rishannp/MI-PLVGAT
This is works in attempt to develop novel, state-of-the-art models for decoding EEG MI data from patient datasets. Specifically using GAT, highlighting their potential advantages.
This project helps researchers and clinicians accurately interpret motor imagery (MI) brain signals from EEG data, particularly in individuals with conditions like ALS. It takes raw EEG recordings over time and processes them to classify different motor imagery states. This tool is designed for neuroscientists, clinical researchers, and biomedical engineers working with brain-computer interfaces (BCIs) and neurodegenerative diseases.
No commits in the last 6 months.
Use this if you need to classify motor imagery EEG signals with improved accuracy and stability, especially when dealing with data that shows significant variability across subjects or recording sessions.
Not ideal if you are looking for a general-purpose EEG analysis tool beyond motor imagery classification, or if your primary interest is in real-time BCI control rather than research-focused signal decoding.
Stars
14
Forks
2
Language
Python
License
GPL-3.0
Category
Last pushed
Jul 21, 2025
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/rishannp/MI-PLVGAT"
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...