EEG-Based Drowsiness Detection With Fuzzy Independent Phase-Locking Value Representations Using Lagrangian-Based Deep Neural Networks

Abstract

Passive electroencephalogram (EEG) brain–computer interfaces (BCI) have common usage in the area of Driver Drowsiness Detection. The approach presented herein identifies the cognitive state of the user while no mental action is required. Data recorded in EEG-based BCI experiments are generally noisy, nonstationary, and contaminated with artifacts that can deteriorate any analyzer’s performance. Recently, common spatial patterns (CSPs) have been adapted with EEG state-space incorporating spatiospectral optimization using fuzzy time delay (FTD-CSSP). Temporal phase disparity sequence (TPDS) is used to measure synchrony between EEG signals. The output of Linear transforms operating on the TPDS constitute useful features for EEG regression problems. On similar lines, this article proposes spatiospectral optimized fuzzy-independent phase-locking value (SSO-FIPLV) representations (exploiting the spatiospectral information from TPDS) for EEG signals to monitor a user’s cognitive states. Specifically, we analyze changes in EEG synchronization for a car driver as she/he drifts between alert and drowsy states. We use neural networks (NNs) for prediction. This article also proposes a cutting-edge method for training NN using the Euler–Lagrangian formulation. A stability proof is provided for the intended training approach alongside, and the performance is corroborated on the EEG reaction time prediction task, both within and across subjects, using a publicly available dataset. The NN trained by the proposed approach performs better than other competitive approaches in terms of minimizing root-mean-squared error and maximizing correlation coefficient. Channelwise feature importance in terms of average relevance values calculated from NN feature representations is visualized in the form of Topoplots using layerwise relevance propagation for regression.