The automotive industry is undergoing a significant transformation, driven by advancements in autonomous driving, sophisticated human-machine interfaces (HMIs), and an increasing focus on occupant well-being. Central to this evolution is the growing integration of electrophysiological (ExG) biosignals into both research and development (R&D) and commercial applications. ExG signals, encompassing electroencephalography (EEG), electrocardiography (ECG), and electromyography (EMG), provide direct physiological and cognitive insights into vehicle occupants, facilitating the development of more intuitive, safer, and personalized driving experiences.
Electroencephalography (EEG) for Cognitive State Monitoring
EEG, a measure of the brain’s electrical activity, offers significant utility in understanding and mitigating cognitive factors in driving. Driver fatigue and inattention contribute substantially to global road accidents (National Highway Traffic Safety Administration, 2025; National Sleep Foundation, 2024). Early and foundational research, spanning the late 20th and early 21st centuries, established the relationship between changes in EEG frequency bands (e.g., increased theta and alpha power, decreased beta power) and increasing levels of drowsiness and reduced alertness in driving scenarios. Comprehensive reviews of this work highlight the consistent physiological markers of fatigue derived from EEG (Lal & Craig, 2001; Stancin et al., 2021). EEG enables the detection of subtle alterations in brainwave patterns indicative of drowsiness, often preceding overt behavioral manifestations.
Beyond fatigue detection, EEG facilitates the assessment of cognitive workload and attentional allocation, critical parameters for effective HMI design in increasingly complex vehicular environments. For instance, EEG has been employed to quantify the cognitive load experienced by drivers in partially automated vehicles, comparing it to manual driving scenarios (Figalová et al., 2024). The capacity for real-time cognitive state monitoring allows for the development of adaptive vehicle systems that can adjust warnings, information presentation, or even automation levels in accordance with the driver’s cognitive readiness.
Electromyography (EMG) for Musculoskeletal Analysis and Control
Electromyography (EMG) involves recording the electrical activity generated by muscle contractions. In the automotive context, EMG provides insights into driver comfort, physical workload, and offers a potential pathway for novel control interfaces. Research has explored the application of surface EMG (sEMG) to assess muscle activity related to driver posture and comfort, particularly in response to vehicle dynamics such as lateral acceleration during cornering. This information can inform ergonomic seat design and suspension tuning, aiming to minimize driver fatigue and discomfort during extended journeys (Katsis et al., 2004).
Additionally, EMG is under investigation for direct vehicle control in specialized applications. By interpreting muscle signals, for example, from the forearm, researchers are exploring alternative input methods that could augment or replace conventional steering mechanisms (Wang et al., 2021, Nacpil et al., 2018). This area of research underscores EMG’s potential to contribute to more inclusive and adaptable vehicle designs.
Electrocardiography (ECG) for Physiological State and Health Monitoring
Electrocardiography (ECG) measures the heart’s electrical activity, yielding comprehensive information regarding a driver’s physiological state, including stress, emotional arousal, and cardiovascular health. Acute medical events, particularly cardiovascular incidents, represent a significant concern for road safety. Continuous, unobtrusive ECG monitoring within vehicles holds promise for detecting early indicators of cardiac distress, potentially enabling timely intervention and improved outcomes (Koh & Lee, 2019).
Current research focuses on integrating ECG sensors into various vehicle components, such as steering wheels (Koh & Lee, 2019) or car seats (Sakai et al., 2013), to enable non-contact or minimally intrusive measurements. These systems aim to derive heart rate (HR) and heart rate variability (HRV) metrics, which are established indicators of stress, cognitive load, and autonomic nervous system activity. By analyzing these physiological responses, automotive engineers can design systems that mitigate driver stress in challenging scenarios or even predict and avert situations that might induce anxiety or panic. Beyond safety, ECG data can also contribute to personalized in-cabin experiences, adapting environmental parameters such as lighting or temperature based on the driver’s physiological state.
Conclusion
The integration of ExG biosignals represents a transformative advancement in automotive R&D. By providing direct, objective measures of human physiological and cognitive states, EEG, EMG, and ECG enable a more profound understanding of human-vehicle interaction. This understanding is critical for developing advanced driver-assistance systems, intuitive HMIs, and features that prioritize occupant safety, comfort, and well-being. As the automotive landscape continues its trajectory toward increasing automation and connectivity, the role of biosignal integration will become increasingly prominent, fostering the development of a new generation of human-centric vehicles.
For researchers and developers requiring a versatile and robust platform for ExG biosignal acquisition in automotive applications, the Mentalab Explore Pro system offers a comprehensive solution. With up to 32 channels and research grade ExG data quality, it is ideally suited for demanding research environments. Its compact design, wireless & wired streaming capabilities, open software API, and compatibility with various electrode types, including dry electrodes, render it highly adaptable for both laboratory and in-vehicle studies, providing the precision and flexibility necessary to advance the field of automotive biosignal research.
References
- National Highway Traffic Safety Administration. (2025). Traffic Fatalities Decreased in the First Quarter of 2025. Retrieved from https://www.nhtsa.gov/press-releases/traffic-fatalities-decreased-first-quarter-2025
- National Sleep Foundation. (2024). 2024 Drowsy Driving Survey. Retrieved from https://www.thensf.org/wp-content/uploads/2024/10/Drowsy-Driving-Survey_2024.pdf
- Lal, S. K., & Craig, A. (2001). A critical review of the psychophysiology of driver fatigue. Biological Psychology, 55(3), 173-194.
- Stancin, I., Cifrek, M., & Jovic, A. (2021). A review of EEG signal features and their application in driver drowsiness detection systems. Sensors, 21(11), 3786.
- Figalová, N., Bieg, H. J., Reiser, J. E., Liu, Y. C., Baumann, M., Chuang, L., & Pollatos, O. (2024). From driver to supervisor: Comparing cognitive load and EEG-based attentional resource allocation across automation levels. International Journal of Human-Computer Studies, 182, 103169.
- Katsis, C. D., Ntouvas, N. E., Bafas, C. G., & Fotiadis, D. I. (2004, February). Assessment of muscle fatigue during driving using surface EMG. In Proceedings of the IASTED international conference on biomedical engineering (Vol. 262).
- Wang, Z., Suga, S., Nacpil, E. J. C., Yan, Z., & Nakano, K. (2020). Adaptive driver-automation shared steering control via forearm surface electromyography measurement. IEEE Sensors Journal, 21(4), 5444-5453.
- Nacpil, E. J., Zheng, R., Kaizuka, T., & Nakano, K. (2017, June). Implementation of a sEMG-machine interface for steering a virtual car in a driving simulator. In International Conference on Applied Human Factors and Ergonomics (pp. 274-282). Cham: Springer International Publishing.
- Koh, D. W., & Lee, S. G. (2019). An evaluation method of safe driving for senior adults using ECG signals. Sensors, 19(12), 2828.
- Sakai, K., Yanai, K., Okada, S., & Nishii, K. (2013). Design of seat mounted ECG sensor system for vehicle application. SAE International journal of passenger cars-electronic and electrical systems, 6(2013-01-1339), 342-348.
