Decision of braking intensity during simulated driving based on analysis of neural correlates

Abstract

Recently neurophysiological studies have been concerned with using brain signals for driving assistance technologies. These studies verified that neurophysiological characteristics could be used for detection of emergency situations during simulated driving. However, it is hard to develop the braking assistant system which could control the vehicle continuously using this approach. In this article, the method for decoding of driver's braking intention based on analysis of neural correlates is proposed to control the braking of vehicle continuously. The participants' braking intention is decoded by kernel ridge regression (KRR) model to overcome the limitation of classification approach. In addition, the combination of three different features is employed to enhance the decoding performance. The decoding performances are evaluated by the correlation coefficient (r-value) and the normalized root-mean square error (NRMSE). Keywords-Brain-computer interface (BCI), Kernel ridge regression model (KRR), Electroencephalography (EEG)I.

EEG–EMG coupling as a hybrid method for steering detection in car driving settings

“…In particular, these latter studies show the possibility of using electroencephalography (EEG) to decode the driver’s cognitive processes in simulated and real car scenarios with the ultimate goal of predicting the upcoming action. Although the success in the classification of salient driving events such as braking (Haufe et al 2011 , 2014 ; Kim et al 2014 , 2015 ; Hernández et al 2018 ; Wang et al 2018 ; Teng et al 2018 ; Lin et al 2018 ; Nguyen and Chung 2019 ) and steering (Gheorghe et al 2013 ) actions, the level of accuracy is still moderate. Most importantly, the detected neurophysiological features are elicited just around a few milliseconds before the upcoming driving event, making it difficult to implement electronic assisting devices.…”

Emergency Braking Evoked Brain Activities during Distracted Driving

“…Currently, the research on EEG-based emergency braking intention detection and recognition mainly focuses on EEG feature extraction and classification algorithms. In terms of feature extraction, some researchers select a particular band or combine multiple bands of EEG signals [ 18 , 19 , 20 ] or select event-related potential (ERP), readiness potential (RP), and event-related desynchronization (ERD) features of EEG signals [ 15 , 21 , 22 ], and others choose neural correlation features of the brain [ 1 , 23 ]. In terms of classification methods, some researchers use traditional machine learning algorithms [ 18 , 19 , 20 , 21 , 22 ], such as linear discriminant analysis (LDA) and support vector machines (SVM) classification algorithms, which are widely used in offline and online EEG classification, especially online EEG classification [ 24 ], while others adopt deep learning [ 25 , 26 ] or combine machine learning with deep learning [ 27 , 28 ], all of which achieve better results.…”