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

Abstract

Understanding mental processes in complex human behavior is a key issue in driving, representing a milestone for developing user-centered assistive driving devices. Here, we propose a hybrid method based on electroencephalographic (EEG) and electromyographic (EMG) signatures to distinguish left and right steering in driving scenarios. Twenty-four participants took part in the experiment consisting of recordings of 128-channel EEG and EMG activity from deltoids and forearm extensors in non-ecological and ecological steering tasks. Specifically, we identified the EEG mu rhythm modulation correlates with motor preparation of self-paced steering actions in the non-ecological task, while the concurrent EMG activity of the left (right) deltoids correlates with right (left) steering. Consequently, we exploited the mu rhythm de-synchronization resulting from the non-ecological task to detect the steering side using cross-correlation analysis with the ecological EMG signals. Results returned significant cross-correlation values showing the coupling between the non-ecological EEG feature and the muscular activity collected in ecological driving conditions. Moreover, such cross-correlation patterns discriminate the steering side earlier relative to the single EMG signal. This hybrid system overcomes the limitation of the EEG signals collected in ecological settings such as low reliability, accuracy, and adaptability, thus adding to the EMG the characteristic predictive power of the cerebral data. These results prove how it is possible to complement different physiological signals to control the level of assistance needed by the driver.

Investigating multilevel cognitive processing within error-free and error-prone feedback conditions in executed and observed car driving

“…Indeed, an abundance of non-invasive brain research investigated numerous aspects of vehicle operation to the present day. Various groups reported neural correlates to or successful classification of, e.g., steering motion ( Simpson and Rafferty, 2022 ; Vecchiato et al, 2022 ), the driver’s mental state ( ElSherif et al, 2020 ), or the influences of distraction and fatigue ( Schneiders et al, 2020 ; Zeng et al, 2022 ) on the driving performance utilizing the electroencephalogram (EEG). Additionally, insights on discrete error-related brain activity occurring during the operation of a vehicle induced by wrong turns ( Zhang et al, 2015 ) or steering errors caused by the interface ( Garcia et al, 2017 ) emerged specifically in the context of brain-computer interfaces (BCIs).…”