| Summary: | 碩士 === 國立交通大學 === 影像與生醫光電研究所 === 102 === How to deal with the upcoming emergency situations is a key to avoid car accidents. Previous study (Chen, 2013) used brain imaging to reveal that the efficiency of inhibition function is responsible for copying such situations. However, other factors, such as stress, on driving inhibition are still unknown. Hence, in this study, we aim to get an insight into brain activities of emergency management in stress conditions.
To investigate driver’s brain responses to inhibition function, a modified stop-signal driving task was implemented in a virtual-reality driving environment. The electroencephalography (EEG) was recorded from 16 subjects as they performed the experimental tasks under normal (without time pressure) and stress (with time pressure) conditions. Given a fixed road distance, each subject was instructed to arrive at the finishing line within a limited time under the stress condition. In signal processing, independent component analysis (ICA) and event-related spectral perturbation (ERSP) analysis were applied to investigate the spectral dynamics of independent brain processes.
The behavioral results showed that the stop-signal reaction time (SSRT) was shorter under the stress condition than that under the normal condition. This result indicated that the stress could help to improve the efficiency of inhibition ability. The ERSP results showed that the augmentation of delta (1-3 Hz) and theta (4-7 Hz) powers in frontal and central areas are related to the inhibition mechanism. There is no statistically significant difference between two conditions. However, beta (13-30 Hz) and gamma (30-50 Hz) powers in frontal and central areas increased only in the stress condition. The beta and gamma powers of the central area under the stress condition were significantly higher than those under the normal condition. Because the gamma band is thought to reflect the top down modulation, the time pressure could possibly improve the driving inhibition efficiency by the proactive control which prepares to stop before the signal onset.
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