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arXiv Machine Learning By Amarpal Sahota, Navid Mohammadi Foumani, Raul Santos-Rodriguez, Zahraa S. Abdallah

KnowEEG: Explainable Knowledge Driven EEG Classification

Read the original on arXiv Machine Learning →

KnowEEG is a feature‑based EEG classification pipeline that extracts 783 per‑electrode statistical features and cross‑brain connectivity metrics, using a balanced sampling strategy to integrate high‑dimensional per‑electrode data with connectivity information. It achieves performance that matches or surpasses state‑of‑the‑art deep learning models on five diverse tasks—including emotion detection, mental workload, eyes open/closed, abnormal EEG, and event detection—without requiring GPU resources. The approach also offers inherent explainability via feature importance rankings, identifying occipital biomarkers in the eyes open/closed task that align with established neuroscience literature.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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