Influence of mental fatigue on P300 and SSVEP during virtual wheelchair navigation

Lamti, Hachem A.; Khelifa, Mohamed Moncef Ben; Alimi, Adel M.; Gorce, Philippe

doi:10.1109/embc.2014.6943825

Cited by 10 publications

(13 citation statements)

References 12 publications

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“…But several researchers have pointed out that "for general acceptance of this technology, usability and user experience will need to be taken into account when designing (BCI) systems" [1]. Several factors could indeed impair the BCI user experience such as the reliability, the delay, but also the kind of visual stimulations used [7].…”

Section: Related Workmentioning

confidence: 99%

“…SSVEPbased BCIs are rather robust to external noise, they require limited training, and they have relatively stable performance across users. However, the flickering stimulation can be tiring and uncomfortable for the user [7].…”

Section: Brain-computer Interfacementioning

confidence: 99%

“…The definitions of each term (frustration, fatigue, motivation, and effectiveness) were provided on the questionnaire to ensure a good understanding of the questions. Likert scales for instant frustration, global frustration, fatigue and motivation were scaled on 7 levels : absent (1), barely perceptible (2), faintly present (3), light (4), marked (5), pronounced (6), strongly pronounced (7). Effectiveness was rated on 5 levels : strongly disagree (1), disagree (2), neither agree or disagree (3), agree (4), strongly agree (5).…”

Section: Collected Datamentioning

confidence: 99%

“…Brain-Computer Interfaces (BCIs) should enable to make a dream come true: interacting with computers by the sole means of the thought. BCIs are considered as a promising approach in several application fields, such as for the assistance to disabled people [7] or the mass market of videogames [1,9]. Since the pioneering works of the 1970s, a lot of progress has been made regarding BCI technology and the reliability of brain activity classification using electro-encephalography signals (EEG).…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Influence of Error Rate on Frustration of BCI Users

Évain

Argelaguet

Strock

et al. 2016

Proceedings of the International Working Conference on Advanced Visual Interfaces

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International audienceBrain-Computer Interfaces (BCIs) are still much less reliable than other input devices. The error rates of BCIs range from 5% up to 60%. In this paper, we assess the subjective frustration, motivation, and fatigue of BCI users, when confronted to different levels of error rate. We conducted a BCI experiment in which the error rate was artificially controlled. Our results first show that a prolonged use of BCI significantly increases the perceived fatigue, and induces a drop in motivation. We also found that user frustration increases with the error rate of the system but this increase does not seem critical for small differences of error rate. Thus, for future BCIs, we would advise to favor user comfort over accuracy when the potential gain of accuracy remains small

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Section: Related Workmentioning

confidence: 99%

Section: Brain-computer Interfacementioning

confidence: 99%

Section: Collected Datamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Influence of Error Rate on Frustration of BCI Users

Évain

Argelaguet

Strock

et al. 2016

Proceedings of the International Working Conference on Advanced Visual Interfaces

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“…Ref. [19] tried to command a powered wheelchair using a high frequency SSVEP (37)(38)(39)(40). The experiment consisted on driving the wheelchair in a room with four different layouts.…”

Section: Introductionmentioning

confidence: 99%

Cerebral and gaze data fusion for wheelchair navigation enhancement: case of distracted users

Lamti¹,

Khelifa²,

Hugel³

2018

Robotica

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SUMMARYThe goal of this paper is to present a new hybrid system based on the fusion of gaze data and Steady State Visual Evoked Potentials (SSVEP) not only to command a powered wheelchair, but also to account for users distraction levels (concentrated or distracted). For this purpose, a multi-layer perception neural network was set up in order to combine relevant gazing and blinking features from gaze sequence and brainwave features from occipital and parietal brain regions. The motivation behind this work is the shortages raised from the individual use of gaze-based and SSVEP-based wheelchair command techniques. The proposed framework is based on three main modules: a gaze module to select command and activate the flashing stimuli. An SSVEP module to validate the selected command. In parallel, a distraction level module estimates the intention of the user by mean of behavioral entropy and validates/inhibits the command accordingly. An experimental protocol was set up and the prototype was tested on five paraplegic subjects and compared with standard SSVEP and gaze-based systems. The results showed that the new framework performed better than conventional gaze-based and SSVEP-based systems. Navigation performance was assessed based on navigation time and obstacles collisions.

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

2019

Self Cite

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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.

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Influence of mental fatigue on P300 and SSVEP during virtual wheelchair navigation

Cited by 10 publications

References 12 publications

Influence of Error Rate on Frustration of BCI Users

Influence of Error Rate on Frustration of BCI Users

Cerebral and gaze data fusion for wheelchair navigation enhancement: case of distracted users

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

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