MedMaxLab/eegpartition
Evaluating the role of EEG data partition for deep learning applications.
When developing deep learning models that analyze EEG signals for tasks like diagnosing neurological conditions or brain-computer interfaces, ensuring your model's performance estimates are accurate and reliable is critical. This project helps researchers and scientists understand how different ways of splitting EEG data (called partitioning) impact the reported accuracy of their deep learning models. It takes raw or preprocessed EEG data and a specific deep learning model, then evaluates various data partitioning strategies to show how they can inflate or deflate performance metrics, ultimately guiding you to more robust evaluation practices. This project is for neuroscience researchers, clinical scientists, and AI/ML researchers working with EEG data in medical or BCI applications.
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Use this if you are a researcher or scientist developing deep learning models with EEG data and need to ensure your model's reported performance is accurate and not overly optimistic due to flawed data partitioning.
Not ideal if you are looking for a pre-built, production-ready EEG analysis tool or if your primary goal is to train a model without critically evaluating the robustness of its performance metrics.
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MIT
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Jul 01, 2025
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