KnowEEG: Explainable Knowledge Driven EEG Classification

TL;DR

This work tackles the explainability gap in EEG classification by introducing KnowEEG, which builds a large, interpretable feature space of $783$ per-electrode statistics plus connectivity metrics and feeds them into a Fusion Forest that balances electrode-wise information with inter-electrode connectivity. It achieves performance that matches or surpasses state-of-the-art deep learning methods across five EEG tasks, while providing intrinsic explainability via direct feature importances and neuroscience-consistent insights (e.g., occipital-region and alpha/gamma band patterns for eyes-open/closed). The approach is CPU-friendly and does not require GPUs, broadening accessibility for clinical and real-world deployments. Explainability analyses demonstrate meaningful knowledge discovery that aligns with current neuroscience literature, underscoring KnowEEG’s potential impact in healthcare domains where interpretability is critical.

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.

Figures (7)

• Figure 1: KnowEEG Pipeline: There are two parallel threads to the pipeline. In thread one, 783 features from generalised and EEG-specific time-series literature are calculated per electrode and concatenated. Feature relevance is evaluated via univariate non-parametric hypothesis tests (Mann-Whitney U for binary targets and Kruskal-Wallis tests for multiclass targets), with Benjamini–Yekutieli correction to control the false discovery rate ($\alpha = 0.05$), filtering out uninformative features. In thread two, between-electrode connectivity features are calculated for connectivity metrics. The best-performing metric is selected using classifier performance on the validation data. The features from thread one and thread two are then combined using the Fusion Forest (see Algorithm \ref{['alg:fusionforest']}).
• Figure 2: This histogram displays the Gini importance values for the Functional Power Connectivity features (FPC) used by the trained KnowEEG model on the Crowdsource dataset. The x-axis represents the feature importance score with the y-axis showing the count of features at that importance level. The distribution is highly skewed with an exponential-like decay, showing that a large majority of features have an importance score near zero, while a small group of features have higher importance scores.
• Figure 3: Feature Importance score per power band for Functional Power Connectivity on the Crowdsource dataset. FPC gamma features have the highest feature importance rank score overall followed by alpha, delta, theta and sigma with the lowest score.
• Figure 4: This plot shows mean relative band power in the alpha and gamma bands for eyes closed (left) and eyes open (right) participants. For the Alpha band the eyes closed group have a higher mean power particular in the Occipital Lobes towards the back of the head. For the Gamma band the eyes open group have a higher mean power with peaks in the Left and Right Temporal / Central regions of the brain.
• Figure 5: This plot shows the mean Alpha and Gamma band connectivity for the eyes closed (left) and eyes open (right) groups per the FPC features (defined in \ref{['FPC']}). The eyes closed group have higher Alpha connectivity across the brain particularly in the Occipital regions. For Gamma connectivity the differences between the groups are not visually significant.