A kernel partial least squares classifier trained on EEG power spectral densities accurately classified alert versus fatigued states with 91% to 100% accuracy.
Observational (n=22)
No
Can EEG power spectral densities accurately classify and track mental fatigue?
EEG can accurately track the development of mental fatigue over time using 13-second segments, with a classifier achieving 91-100% accuracy.
Mental fatigue was associated with increased power in frontal theta (θ) and parietal alpha (α) EEG rhythms. A statistical classifier can use these effects to model EEG-fatigue relationships accurately. Participants (n = 22) solved math problems on a computer until either they felt exhausted or 3 h had elapsed. Pre- and post-task mood scales showed that fatigue increased and energy decreased. Mean response times rose from 6.7 s to 7.9 s but accuracy did not change significantly. Mean power spectral densities or PSDs of θ and α bands rose by 29% and 44%, respectively. A kernel partial least squares classifier trained to classify PSD coefficients (1 - 18 Hz) of single 13-s EEG segments from alert or fatigued task periods was 91% to 100% accurate. For EEG segments from other task periods, the classifier outputs tracked the time course of the development of mental fatigue. By this measure, most subjects became substantially fatigued after 60 min of task performance. However, the trajectories of individual classifier outputs showed that EEG signs of developing fatigue were present in all subjects after 15 - 30 minutes of task performance. The results show that EEG can track the development of mental fatigue over time with accurate updates on a time scale a short as 13 seconds. In addition, the results agree with the notion that growing mental fatigue produces a shift away from executive and attention networks to default mode and is accompanied by a shift in alpha frequency to the lower alpha band.
Trejo et al. (Thu,) conducted a observational in Mental Fatigue (n=22). Continuous mental arithmetic task vs. Baseline (alert state) was evaluated on Classification accuracy of mental fatigue using EEG power spectral densities. A kernel partial least squares classifier trained on EEG power spectral densities accurately classified alert versus fatigued states with 91% to 100% accuracy.