We reported EEG activity in response to kinesthetic inputs in different kinds of driving events: deceleration, acceleration and deviation. This innovative study was conducted in a VR-environment on a 6 DOF motion platform. Our results show that EEG responses to kinesthetic stimulus during driving induce: (1) Mu blocking in the somatomotor components.
(2) Negative ERP in the central midline component. The Mu blocking appeared to be induced by two kinds of stimuli. When the subjects received kinesthetic inputs, their alpha activities accounted for by the left and right mu components were blocked. After a short period, the subjects adjusted his/her body to balance him/herself, this induced Mu blocking again.
Negative ERP was found in the central midline component following kinesthetic stimulus onsets. These results demonstrate that multiple cortical EEG sources response to the driving events differentially in dynamic and static environments. We showed that a static driving simulator might not be able to induce some cognitive responses that might be well involved in real driving. Thus a driving simulator with motion platform is very crucial to study brain activity involving in real driving. We also confirmed that the absence of driving motion will increase the reaction time to external perturbations by studying the response time in deviating events. Thus a driving simulator with motion platform is a necessary solution either in simulating real driving or investigating cognitive state during driving.
Traditionally, EEG alpha band was used as an indicator of drowsiness estimation during driving [68][72]. In our study, we observed that alpha band variations occurred in many components (Mu, parietal, occipital) during driving, especially when the vehicle is moving.
Alpha power has been reported to index the level of drowsiness in attention-sustained experiments in a laboratory setting. The alpha power variation induced by motion of the
vehicle might interfere with the estimation of driving cognitive state, so those estimations which were based on alpha band may not be always reliable.
In the future, we will apply our finding on previous studied driving drowsiness estimator [68][69], in order to improve the performance of estimation. This thesis is a beginning of building up a foundation for studying EEG in a continuous driving experiment on a 6-DOF motion platform. We will further investigate more detailed about the driving events, for instance, to study subjects’ cognitive stage under deviation event without steering the wheel.
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