EEG-Based Emergency Braking Prediction Using Data Ablation and SVM Classification

Contemporary advanced driver assistance system (ADAS) features for semi-autonomous vehicles include braking assistance during collision avoidance. Although precollision detection typically relies on sensing systems to enable production vehicles to perceive oncoming road obstacles, the physiological state of the driver is not measured to predict emergency braking. On the other hand, previous driving simulation experiments have demonstrated the ability of regularized linear discriminant analysis (RLDA) to predict precollision braking using brain signals from multiple electroencephalogram (EEG) electrodes. In contrast, the current study used EEG data from these previous experiments to determine the quality of support vector machine (SVM) predictions as a first step toward realizing a brain-computer interface (BCI) for emergency braking. Power spectral density (PSD) features were extracted from the EEG of one electrode to train and evaluate an SVM. Through a novel data ablation analysis, the optimal number of PSD components was determined to optimize model classification quality measured by the area under the curve (AUC). A comparison of the proposed model to the previous RLDA and other machine learning (ML) methods indicated that the SVM had a superior AUC. Thus, the proposed model is a candidate for assisting ADASs with precollision detection. Moreover, since the proposed model only utilized one electrode, our study potentially contributes to the facilitation of BCIs for autonomous vehicles.