Measures and models for predicting cognitive fatigue

Leonard J. Trejo; Rebekah Kochavi; Karla Kubitz; Leslie D. Montgomery; Roman Rosipal; Bryan Matthews

doi:10.1117/12.604286

23 May 2005 Measures and models for predicting cognitive fatigue

Leonard J. Trejo, Rebekah Kochavi, Karla Kubitz, Leslie D. Montgomery, Roman Rosipal, Bryan Matthews

Proceedings Volume 5797, Biomonitoring for Physiological and Cognitive Performance during Military Operations; (2005) https://doi.org/10.1117/12.604286
Event: Defense and Security, 2005, Orlando, Florida, United States

Abstract

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.

Citation Download Citation

Leonard J. Trejo, Rebekah Kochavi, Karla Kubitz, Leslie D. Montgomery, Roman Rosipal, and Bryan Matthews "Measures and models for predicting cognitive fatigue", Proc. SPIE 5797, Biomonitoring for Physiological and Cognitive Performance during Military Operations, (23 May 2005); https://doi.org/10.1117/12.604286

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