amrzhd/EEG-MSCNN
This project explores the impact of Multi-Scale CNNs on the classification of EEG signals in Brain-Computer Interface (BCI) systems. By comparing the performance of two models, EEGNet and MSTANN, the study demonstrates how richer temporal feature extractions can enhance CNN models in classifying EEG signals
This project helps researchers and engineers analyze raw brainwave (EEG) signals to classify specific brain activities, such as imagined hand movements, for Brain-Computer Interface (BCI) systems. It takes raw or pre-processed EEG recordings as input and outputs a classification of the brain activity, showing how different methods of extracting temporal features can improve accuracy. This is useful for neuroscientists, biomedical engineers, and BCI system developers.
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Use this if you need to accurately classify specific mental states or motor intentions from EEG data to build or improve Brain-Computer Interface applications.
Not ideal if you are looking for a general-purpose EEG analysis tool for broader diagnostic or signal processing tasks outside of classification for BCIs.
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MIT
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Last pushed
Aug 11, 2025
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