标题: | 警示回馈对于脑波及行为上的影响及其于疲劳驾车之应用 Analysis of EEG and behavioral changes induced by arousing feedback and its application for drowsy driving |
作者: | 黄冠智 Huang, Kuan-Chih 林进灯 Lin, Chin-Teng 电控工程研究所 |
关键字: | 脑波;疲劳;声音警示;打瞌睡;行车安全;独立成分分析法;Electroencephalography (EEG);Fatigue;Auditory feedback;Drowsiness;Brain dynamics;Driving safety;Independent Component Analysis (ICA) |
公开日期: | 2015 |
摘要: | 过去许多研究提出,当人们处于疲劳的状态同时在精神上产生负面影响,伴随着生理认知行为上发生错误,更可能是造成重大意外的关键因素,然而利用刺激提醒并结合行为及其伴随的脑波变化的研究仍相当不足。本研究将有系统探讨疲劳驾车在认知行为和脑波上的变化,并提出如何应用于真实生活中。本研究在虚拟实境的环境,模拟真实长时间驾车的场景,使受测者在虚拟场景中进行长时间驾车并维持注意力的实验,目的在于探讨在需要长时间维持注意的情况下,产生疲劳所造成的认知错误,并利用声音讯号的刺激提醒之后,观察脑波以及行为表现上的变化。本研究更进一步发展一个“基于脑波特征的疲劳侦测及减缓系统”透过脑波即时监测以防止疲劳所造成的认知错误。 在实验中,受测者的脑波长期与暂态的动态变化会透过独立讯号分析、时域频域转换等方法进行分析比较。研究结果归纳出当受测者处于疲劳造成的打瞌睡状态,枕叶脑区皮质的脑波在α和θ频带的能量会产生上升变化,行为的表现会下降。疲劳受测者在接收警示提醒之后,行为的表现会提升,同时枕叶脑波活动在α和θ频段的上升的能量会下降接近清醒的状态。从脑波的比较,警示的效果可以持续30秒或是更久的时间。由此,我们可以藉由脑波萃取出个人的认知清醒状况资讯,更进一步的,将其应用在即时判断监测认知上的变化。然而,本研究也提出,警示的效果可能会随着实施次数的增加而减少,故本研究提出藉由机器学习的技术即时判断警示效果,给予不同的刺激变化,来维持提醒受测者的警示效果。 本研究呈现的结果明确的指出藉由脑波的变化,可以判断认知相关的资讯,更进一步利用“基于脑波监测的疲劳预测及减缓系统”来防止认知错误所可能引发的灾祸事件。 Research has indicated that fatigue is a critical factor in cognitive and behavioral lapses because it negatively affects an individual’s internal state, which is then manifested physiologically. However, to our best knowledge, no study has assessed the EEG correlated of improved task performance following arousing signals. This study investigates brain dynamics and behavioral changes in response to arousing auditory signals presented to individuals experiencing momentary cognitive lapses, due to fatigue, during a sustained-attention task. Electroencephalographic (EEG) and behavioral data were simultaneously collected during virtual-reality (VR) based driving experiments, in which subjects were instructed to maintain their cruising position and compensate for randomly induced lane deviations using the steering wheel. This study further demonstrates the feasibility of an on-line closed-loop EEG-based fatigue prediction and mitigation system that detects physiological change and can thereby prevent fatigue-related cognitive lapses. Moreover, this work compares the efficacy of fatigue prediction and mitigation between the EEG-based and a non-EEG-based method. Each participant was instructed to maintain his/her cruising position at all times during the experiment. Each participant’s EEG signal was monitored continuously and a warning was delivered in real time to participants once the EEG signature of fatigue was detected. 30-channel EEG data were analyzed by independent component analysis and the short-time Fourier transform. Across subjects and sessions, intermittent performance during drowsiness was accompanied by characteristic spectral augmentation or suppression in the alpha- and theta-band spectra of a occipital component, corresponding to brief periods of normal (wakeful) and hypnagogic (sleeping) awareness and behavior. The improved behavioral performance was accompanied by concurrent spectral suppression in the theta- and alpha-bands of the occipital component. The effects of auditory feedback on spectral changes lasted 30 s or longer. The results of this study demonstrate the amount of cognitive state information that can be extracted from noninvasively recorded EEG data and the feasibility of online assessment and rectification of brain networks exhibiting characteristic dynamic patterns in response to momentary cognitive challenges. However, study results also showed reduced feedback efficacy (i.e., increased response times to lane deviations) accompanied by increased alpha-power due to the effects of habituation to repeat warnings. This study further proposes a feedback efficacy assessment system to accurately estimate the efficacy of arousing warning signals delivered to drowsy participants by monitoring the changes in their EEG power spectra immediately thereafter. The results of this study explore the amount of cognitive state information that can be extracted from noninvasively recorded EEG data and clearly demonstrate and validate the efficacy of this on-line closed-loop EEG-based fatigue prediction and mitigation mechanism to identify cognitive lapses that may lead to catastrophic incidents in countless operational environments. |
URI: | http://140.113.39.130/cdrfb3/record/nctu/#GT079612823 http://hdl.handle.net/11536/126609 |
显示于类别: | Thesis |