Noninvasive neural signal-based detection of soft and emergency braking intentions of drivers

In this paper, to address driving safety under emergency situations, we investigated the neural signatures of emergency braking and soft braking of drivers and proposed recognition methods to distinguish the two kinds of braking intentions by using electroencephalography (EEG) signals. The neural signatures are captured in the temporal and spectral domains of EEG signals. Temporal features, spectral features, and combined features were used as input features of the recognition model based on the regularized linear discrimination analysis. Experimental results showed significant differences in neural signatures between emergency braking and soft braking, and the recognition model can detect the emergency braking and soft braking intentions from the normal driving intention well. These features were extracted from response-locked segments. The recognition accuracy for the three-class classification reached 78.33% ± 6.86% by using the system based on combined features. This works can further advance the development of EEG-based human-centric assistant driving systems to improve driving safety and driving comfort.