hancan16/SST-DPN
A Lightweight and High Performance Neural network for MI-EEG decoding
This project helps researchers and engineers working with Brain-Computer Interfaces (BCI) analyze Motor Imagery (MI) EEG signals. It takes raw EEG data from MI tasks and classifies the intended movement (e.g., left hand, right hand), providing accurate decoding results. This tool is designed for BCI developers and neuroscientists who need to interpret brain signals for assistive technologies or research.
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Use this if you are developing or researching real-time Motor Imagery Brain-Computer Interface applications and need to accurately decode complex EEG signals, especially with limited data.
Not ideal if your primary focus is on other types of EEG analysis beyond motor imagery, or if you require a pre-built, non-customizable commercial solution.
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40
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Language
Python
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Last pushed
Mar 18, 2025
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