Hybrid EEG-EMG system to detect steering actions in car driving settings

Abstract

Understanding mental processes in complex human behaviour is a key issue in the context of driving, representing a milestone for developing user-centred assistive driving devices. Here we propose a hybrid method based on electroencephalographic (EEG) and electromyographic (EMG) signatures to distinguish left from right steering in driving scenarios. Twenty-four participants took part in the experiment consisting of recordings 128-channel EEG as well as 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 by means of a 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 left from right steering with an earlier dynamic with respect 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 are a proof of concept of how it is possible to complement different physiological signals to control the level of assistance needed by the driver.