Robust anticipation of continuous steering actions from electroencephalographic data during simulated driving

• Published in: Scientific reports 2021-12, Vol.11 (1), p.23383-23383, Article 23383
• Main Authors: Di Liberto, Giovanni M., Barsotti, Michele, Vecchiato, Giovanni, Ambeck-Madsen, Jonas, Del Vecchio, Maria, Avanzini, Pietro, Ascari, Luca
• Format: Article
• Language: English
• Subjects: 631/1647/1453/1450; 631/378/2632/1663; 631/378/2649/2150; Anticipation, Psychological - physiology; Automobile Driving - psychology; Canonical Correlation Analysis; Cerebral Cortex - physiology; Cognitive ability; Computer Simulation; Correlation analysis; EEG; Electroencephalography; Humanities and Social Sciences; Humans; Linear Models; multidisciplinary; Neural Networks, Computer; Psychomotor Performance - physiology; Science; Science (multidisciplinary)
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-021-02750-w

Driving a car requires high cognitive demands, from sustained attention to perception and action planning. Recent research investigated the neural processes reflecting the planning of driving actions, aiming to better understand the factors leading to driving errors and to devise methodologies to anticipate and prevent such errors by monitoring the driver’s cognitive state and intention. While such anticipation was shown for discrete driving actions, such as emergency braking, there is no evidence for robust neural signatures of continuous action planning. This study aims to fill this gap by investigating continuous steering actions during a driving task in a car simulator with multimodal recordings of behavioural and electroencephalography (EEG) signals. System identification is used to assess whether robust neurophysiological signatures emerge before steering actions. Linear decoding models are then used to determine whether such cortical signals can predict continuous steering actions with progressively longer anticipation. Results point to significant EEG signatures of continuous action planning. Such neural signals show consistent dynamics across participants for anticipations up to 1 s, while individual-subject neural activity could reliably decode steering actions and predict future actions for anticipations up to 1.8 s. Finally, we use canonical correlation analysis to attempt disentangling brain and non-brain contributors to the EEG-based decoding. Our results suggest that low-frequency cortical dynamics are involved in the planning of steering actions and that EEG is sensitive to that neural activity. As a result, we propose a framework to investigate anticipatory neural activity in realistic continuous motor tasks.