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

Published online by Cambridge University Press:  25 September 2018

Hachem A. Lamti ,
Mohamed Moncef Ben Khelifa  and
Vincent Hugel
Hachem A. Lamti*
Affiliation:
COnception de Systemes Mecaniques et Robotiques (COSMER) Laboratory, South University, Toulon-Var, France. E-mail: [email protected]
Mohamed Moncef Ben Khelifa
Affiliation:
Impact de l'Activite Physique sur la Sante (IAPS) Laboratory, South University, Toulon-Var, France. E-mail: [email protected]
Vincent Hugel
Affiliation:
COnception de Systemes Mecaniques et Robotiques (COSMER) Laboratory, South University, Toulon-Var, France. E-mail: [email protected]
*
*Corresponding author. E-mail: [email protected]
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Summary

The 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.

Keywords

BCIEEGWheelchairSSVEPNavigationGazeDistractionBehavioral entropy
Type
Articles
Information
Robotica , Volume 37 , Issue 2 , February 2019 , pp. 246 - 263
Copyright
Copyright © Cambridge University Press 2018 

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