Mental State Detection Using Riemannian Geometry on Electroencephalogram Brain Signals

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 fo...

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Auteurs principaux:	Selina C. Wriessnegger, Philipp Raggam, Kyriaki Kostoglou, Gernot R. Müller-Putz
Format:	article
Langue:	EN
Publié:	Frontiers Media S.A. 2021
Sujets:	EEG mental workload mental fatigue band power features Riemannian geometry Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571
Accès en ligne:	https://doaj.org/article/8a2b8f672f7442df975fcdd0edf8c575
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spelling	oai:doaj.org-article:8a2b8f672f7442df975fcdd0edf8c5752021-12-01T09:14:21ZMental State Detection Using Riemannian Geometry on Electroencephalogram Brain Signals1662-516110.3389/fnhum.2021.746081https://doaj.org/article/8a2b8f672f7442df975fcdd0edf8c5752021-11-01T00:00:00Zhttps://www.frontiersin.org/articles/10.3389/fnhum.2021.746081/fullhttps://doaj.org/toc/1662-5161The 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.Selina C. WriessneggerSelina C. WriessneggerPhilipp RaggamPhilipp RaggamKyriaki KostoglouGernot R. Müller-PutzGernot R. Müller-PutzFrontiers Media S.A.articleEEGmental workloadmental fatigueband power featuresRiemannian geometryNeurosciences. Biological psychiatry. NeuropsychiatryRC321-571ENFrontiers in Human Neuroscience, Vol 15 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	EEG mental workload mental fatigue band power features Riemannian geometry Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571
spellingShingle	EEG mental workload mental fatigue band power features Riemannian geometry Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571 Selina C. Wriessnegger Selina C. Wriessnegger Philipp Raggam Philipp Raggam Kyriaki Kostoglou Gernot R. Müller-Putz Gernot R. Müller-Putz Mental State Detection Using Riemannian Geometry on Electroencephalogram Brain Signals
description	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.
format	article
author	Selina C. Wriessnegger Selina C. Wriessnegger Philipp Raggam Philipp Raggam Kyriaki Kostoglou Gernot R. Müller-Putz Gernot R. Müller-Putz
author_facet	Selina C. Wriessnegger Selina C. Wriessnegger Philipp Raggam Philipp Raggam Kyriaki Kostoglou Gernot R. Müller-Putz Gernot R. Müller-Putz
author_sort	Selina C. Wriessnegger
title	Mental State Detection Using Riemannian Geometry on Electroencephalogram Brain Signals
title_short	Mental State Detection Using Riemannian Geometry on Electroencephalogram Brain Signals
title_full	Mental State Detection Using Riemannian Geometry on Electroencephalogram Brain Signals
title_fullStr	Mental State Detection Using Riemannian Geometry on Electroencephalogram Brain Signals
title_full_unstemmed	Mental State Detection Using Riemannian Geometry on Electroencephalogram Brain Signals
title_sort	mental state detection using riemannian geometry on electroencephalogram brain signals
publisher	Frontiers Media S.A.
publishDate	2021
url	https://doaj.org/article/8a2b8f672f7442df975fcdd0edf8c575
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Mental State Detection Using Riemannian Geometry on Electroencephalogram Brain Signals

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