Unified EEG Feature Extraction for Cross-Subject Driver State Recognition and a Leakage-Free Safe-Stop Trigger Mechanism

Abstract

Electroencephalography (EEG)-based Brain–Computer Interfaces offer direct insight into a driver’s mental state, relevant to autonomous-vehicle perception stacks. This work addresses vigilance/drowsiness specifically, one of several driver states relevant to Level 3 takeover readiness. Detecting drowsiness before handover in Level 3 vehicles requires balancing high-dimensional EEG features against the real-time demands of a lightweight safe-stop trigger mechanism. We present a complete pipeline: a standardized 22-feature-per-channel schema extracted across four driving-related EEG datasets, an autoencoder achieving over 95% dimensionality reduction with minimal performance loss, and a classifier driving a constant-deceleration kinematic profile used to obtain a measurable latency figure, evaluated under a fully subject-disjoint, leakage-free protocol. Leave-one-subject-out cross-validation on the vigilance dataset yields a mean macro-F1 of 0.793 ± 0.163 (0.763 ± 0.155 on an alternative run, within expected autoencoder-retraining variance). Extending compression evaluation to all four datasets shows preserved or improved performance on three, with a small cost on the fourth. The full decision chain completes in under 1 ms (mean 0.13 ms) on standard hardware; this covers the perception-to-trigger budget only, excluding solver latency that an optimization-based planner (MPC/CBF-QP) would add downstream. These findings show cognitive-state-aware safe-stop triggering is implementation-viable, while highlighting the calibration and cross-machine validation needed for deployment.