Emotion detection for wheelchair navigation enhancement

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

doi:10.1017/s0263574714002197

Cited by 8 publications

(5 citation statements)

References 19 publications

(13 reference statements)

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“…This fact is consecutive to the use of flickering stimuli which can be the main issue before disabled users can accept the proposed solution. This specific point was also reported in our previous works 37,49,50 where emotion and mental fatigue were induced in simulated environments. Consequently, behavior entropy assumption could be biased by this fact as we consider that he differences between normal and distracted scenarios are only due to the distraction level of the user.…”

Section: Systems Performances Comparisonsupporting

confidence: 86%

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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Section: Systems Performances Comparisonsupporting

confidence: 86%

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

Lamti¹,

Khelifa²,

Hugel³

2018

Robotica

Self Cite

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“…The computational methods to extract and classify emotional features from EEG are summarized in Table 5. Artificial Neural Network (ANN) 7 [105,176,190,204,216,223,227] It is noteworthy to mention that a single feature extraction technique is not optimal across all of the applications. Besides, existing signals are not enough for high accuracy feature extraction.…”

Section: Eeg Correlates Of Emotion (Signals)mentioning

confidence: 99%

“…They were used to improve behavior, cognition, and emotion regulation. [1,40,95,105,139,194,214,227,282,283] …”

Section: Monitoringmentioning

confidence: 99%

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Review and Classification of Emotion Recognition Based on EEG Brain-Computer Interface System Research: A Systematic Review

et al. 2017

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Abstract:Recent developments and studies in brain-computer interface (BCI) technologies have facilitated emotion detection and classification. Many BCI studies have sought to investigate, detect, and recognize participants' emotional affective states. The applied domains for these studies are varied, and include such fields as communication, education, entertainment, and medicine. To understand trends in electroencephalography (EEG)-based emotion recognition system research and to provide practitioners and researchers with insights into and future directions for emotion recognition systems, this study set out to review published articles on emotion detection, recognition, and classification. The study also reviews current and future trends and discusses how these trends may impact researchers and practitioners alike. We reviewed 285 articles, of which 160 were refereed journal articles that were published since the inception of affective computing research. The articles were classified based on a scheme consisting of two categories: research orientation and domains/applications. Our results show considerable growth of EEG-based emotion detection journal publications. This growth reflects an increased research interest in EEG-based emotion detection as a salient and legitimate research area. Such factors as the proliferation of wireless EEG devices, advances in computational intelligence techniques, and machine learning spurred this growth.

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“…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.…”

Section: Introductionmentioning

confidence: 97%

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

Khelifa

Lamti²,

Hugel³

2021

Brain Sciences

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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.

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Emotion detection for wheelchair navigation enhancement

Cited by 8 publications

References 19 publications

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

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

Review and Classification of Emotion Recognition Based on EEG Brain-Computer Interface System Research: A Systematic Review

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

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