amrzhd/EEGNet
This project focuses on implementing CNN model based on the EEGNet architecture with Pytorch library for classifying motor imagery tasks using EEG data.
This project helps researchers and engineers working with brain-computer interfaces (BCIs) to automatically identify imagined movements from brainwave data. It takes raw or preprocessed EEG signals, specifically those related to motor imagery tasks, and classifies them into specific intended actions like 'Left Hand', 'Right Hand', 'Foot', or 'Tongue'. Neuroscientists, BCI developers, and medical researchers would find this useful for analyzing brain activity patterns.
165 stars. No commits in the last 6 months.
Use this if you need to classify motor imagery tasks from EEG data, specifically distinguishing between imagined movements of the left hand, right hand, foot, and tongue.
Not ideal if your EEG data pertains to different types of brain activity (e.g., visual evoked potentials, sleep stages) or requires classification beyond these four motor imagery classes.
Stars
165
Forks
10
Language
Jupyter Notebook
License
MIT
Category
Last pushed
Sep 08, 2024
Commits (30d)
0
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