Analysis of EEG and behavioral changes induced by arousing feedback and its application for drowsy driving
|關鍵字:||腦波;疲勞;聲音警示;打瞌睡;行車安全;獨立成分分析法;Electroencephalography (EEG);Fatigue;Auditory feedback;Drowsiness;Brain dynamics;Driving safety;Independent Component Analysis (ICA)|
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.
|Appears in Collections:||Thesis|