xiangzhang1015/Deep-Learning-for-BCI
Resources for Book: Deep Learning for EEG-based Brain-Computer Interface: Representations, Algorithms and Applications
This project provides practical guidance and code examples for anyone looking to build brain-computer interface (BCI) systems using deep learning. It helps you take raw brain signal data (like EEG) and process it to understand or predict cognitive states, intentions, or neurological conditions. Researchers, neuroscientists, or biomedical engineers working with brain signals would find this useful for developing BCI applications.
278 stars. No commits in the last 6 months.
Use this if you are a researcher or engineer in neuroscience or BCI, seeking to apply deep learning to classify and interpret various brain signals for applications like authentication, visual reconstruction, or diagnosing neurological disorders.
Not ideal if you are looking for a plug-and-play BCI system for immediate end-user application without diving into the underlying deep learning models and signal processing.
Stars
278
Forks
69
Language
Jupyter Notebook
License
MIT
Category
Last pushed
Jan 31, 2023
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/xiangzhang1015/Deep-Learning-for-BCI"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
mne-tools/mne-python
MNE: Magnetoencephalography (MEG) and Electroencephalography (EEG) in Python
braindecode/braindecode
Deep learning software to decode EEG, ECG or MEG signals
NeuroTechX/moabb
Mother of All BCI Benchmarks
neuromodulation/py_neuromodulation
Real-time analysis of intracranial neurophysiology recordings.
IoBT-VISTEC/MIN2Net
End-to-End Multi-Task Learning for Subject-Independent Motor Imagery EEG Classification (IEEE...