Approximate Entropy as a Measure of Cognitive Fatigue: An EEG Pilot Study

Abstract:

Nonlinear analysis of electroencephalogram (EEG) activity can provide a better understanding of brain signal dynamics during cognitive fatigue. The aim of this study was to analyse the regularity of EEG time series of healthy participants undergoing a series of cognitive tasks to test the hypothesis whether the irregularity of EEG signals changes through increasing time on performing cognitive task. EEG activities were recorded from two scalp loci of the international 10-20 system (that are F_z and P­_z electrodes representing the midline frontal and parietal lobes of the brain respectively) in twelve participants from which Approximate Entropy (ApEn) values were computed. ApEn is a nonlinear method which quantifies the irregularity of a time series whereby larger ApEn corresponds to more irregularity. ApEn values were found to be significantly different among the six time intervals of a series of 5-minutes cognitive tasks (p<0.01). Moreover, there was a significant positive correlation between ApEn at the P_z electrode and measured mental fatigue visual analogue scale (p<0.01). Therefore, the irregularity found in the participants’ EEG signals across the time intervals of performing cognitive task demonstrate that EEG regularity analysis with ApEn might be a useful tool in increasing our insight into the characteristics of the brain processes involved while performing fatiguing cognitive task and in quantifying cognitive fatigue.

Keywords: approximate entropy, attention, cognitive fatigue, EEG analysis, nonlinear method