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  • 标题:EEG-based Estimation of Cognitive Fatigue
  • 本地全文:下载
  • 作者:Polikanova I.S. ; Isaev A.V. ; Leonov S.V.
  • 期刊名称:Natsionalny Psikhologichesky Zhurnal = National Psychological Journal
  • 印刷版ISSN:2079-6617
  • 出版年度:2019
  • 卷号:12
  • 期号:4
  • 页码:53-63
  • DOI:10.11621/npj.2019.0406
  • 出版社:Editions Soliton Ltd
  • 摘要:We measured multichannel EEG spectra during a continuous mental arithmetic task and created statistical learning models of cognitive fatigue for single subjects. Sixteen subjects (4 F, 18-38 y) viewed 4-digit problems on a computer, solved the problems, and pressed keys to respond (inter-trial interval = 1 s). Subjects performed until either they felt exhausted or three hours had elapsed. Pre- and post-task measures of mood (Activation Deactivation Adjective Checklist, Visual Analogue Mood Scale) confirmed that fatigue increased and energy decreased over time. We examined response times (RT); amplitudes of ERP components N1, P2, and P300, readiness potentials; and power of frontal theta and parietal alpha rhythms for change as a function of time. Mean RT rose from 6.7 s to 7.9 s over time. After controlling for or rejecting sources of artifact such as EOG, EMG, motion, bad electrodes, and electrical interference, we found that frontal theta power rose by 29% and alpha power rose by 44% over the course of the task. We used 30-channel EEG frequency spectra to model the effects of time in single subjects using a kernel partial least squares (KPLS) classifier. We classified 13-s long EEG segments as being from the first or last 15 minutes of the task, using random sub-samples of each class. Test set accuracies ranged from 91% to 100% correct. We conclude that a KPLS classifier of multichannel spectral measures provides a highly accurate model of EEG-fatigue relationships and is suitable for on-line applications to neurological monitoring.
  • 关键词:EEG; cognitive; fatigue; estimation; monitoring
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