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

Lamti, Hachem A.; Khelifa, Mohamed Moncef Ben; Hugel, Vincent

doi:10.1017/s0263574718000991

Cited by 11 publications

(4 citation statements)

References 40 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Authors in [76] successfully linked visible feedback using a combination of Emotiv and a head-mounted device named HTC VIVE to familiarize disabled people with brain-machine interaction on a 3-D space. In two separate studies, Lamti et al developed EEG and gaze data fusing framework for wheelchair navigation [77] and, in [88], Emotiv and a Tobii eye tracker (EyeX model) that were were used to monitor brain and eye activities.…”

Section: Brain-computer Interfacementioning

confidence: 99%

Wireless Sensors for Brain Activity—A Survey

et al. 2020

View full text Add to dashboard Cite

Over the last decade, the area of electroencephalography (EEG) witnessed a progressive move from high-end large measurement devices, relying on accurate construction and providing high sensitivity, to miniature hardware, more specifically wireless wearable EEG devices. While accurate, traditional EEG systems need a complex structure and long periods of application time, unwittingly causing discomfort and distress on the users. Given their size and price, aside from their lower sensitivity and narrower spectrum band(s), wearable EEG devices may be used regularly by individuals for continuous collection of user data from non-medical environments. This allows their usage for diverse, nontraditional, non-medical applications, including cognition, BCI, education, and gaming. Given the reduced need for standardization or accuracy, the area remains a rather incipient one, mostly driven by the emergence of new devices that represent the critical link of the innovation chain. In this context, the aim of this study is to provide a holistic assessment of the consumer-grade EEG devices for cognition, BCI, education, and gaming, based on the existing products, the success of their underlying technologies, as benchmarked by the undertaken studies, and their integration with current applications across the four areas. Beyond establishing a reference point, this review also provides the critical and necessary systematic guidance for non-medical EEG research and development efforts at the start of their investigation.

show abstract

Section: Brain-computer Interfacementioning

confidence: 99%

Wireless Sensors for Brain Activity—A Survey

et al. 2020

View full text Add to dashboard Cite

show abstract

“…TMSI benefits from exploiting the temporally local structure of EEG signals and could be a potential method for the performance of SSVEP-based BCI. In our former study (Lamti et al 2018), an active gaze/SSVEP hybrid wheelchair command system was set up in order to enhance navigation performance. This project deals mainly with the case of distracted users where gaze and cerebral distractions were added.…”

Section: Ssvepmentioning

confidence: 99%

“…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).…”

Section: Introductionmentioning

confidence: 99%

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

2019

Self Cite

View full text Add to dashboard Cite

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.

show abstract

“…These results suggest that deeper investigations could detect efficiently the introduction of the time pressure factor as stressor. In our previous work [ 26 , 27 ] time pressure was investigated as stressor as well as its influence on EEG data. It was concluded that temporal and frequency features extracted from frontal and fronto-central sensors were significantly correlated with stress levels.…”

Section: Introductionmentioning

confidence: 99%

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

Khelifa

Lamti²,

Hugel³

2021

Brain Sciences

Self Cite

View full text Add to dashboard Cite

The work presented in this manuscript has the purpose to assess the relationship between human factors and physiological indices. We discuss the relationship between stress as human factor and cerebral and muscular signals as features. Ten male paraplegic, right-handed subjects were volunteers for the experiment (mean age 34 ±6). They drove a virtual wheelchair in an indoor environment. They filled five missions where, in each one, an environmental parameter was changed. Meanwhile, they were equipped with Electromyography (EMG) sensors and Electroencephalography (EEG). Frequency and temporal features were filtered and extracted. Principal component analysis (PCA), Fisher’s tests, repeated measure Anova and post hoc Tukey test (α = 0.05) were implemented for statistics. Environmental modifications are subject to induce stress, which impacts muscular and cerebral activities. While the time pressure parameter was the most influent, the transition from static to moving obstacles (avatars), tends to have a significant impact on stress levels. However, adding more moving obstacles did not show any impact. A synchronization factor was noticed between cerebral and muscular features in higher stress levels. Further examination is needed to assess EEG reliability in these situations.

show abstract

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

Cited by 11 publications

References 40 publications

Wireless Sensors for Brain Activity—A Survey

Wireless Sensors for Brain Activity—A Survey

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

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

Contact Info

Product

Resources

About