Amir-Hofo/EEGNet
This code implements the EEG Net deep learning model using PyTorch. The EEG Net model is based on the research paper titled "EEGNet: A Compact Convolutional Neural Network for EEG-based Brain-Computer Interfaces".
This tool helps researchers and neuroscientists develop brain-computer interfaces (BCIs) by processing raw electroencephalography (EEG) signals. It takes raw EEG data as input and provides classifications or insights based on brain activity, which can then be used to control external devices or interpret cognitive states. It's designed for those building BCI systems for medical, assistive technology, or research applications.
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Use this if you are a BCI researcher or developer looking for an efficient deep learning model to interpret EEG signals with a compact and powerful network architecture.
Not ideal if you need a plug-and-play solution for BCI applications without any programming or deep learning expertise.
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Apr 02, 2025
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