Mental State Detection Using Riemannian Geometry on Electroencephalogram Brain Signals.

Selina C Wriessnegger,Philipp Raggam,Gernot R Müller-Putz,Kyriaki Kostoglou

doi:10.3389/fnhum.2021.746081

Abstract

The goal of this study was to implement a Riemannian geometry (RG)-based algorithm to detect high mental workload (MWL) and mental fatigue (MF) using task-induced electroencephalogram (EEG) signals. In order to elicit high MWL and MF, the participants performed a cognitively demanding task in the form of the letter n-back task. We analyzed the time-varying characteristics of the EEG band power (BP) features in the theta and alpha frequency band at different task conditions and cortical areas by employing a RG-based framework. MWL and MF were considered as too high, when the Riemannian distances of the task-run EEG reached or surpassed the threshold of the baseline EEG. The results of this study showed a BP increase in the theta and alpha frequency bands with increasing experiment duration, indicating elevated MWL and MF that impedes/hinders the task performance of the participants. High MWL and MF was detected in 8 out of 20 participants. The Riemannian distances also showed a steady increase toward the threshold with increasing experiment duration, with the most detections occurring toward the end of the experiment. To support our findings, subjective ratings (questionnaires concerning fatigue and workload levels) and behavioral measures (performance accuracies and response times) were also considered.

Highlights

With increasing developments of modern technologies like artificial intelligence and virtual reality environments, more and more applications make use of mental state monitoring systems
In contrast to the theta band, the band power (BP) changes for both performance group (PG) in the alpha band were most prominent at the central cortex
In contrast to the theta band, the post hoc test comparisons revealed statistically significant differences between run 1 and run 3 for each regions of interest (ROIs). These results suggest that the influence of increasing mental workload (MWL) and mental fatigue (MF) is higher in the alpha band than in the theta band, which can be seen at the topographical plots of the BP changes

Summary

Introduction

With increasing developments of modern technologies like artificial intelligence and virtual reality environments, more and more applications make use of mental state monitoring systems. More precisely increasing mental workload (MWL) and mental fatigue (MF), are known to affect the performance of a person while executing a cognitive demanding task (Käthner et al, 2014). This effect is usually projected on electrophysiological signals such as brain signals. It has been shown that reduced motivation of a user to perform a task which induces high MF, is associated with increased sympathetic activity and decreased parasympathetic activity (Mezzacappa et al, 1998; Johnson et al, 2006; Tanaka et al, 2009)

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Conclusion