Literature DB >> 24444995

Kinesthesia in a sustained-attention driving task.

Chun-Hsiang Chuang1, Li-Wei Ko2, Tzyy-Ping Jung3, Chin-Teng Lin4.   

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.
Copyright © 2014 Elsevier Inc. All rights reserved.

Entities:  

Keywords:  Driving; EEG; Independent component analysis; Kinesthesia; Time–frequency analysis

Mesh:

Year:  2014        PMID: 24444995     DOI: 10.1016/j.neuroimage.2014.01.015

Source DB:  PubMed          Journal:  Neuroimage        ISSN: 1053-8119            Impact factor:   6.556


  11 in total

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3.  BLINKER: Automated Extraction of Ocular Indices from EEG Enabling Large-Scale Analysis.

Authors:  Kelly Kleifges; Nima Bigdely-Shamlo; Scott E Kerick; Kay A Robbins
Journal:  Front Neurosci       Date:  2017-02-03       Impact factor: 4.677

4.  Time-Frequency Analysis of Mu Rhythm Activity during Picture and Video Action Naming Tasks.

Authors:  Megan E Cuellar; Christina M Del Toro
Journal:  Brain Sci       Date:  2017-09-06

5.  Multi-channel EEG recordings during a sustained-attention driving task.

Authors:  Zehong Cao; Chun-Hsiang Chuang; Jung-Kai King; Chin-Teng Lin
Journal:  Sci Data       Date:  2019-04-05       Impact factor: 6.444

6.  EEG and Eye Tracking Demonstrate Vigilance Enhancement with Challenge Integration.

Authors:  Indu P Bodala; Junhua Li; Nitish V Thakor; Hasan Al-Nashash
Journal:  Front Hum Neurosci       Date:  2016-06-07       Impact factor: 3.169

7.  Identifying changes in EEG information transfer during drowsy driving by transfer entropy.

Authors:  Chih-Sheng Huang; Nikhil R Pal; Chun-Hsiang Chuang; Chin-Teng Lin
Journal:  Front Hum Neurosci       Date:  2015-10-23       Impact factor: 3.169

8.  Mind-Wandering Tends to Occur under Low Perceptual Demands during Driving.

Authors:  Chin-Teng Lin; Chun-Hsiang Chuang; Scott Kerick; Tim Mullen; Tzyy-Ping Jung; Li-Wei Ko; Shi-An Chen; Jung-Tai King; Kaleb McDowell
Journal:  Sci Rep       Date:  2016-02-17       Impact factor: 4.379

9.  Brain Electrodynamic and Hemodynamic Signatures Against Fatigue During Driving.

Authors:  Chun-Hsiang Chuang; Zehong Cao; Jung-Tai King; Bing-Syun Wu; Yu-Kai Wang; Chin-Teng Lin
Journal:  Front Neurosci       Date:  2018-03-27       Impact factor: 4.677

10.  Brain Network Changes in Fatigued Drivers: A Longitudinal Study in a Real-World Environment Based on the Effective Connectivity Analysis and Actigraphy Data.

Authors:  André Fonseca; Scott Kerick; Jung-Tai King; Chin-Teng Lin; Tzyy-Ping Jung
Journal:  Front Hum Neurosci       Date:  2018-11-12       Impact factor: 3.169

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