Trejo L.J., Knuth K., Prado R., Rosipal R., Kubitz K.,Kochavi R., Matthews B., Zhang Y.

EEG-based Estimation of Mental Fatigue: Convergent Evidence for a Three-State Model

Proceedings of 13th International Conference on Human Computer Interaction, Schmorrow D.D., Reeves L.M.(eds.): Augmented Cognition, HCII 2007, Beijing, China, Springer, pp. 201-211, 2007.

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.