MedMaxLab/eegprepro
Evaluating the role of EEG preprocessing for deep learning applications.
This project investigates how different levels of EEG data preprocessing affect the accuracy of deep learning models in various neurological classification tasks. It takes raw EEG recordings and processes them through different pipelines, then feeds them into deep learning models to classify conditions like Parkinson's, Alzheimer's, or sleep states. Researchers and clinicians working with EEG data for diagnostic or brain-computer interface applications would find this useful.
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Use this if you are developing or evaluating deep learning models for EEG analysis and need to understand the impact of preprocessing choices on model performance.
Not ideal if you are looking for a plug-and-play tool for immediate clinical diagnosis or a general-purpose EEG analysis software.
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Jupyter Notebook
License
MIT
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Last pushed
Mar 10, 2025
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