Monitoring and Predicting Driving Performance Using EEG Activity

Elsherif, Ahmed; Karaman, Ahmed; Ahmed, Omar; Magdy, Omar; Shouman, Reham; El-Noumier, Rita; Hamed, Ahmed M.; Eldawlatly, Hany; Eldawlatly, Seif

doi:10.1109/icces51560.2020.9334574

Cited by 4 publications

(2 citation statements)

References 23 publications

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“…To compare the results obtained with the proposed method, we referred to the following articles [57][58][59][60][61], which focused on the automatic detection of drowsiness and cognitive states for the driver of a vehicle. The comparative results are presented in Table 2.…”

Section: Solution For Viewing and Centralizing Online Data Received From Iot Devicesmentioning

confidence: 99%

Advanced e-Call Support Based on Non-Intrusive Driver Condition Monitoring for Connected and Autonomous Vehicles

Minea

Dumitrescu

Costea

2021

Sensors

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Background: The growth of the number of vehicles in traffic has led to an exponential increase in the number of road accidents with many negative consequences, such as loss of lives and pollution. Methods: This article focuses on using a new technology in automotive electronics by equipping a semi-autonomous vehicle with a complex sensor structure that is able to provide centralized information regarding the physiological signals (Electro encephalogram—EEG, electrocardiogram—ECG) of the driver/passengers and their location along with indoor temperature changes, employing the Internet of Things (IoT) technology. Thus, transforming the vehicle into a mobile sensor connected to the internet will help highlight and create a new perspective on the cognitive and physiological conditions of passengers, which is useful for specific applications, such as health management and a more effective intervention in case of road accidents. These sensor structures mounted in vehicles will allow for a higher detection rate of potential dangers in real time. The approach uses detection, recording, and transmission of relevant health information in the event of an incident as support for e-Call or other emergency services, including telemedicine. Results: The novelty of the research is based on the design of specialized non-invasive sensors for the acquisition of EEG and ECG signals installed in the headrest and backrest of car seats, on the algorithms used for data analysis and fusion, but also on the implementation of an IoT temperature measurement system in several points that simultaneously uses sensors based on MEMS technology. The solution can also be integrated with an e-Call system for telemedicine emergency assistance. Conclusion: The research presents both positive and negative results of field experiments, with possible further developments. In this context, the solution has been developed based on state-of-the-art technical devices, methods, and technologies for monitoring vital functions of the driver/passengers (degree of fatigue, cognitive state, heart rate, blood pressure). The purpose is to reduce the risk of accidents for semi-autonomous vehicles and to also monitor the condition of passengers in the case of autonomous vehicles for providing first aid in a timely manner. Reported abnormal values of vital parameters (critical situations) will allow interveneing in a timely manner, saving the patient’s life, with the support of the e-Call system.

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Section: Solution For Viewing and Centralizing Online Data Received From Iot Devicesmentioning

confidence: 99%

Advanced e-Call Support Based on Non-Intrusive Driver Condition Monitoring for Connected and Autonomous Vehicles

Minea

Dumitrescu

Costea

2021

Sensors

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“…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).…”

Section: Introductionmentioning

confidence: 99%

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

Pulferer,

Guan,

Müller-Putz

2024

Front. Hum. Neurosci.

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Accident analyses repeatedly reported the considerable contribution of run-off-road incidents to fatalities in road traffic, and despite considerable advances in assistive technologies to mitigate devastating consequences, little insight into the drivers’ brain response during such accident scenarios has been gained. While various literature documents neural correlates to steering motion, the driver’s mental state, and the impact of distraction and fatigue on driving performance, the cortical substrate of continuous deviations of a car from the road – i.e., how the brain represents a varying discrepancy between the intended and observed car position and subsequently assigns customized levels of corrective measures – remains unclear. Furthermore, the superposition of multiple subprocesses, such as visual and erroneous feedback processing, performance monitoring, or motor control, complicates a clear interpretation of engaged brain regions within car driving tasks. In the present study, we thus attempted to disentangle these subprocesses, employing passive and active steering conditions within both error-free and error-prone vehicle operation conditions. We recorded EEG signals of 26 participants in 13 sessions, simultaneously measuring pairs of Executors (actively steering) and Observers (strictly observing) during a car driving task. We observed common brain patterns in the Executors regardless of error-free or error-prone vehicle operation, albeit with a shift in spectral activity from motor beta to occipital alpha oscillations within erroneous conditions. Further, significant frontocentral differences between Observers and Executors, tracing back to the caudal anterior cingulate cortex, arose during active steering conditions, indicating increased levels of motor-behavioral cognitive control. Finally, we present regression results of both the steering signal and the car position, indicating that a regression of continuous deviations from the road utilizing the EEG might be feasible.

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EEG Monitoring in Driving Using Embedded Systems

Alves,

Matos

2024

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Monitoring and Predicting Driving Performance Using EEG Activity

Cited by 4 publications

References 23 publications

Advanced e-Call Support Based on Non-Intrusive Driver Condition Monitoring for Connected and Autonomous Vehicles

Advanced e-Call Support Based on Non-Intrusive Driver Condition Monitoring for Connected and Autonomous Vehicles

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

EEG Monitoring in Driving Using Embedded Systems

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