KnowEEG: Explainable Knowledge Driven EEG Classification
Amarpal Sahota, Navid Mohammadi Foumani, Raul Santos-Rodriguez, Zahraa S. Abdallah

TL;DR
KnowEEG is an explainable machine learning method that combines statistical EEG features and connectivity analysis with a modified Random Forest to improve classification performance and interpretability in EEG-based applications.
Contribution
It introduces a novel explainable approach that integrates domain knowledge and statistical features into a Random Forest model for EEG classification.
Findings
Achieves performance comparable or superior to deep learning models
Provides explainability through feature importance scores
Discovered knowledge aligns with neuroscience literature
Abstract
Electroencephalography (EEG) is a method of recording brain activity that shows significant promise in applications ranging from disease classification to emotion detection and brain-computer interfaces. Recent advances in deep learning have improved EEG classification performance yet model explainability remains an issue. To address this key limitation of explainability we introduce KnowEEG; a novel explainable machine learning approach for EEG classification. KnowEEG extracts a comprehensive set of per-electrode features, filters them using statistical tests, and integrates between-electrode connectivity statistics. These features are then input to our modified Random Forest model (Fusion Forest) that balances per electrode statistics with between electrode connectivity features in growing the trees of the forest. By incorporating knowledge from both the generalized time-series and…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
