Optimization of EEG-based wheelchair control: machine learning, feature selection, outlier management, and explainable AI

Abstract

Classifying Electroencephalogram (EEG) signals for wheelchair navigation presents significant challenges due to high dimensionality, noise, outliers, and class imbalances. This study proposes an optimized classification framework that evaluates ten machine learning (ML) models, emphasizing ensemble methods, feature selection (FS), and outlier utilization. The dataset, comprising 2869 samples and 141 features, was processed using Recursive Feature Elimination (RFE) and correlation thresholds (CTs), achieving a peak accuracy of 69% with Extra Trees after FS. Notably, training on outlier-only data yielded even higher accuracy (Extra Trees: 82%), underscoring the value of outliers in enhancing class separability. Receiver Operating Characteristic–Precision Recall (ROC-PR) curve analysis confirmed that Extra Trees achieved a ROC AUC (Area Under Curve) of 0.92 and PR AUC of 0.82 for the best-classified movement command, while other models exhibited lower precision-recall (PR) balance. This approach, complemented by explainability techniques, offers a robust solution for EEG-based wheelchair control systems and paves the way for interpretable brain-computer interfaces (BCIs).