Mental State Detection Using Riemannian Geometry on Electroencephalogram Brain Signals

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.

Acute combined effects of concurrent physical activities on autonomic nervous activation during cognitive tasks

“…The n-back task performance exhibited adverse effects, including the extension of the MRT and maxRT, an increase in wrong responding rates, and a decrease in correct rates. These outcomes are in good agreement with the findings of previous study ( Wriessnegger et al, 2021 ). To induce diverse levels of mental workload, Wriessnegger et al instructed subjects to engage in three types of n-back tasks (1-back, 2-back, and 3-back) for three trials (20 min each, 60 min in total) ( Wriessnegger et al, 2021 ).…”

“…In the current study, the n-back working memory task program introduced by Wriessnegger et al (2021) , was applied to induce mental workload and fatigue states in the study subjects. At the initiation of the n-back task, subjects received instructions, followed by a sequence of letters.…”

“…Maintaining the natural working environment and operators’ activities is crucial, but subjective methods may compromise task execution ( Albuquerque et al, 2020 ). In contrast, objective measurement methods, including electroencephalogram (EEG) ( Zhang et al, 2021 ; Chitti et al, 2021 ; Wriessnegger et al, 2021 ), eye movement features (represented by blink, pupil diameter and saccade) ( Bafna and Hansen, 2021 ), heart rate variability (HRV) ( Burlacu et al, 2021 ) and postural stability indicators ( Cheng et al, 2018 ; Sun et al, 2019 ), rely on biological signals and offer promising avenues for effective and objective mental workload assessment without disrupting tasks. These approaches pave the way for more precise and real-time monitoring, ensuring a comprehensive understanding of mental workload dynamics during various activities.…”

“…While mental workload in most existing research protocols is induced by sleep deprivation ( Cheng et al, 2021 ), continuous cognitive tasks such as an n-back task ( Karthikeyan et al, 2021 ; Wriessnegger et al, 2021 ) and a Stroop task ( Nikooharf Salehi et al, 2022 ), these protocols often overlook the role of non-cognitive variables, particularly physical factors. The real-time evaluation of mental workload based on ECG signals is undoubtedly influenced by dynamics.…”

Editorial: Women in brain-computer interfaces

“…Especially the type of BCI, which are not consciously controlled by the user or reacting to external stimulation, called passive BCIs (pBCIs) are of main interest (Zander and Kothe, 2011 ). Having access to the user's ongoing brain activity enables applications spanning a variety of domains such as brain-activity based gaming (Bos et al, 2010 ; Holz et al, 2013 ), workload assessment (Wriessnegger et al, 2021 ); and neuromarketing (van Erp et al, 2012 ; Wriessnegger et al, 2017 ). In all these and other contexts and applications, feedback is a crucial aspect in the use of BCI techniques as it provides the subject with direct and continuous information on their own brain activity, thus allowing them to learn to modulate it in order to operate the BCI system, ultimately improving their performance (Sokunbi, 2017 ).…”