EEG-based estimation of mental fatigue: convergent evidence for a three-state model

  • Authors:

  • Leonard J. Trejo;Kevin Knuth;Raquel Prado;Roman Rosipal;Karla Kubitz;Rebekah Kochavi;Bryan Matthews;Yuzheng Zhang

  • Affiliations:

  • Quantum Applied Science and Research, Palo Alto, CA;Department of Physics, University at Albany, Albany, NY;Department of Applied Mathematics and Statistics, University of California, Santa Cruz, CA;Austrian Research Institute for Artificial Intelligence, Vienna, Austria;Dept. of Kinesiology, Towson University, Towson, MD;QSS Group, Inc., Moffett Field, CA;Mission Critical Technologies, Inc., Moffett Field, CA;Department of Applied Mathematics and Statistics, University of California, Santa Cruz, CA

  • Venue:
  • FAC'07 Proceedings of the 3rd international conference on Foundations of augmented cognition
  • Year:
  • 2007

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Abstract

Two new computational models show that the EEG distinguishes three distinct mental states ranging from alert to fatigue. State 1 indicates heightened alertness and is frequently present during the first few minutes of time on task. State 2 indicates normal alertness, often following and lasting longer than State 1. State 3 indicates fatigue, usually following State 2, but sometimes alternating with State 1 and State 2. Thirty-channel EEGs were recorded from 16 subjects who performed up to 180 min of nonstop computer-based mental arithmetic. Alert or fatigued states were independently confirmed with measures of subjects performance and pre- or post-task mood. We found convergent evidence for a three-state model of fatigue using Bayesian analysis of two different types of EEG features, both computed for single 13-s EEG epochs: 1) kernel partial least squares scores representing composite multichannel power spectra; 2) amplitude and frequency parameters of multiple single-channel autoregressive models.