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

Abstract

The purpose of this work is to set up a model that can estimate the mental fatigue of users based on the fusion of relevant features extracted from Positive 300 (P300) and steady state visual evoked potentials (SSVEP) measured by electroencephalogram. To this end, an experimental protocol describes the induction of P300, SSVEP and mental workload (which leads to mental fatigue by varying time-on-task) in different scenarios where environmental artifacts are controlled (obstacles number, obstacles velocities, ambient luminosity). Ten subjects took part in the experiment (with two suffering from cerebral palsy). Their mission is to navigate along a corridor from a starting point A to a goal point B where specific flickering stimuli are introduced to perform the P300 task. On the other hand, SSVEP task is elicited thanks to 10 Hz flickering lights. Correlated features are considered as inputs to fusion block which estimates mental workload. In order to deal with uncertainties and heterogeneity of P300 and SSVEP features, Dempster-Shafer (D-S) evidential reasoning is introduced. As the goal is to assess the reliability for the estimation of mental fatigue levels, D-S is compared to multi layer perception and linear discriminant analysis. The results show that D-S globally outperforms the other classifiers (although its performance significantly decreases between healthy and palsied groups). Finally we discuss the feasibility of such a fusion proposal in real life situation.

The Impact of Control Interface on Features of Heart Rate Variability

“…al. extracted features from electroencephalogram sensor signals that model mental fatigue [12]. To our knowledge there is no study that has modeled workload using HRV features during powered wheelchair navigation with various interfaces.…”

A Muscular and Cerebral Physiological Indices Assessment for Stress Measuring during Virtual Wheelchair Guidance

“…Such dissatisfaction promotes the appearance of negative emotions such as stress, nervousness. In our former studies [ 14 , 15 , 16 ] a comparison between healthy and disabled groups was undertaken and showed that the latter did not feel comfortable with the proposed system and we concluded that the setup of a solution to healthy people with adoption to disabled is not recommended due to acceptability differences. In this manuscript, we investigate the effect of stress on cerebral and muscular physiological indices.…”

SSVEP-EEG Denoising via Image Filtering Methods

“…There are different SSVEP response characteristics for patients with color blindness, color weakness, or normal vision, allowing Zheng et al to measure SSVEP to accurately and objectively detect visual dysfunction [9] . Lamti et al monitored SSVEP to detect visual fatigue, and established the relationship between SSVEP and visual fatigue grade [10] . Spiegel et al used SSVEP as a measurement tool to explore the binocular competition mechanism in autistic patients and successfully predicted the severity of autistic symptoms and correctly classified the diagnostic status of individuals (autistic and control groups) solely from neural data [11] .…”