用可解释的特征融合方法提升脑电图分类性能

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 EEG-specific domains, KnowEEG achieves performance comparable to or exceeding state-of-the-art deep learning models across five different classification tasks: emotion detection, mental workload classification, eyes open/closed detection, abnormal EEG classification, and event detection. In addition to high performance, KnowEEG provides inherent explainability through feature importance scores for understandable features. We demonstrate by example on the eyes closed/open classification task that this explainability can be used to discover knowledge about the classes. This discovered knowledge for eyes open/closed classification was proven to be correct by current neuroscience literature. Therefore, the impact of KnowEEG will be significant for domains where EEG explainability is critical such as healthcare.