KnowEEG: Explainable Knowledge Driven EEG Classification

2025-05-01

AI Summary

To address the limited interpretability of deep learning models in electroencephalography (EEG) classification, this paper proposes an interpretable framework integrating domain-specific priors with statistical feature engineering. Our core contribution is the novel “Fusion Forest”—a random forest architecture that jointly models univariate temporal statistical features per electrode and multivariate functional connectivity features across electrodes. It incorporates multi-scale temporal representations, statistical significance–driven feature selection, and an enhanced ensemble strategy. Evaluated on five benchmark tasks—including emotion recognition and mental workload estimation—the framework achieves performance comparable to or exceeding state-of-the-art deep models. Crucially, feature importance analysis robustly recovers canonical neurophysiological mechanisms: for instance, it identifies occipital alpha rhythm suppression during eye-opening versus eye-closing states—a finding empirically validated by neuroscience literature. Thus, the method delivers both high predictive accuracy and neuroscientifically grounded interpretability.

Technology Category

• Machine Learning: Ensemble Methods
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization
• Web Mining and Content Analysis: Large pretrained models with web data

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 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.

Problem

• Addressing EEG classification explainability in deep learning models
• Integrating per-electrode and between-electrode connectivity features
• Achieving high performance with interpretable feature importance scores

Innovation

• Extracts per-electrode features with statistical filtering
• Uses modified Random Forest balancing electrode statistics
• Integrates time-series and EEG-specific domain knowledge