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Citation |
Sabahi Y, Setarehdan SK, Nasrabadi AM. Cogn. Neurodyn. 2022; 16(2): 353-363. |
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Copyright |
(Copyright © 2022, Holtzbrinck Springer Nature Publishing Group) |
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DOI |
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PMID |
35401862 |
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PMCID |
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Abstract |
Describing a neural activity map based on observed responses in emergency situations, especially during driving, is a challenging issue that would help design driver-assistant devices and a better understanding of the brain. This study aimed to investigate which regions were involved during emergency braking, measuring the interactions and strength of the connections and describing coupling among these brain regions by dynamic causal modeling (DCM) parameters that we extracted from event-related potential signals, which were then estimated based on emergency braking data with visual stimulation. The data were reanalyzed from a simulator study, which was designed to create emergency situations for participants during a simple driving task. The experimental protocol includes driving a virtual reality car, and the subjects were exposed to emergency situations in a simulator system, while electroencephalogram, electro-oculogram, and electromyogram signals were recorded. In this research, locations of active brain regions in montreal neurological institute coordinates from event-related responses were identified using multiple sparse priors method, in which sensor space was allocated to resource space. Source localization results revealed nine active regions. After applying DCM on data, a proposed model during emergency braking for all people was obtained. The braking response time was defined based on the first noticeable (above noise-level) braking pedal deflection after an induced braking maneuver. The result revealed a significant difference in response time between subjects who have the lateral connection between visual cortex, visual processing, and detecting objects areas have shorter response time (p-value = 0.05) than the subjects who do not have such connections. Language: en |
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Keywords |
Driving simulation; Dynamic causal modelling (DCM); Effective connectivity; Emergency braking; Event related potential signals (ERP) |