標題: | Kinesthesia in a sustained-attention driving task |
作者: | Chuang, Chun-Hsiang Ko, Li-Wei Jung, Tzyy-Ping Lin, Chin-Teng 生物科技學系 電機工程學系 腦科學研究中心 Department of Biological Science and Technology Department of Electrical and Computer Engineering Brain Research Center |
關鍵字: | EEG;Kinesthesia;Driving;Independent component analysis;Time-frequency analysis |
公開日期: | 1-May-2014 |
摘要: | This study investigated the effects of kinesthetic stimuli on brain activities during a sustained-attention task in an immersive driving simulator. Tonic and phasic brain responses on multiple timescales were analyzed using time-frequency analysis of electroencephalographic (EEG) sources identified by independent component analysis (ICA). Sorting EEG spectra with respect to reaction times (RT) to randomly introduced lane-departure events revealed distinct effects of kinesthetic stimuli on the brain under different performance levels. Experimental results indicated that EEG spectral dynamics highly correlated with performance lapses when driving involved kinesthetic feedback. Furthermore, in the realistic environment involving both visual and kinesthetic feedback, a transitive relationship of power spectra between optimal-, suboptimal-, and poor-performance groups was found predominately across most of the independent components. In contrast to the static environment with visual input only, kinesthetic feedback reduced theta-power augmentation in the central and frontal components when preparing for action and error monitoring, while strengthening alpha suppression in the central component while steering the wheel. In terms of behavior, subjects tended to have a short response time to process unexpected events with the assistance of kinesthesia, yet only when their performance was optimal. Decrease in attentional demand, facilitated by kinesthetic feedback, eventually significantly increased the reaction time in the suboptimal-performance state. Neurophysiological evidence of mutual relationships between behavioral performance and neurocognition in complex task paradigms and experimental environments, presented in this study, might elucidate our understanding of distributed brain dynamics, supporting natural human cognition and complex coordinated, multi-joint naturalistic behavior, and lead to improved understanding of brain-behavior relations in operating environments. (C) 2014 Elsevier Inc. All rights reserved. |
URI: | http://dx.doi.org/10.1016/j.neuroimage.2014.01.015 http://hdl.handle.net/11536/147736 |
ISSN: | 1053-8119 |
DOI: | 10.1016/j.neuroimage.2014.01.015 |
期刊: | NEUROIMAGE |
Volume: | 91 |
起始頁: | 187 |
結束頁: | 202 |
Appears in Collections: | Articles |