Kinesthesia in a sustained-attention driving task

Abstract

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.

Brain Correlates of Lane Changing Reaction Time in Simulated Driving

“…Concluding, the current findings complement recent studies that have identified correlates of other cognitive processes in realistic driving, including drowsiness [6], [21], [22], [23], emergency braking [9], [24], error-awareness [25], anticipation of self-motivated steering [8] and braking actions [7], as well as visual attention [26]. We purport that future driving assistive systems can exploit information derived from these signals -decoded through a brain-machine interface system-, in combination with information from in-car sensors to tailor the support they provide both to the perceived conditions of the environment as well as the internal state of the driver [27].…”

“…after 400 ms). A decrease in the beta power (20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30)(31)(32)(33)(34)(35) is also clear in these two electrodes, appearing at about 300 ms, continuing during lane change behavior. In addition, a late increase at about 500 ms can be observed in the low frequency (1-4 Hz) activity in FCz.…”

The PREP pipeline: standardized preprocessing for large-scale EEG analysis

“…Another collection (N32) uses a 33-channel Neuroscan and consists of 40 subjects in 80 sessions performing a lane-keeping task with and without a motion platform. This collection was contributed from an extensive archive of driving studies performed at the National Chiao Tung University by C-T Lin and his collaborators (Chuang et al, 2012, 2014a,b). Statistics in the summary figures are also included for a task load study (N40) during shooting performed by Kerick and collaborators at the Army Research Laboratory using a 40-channel Neuroscan headset (Kerick et al, 2007).…”

EEG-Based Brain-Computer Interfaces: A Novel Neurotechnology and Computational Intelligence Method

“…Here, we applied GC to independent EEG processes that were collected from a simulated driving experiment in which participants performed a sustained-attention driving task [53]. The asymmetric ratio of the causal flow ( Fig.…”