI data

Here lies a collection of Python scripts used to connect OpenVibe and Arduino using several Machine Learning algorithms in order to control a hand prosthesis