When mental fatigue maybe characterized by Event Related Potential (P300) during virtual wheelchair navigation

Abstract

The goal of this study is to investigate the influence of mental fatigue on the event related potential P300 features (maximum pick, minimum amplitude, latency and period) during virtual wheelchair navigation. For this purpose, an experimental environment was set up based on customizable environmental parameters (luminosity, number of obstacles and obstacles velocities). A correlation study between P300 and fatigue ratings was conducted. Finally, the best correlated features supplied three classification algorithms which are MLP (Multi Layer Perceptron), Linear Discriminate Analysis and Support Vector Machine. The results showed that the maximum feature over visual and temporal regions as well as period feature over frontal, fronto-central and visual regions were correlated with mental fatigue levels. In the other hand, minimum amplitude and latency features didn't show any correlation. Among classification techniques, MLP showed the best performance although the differences between classification techniques are minimal. Those findings can help us in order to design suitable mental fatigue based wheelchair control.

Attention Detection in Virtual Environments Using EEG Signals: A Scoping Review

“…As future direction, we can list the main recommended aspects that surround and must follow attention detection/investigation through EEG-signal: identify attention allocation through N100, N200, P100 and P300 in fronto-central-occipital brain areas (Vogt et al, 2015;Jaquess et al, 2017); P300 in fronto-parietal brain areas (Rohani and Puthusserypady, 2015;Vogt et al, 2015;Yu et al, 2015;Causse et al, 2016;Chen et al, 2016;Lamti et al, 2016;Jaquess et al, 2017;Chung et al, 2018;Li et al, 2020d) and SSVEP component in occipital area (Leite et al, 2018;Chu and D'Zmura, 2019) as we cited in Figure 3; x. Investigate attention by means of brain waves increase, decrease and beta (12-31.25 Hz)/theta (3-8 Hz) ratio in fronto-parietal brain areas, θ (Clemente et al, 2014;Fuentes-García et al, 2019;Lim et al, 2019), in occipital area, θ (Savage et al, 2013), in frontal area, αβθδ (Clemente et al, 2014;Cowley and Ravaja, 2014;Jagannath and Balasubramanian, 2014;Yin and Zhang, 2014;Lee et al, 2015;Hazarika et al, 2018;Lim et al, 2019;Li et al, 2020d) as we cited in Figure 3.…”

Using Muse: Rapid Mobile Assessment of Brain Performance

“…In particular, the amplitude and latency of the P300 ERP component have been shown to be reduced and lengthened, respectively, due to increased cognitive fatigue. Indeed, a number of studies have indicated that cognitive fatigue results in diminished P300 amplitudes (Uetake and Murata, 2000;Kato et al, 2009;Schmidt et al, 2009;Zhao et al, 2012;Käthner et al, 2014;Lamti et al, 2016) and increased P300 latencies (Kaseda et al, 1998;Uetake and Murata, 2000).…”

Mental fatigue level detection based on event related and visual evoked potentials features fusion in virtual indoor environment

“…The conclusions will be exploited as a basis for future schemes in order to ensure the migration of those findings from simulation to real life situations. Speaking of which, in our former studies, P300 and SSVEP were used as a source of control for wheelchair commands (more details could be found in Lamti et al 2016Lamti et al , 2018 (as part of active BCI systems). In an inverse scheme, under the framework of passive BCI, those same modalities are assessed in order to detect, estimate and predict the user's state (mental fatigue and emotions).…”

“…To this end, different terminologies will be exposed such as P300, SSVEP and mental fatigue. Then, the experimental setup and methodologies will be described [for more details please refer to our former studies (Lamti et al 2014a(Lamti et al , 2016].…”