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

Vecchiato, Giovanni; Del Vecchio, Maria; Ambeck-Madsen, Jonas; Ascari, Luca; Avanzini, Pietro · 2022 · Crossref

DOI: 10.1007/s11571-021-09776-w

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Summary

This study addresses the challenge of detecting driver steering intentions in real-world conditions, where electroencephalography (EEG) signals often suffer from low reliability and high noise. While EEG offers predictive power by capturing motor preparation before movement onset, surface electromyography (EMG) provides robust, real-time data on muscle activation but lacks early predictive capability. The authors propose a hybrid method that leverages EEG features identified in controlled, non-ecological settings to enhance the detection of steering direction using EMG data collected during ecological driving simulations. The experiment involved 24 participants who completed two tasks: a non-ecological task involving self-paced steering wheel movements in response to visual cues, and an ecological task using a driving simulator. During both tasks, 128-channel EEG and EMG signals from the deltoids and forearm extensors were recorded. In the non-ecological task, independent component analysis (ICA) and clustering identified specific EEG independent components related to steering. Time-frequency analysis revealed that the mu rhythm desynchronized approximately 1.5 seconds before the onset of left steering, serving as a neural marker for motor preparation. This EEG feature was then used as a template to analyze EMG data from the ecological driving task. The results demonstrated significant coupling between the non-ecological EEG mu rhythm desynchronization and the ecological EMG activity. Specifically, cross-correlation analysis showed that the EEG template derived from the controlled task significantly correlated with deltoid muscle activity during natural driving. Crucially, this hybrid approach allowed for the discrimination of left versus right steering earlier than using EMG signals alone. The cross-correlation patterns detected steering intent prior to the onset of significant muscular activation, thereby extending the prediction window. The method also proved specific to steering actions, as no significant correlations were found when comparing the EEG template against non-steering or shuffled EMG data. The significance of this work lies in its demonstration that physiological signals can be complemented to overcome the limitations of unimodal approaches in complex environments. By identifying reliable neural correlates in controlled settings and applying them to noisy, real-world data, the hybrid system enhances the accuracy and timing of steering detection. This approach offers a viable pathway for developing user-centered assistive driving devices that can anticipate driver actions with greater lead time, potentially improving safety and the responsiveness of automated vehicle systems.

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• steering pattern

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• Empirical Findings: physiological data
• Methodological Resource: tool software
• Theoretical Contribution: computational model