Single-Lead EEG-based virtual reality motion sickness detection using interpretable feature selection.

BACKGROUND

Electroencephalography (EEG) is a key method for detecting virtual reality motion sickness (VRMS), but multi-lead recordings and high-dimensional features increase processing complexity, device cost, and user burden.

OBJECTIVE

This study proposes a SHapley Additive exPlanations (SHAP)-based single-lead modeling and feature-screening framework to reduce the number of EEG leads and feature dimensions for lightweight VRMS detection.

METHOD: Six classical deep-learning EEG decoding models were used to assess the contribution of each lead via SHAP values and identify the optimal single lead. Handcrafted features were then extracted and selected using a correlation-SHAP strategy, and XGBoost was adopted for final classification.

RESULTS

FT11 showed competitive and relatively consistent performance across the evaluated models and was selected as the target lead after integrating SHAP-based lead importance, single-lead classification performance, frequency-band differences, and physiological relevance. Under the window-level tenfold cross-validation protocol used in this study, the proposed model achieved an accuracy of 87.53% ± 0.73% using seven selected features, which was 9.55 percentage points lower than the full-lead full-feature model and 0.69 percentage points lower than the single-lead full-feature model.

SIGNIFICANCE: These findings demonstrate an interpretable framework for identifying a compact single-lead and feature configuration for VRMS-related EEG discrimination. An additional post hoc LOSO test of the globally selected configuration showed limited performance on held-out participants. Because the lead, classifier, and feature configuration had been selected globally before LOSO testing, these results should not be interpreted as a fully independent estimate of cross-subject generalization.