IoBT-VISTEC/MIN2Net
End-to-End Multi-Task Learning for Subject-Independent Motor Imagery EEG Classification (IEEE Transactions on Biomedical Engineering)
This project helps brain-computer interface (BCI) researchers evaluate and develop new algorithms for classifying motor imagery from EEG signals. It takes raw or preprocessed EEG data and produces a classification of imagined movements, such as left hand or right hand. Researchers who are developing or benchmarking BCI models would use this.
103 stars. No commits in the last 6 months. Available on PyPI.
Use this if you are a BCI researcher needing to construct a robust pipeline for benchmarking and validating motor imagery EEG classification models.
Not ideal if you are looking for a plug-and-play BCI system for direct user application without research and development.
Stars
103
Forks
25
Language
Python
License
Apache-2.0
Category
Last pushed
May 30, 2025
Commits (30d)
0
Dependencies
5
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