yiamcb/TAFN
Temporal Attention Fusion Network with Custom Loss Function for EEG-fNIRS Classification
This project helps researchers and engineers analyze and classify brain activity. It takes raw or pre-processed electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) data as input. The output is a classification of cognitive and motor intentions, which can be used to understand brain states or control brain-computer interfaces. It is designed for neuroscientists, biomedical engineers, and BCI practitioners working with multimodal brain data.
No commits in the last 6 months.
Use this if you need to accurately classify cognitive or motor intentions from combined EEG and fNIRS brain signals, especially when dealing with imbalanced datasets.
Not ideal if your primary interest is in single-modality brain signal analysis or if you require real-time processing for immediate brain-computer interface applications without further development.
Stars
12
Forks
3
Language
Python
License
—
Category
Last pushed
Feb 20, 2025
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/yiamcb/TAFN"
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...