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A Brain Controlled Command-Line Interface to Enhance the Accessibility of Severe Motor Disabled People to Personnel Computer

There are many applications controlled by the brain signals to bridge the gap in the digital divide between the disabled and the non-disabled people. The deployment of novel assistive technologies using brain-computer interface (BCI) will go a long way toward achieving this lofty goal, especially af...

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Autores principales: Gannouni, Sofien, Belwafi, Kais, Al-Sulmi, Mohammad Reshood, Al-Farhood, Meshal Dawood, Al-Obaid, Omar Ali, Al-Awadh, Abdullah Mohammed, Aboalsamh, Hatim, Belghith, Abdelfettah
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9313199/
https://www.ncbi.nlm.nih.gov/pubmed/35884732
http://dx.doi.org/10.3390/brainsci12070926
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author Gannouni, Sofien
Belwafi, Kais
Al-Sulmi, Mohammad Reshood
Al-Farhood, Meshal Dawood
Al-Obaid, Omar Ali
Al-Awadh, Abdullah Mohammed
Aboalsamh, Hatim
Belghith, Abdelfettah
author_facet Gannouni, Sofien
Belwafi, Kais
Al-Sulmi, Mohammad Reshood
Al-Farhood, Meshal Dawood
Al-Obaid, Omar Ali
Al-Awadh, Abdullah Mohammed
Aboalsamh, Hatim
Belghith, Abdelfettah
author_sort Gannouni, Sofien
collection PubMed
description There are many applications controlled by the brain signals to bridge the gap in the digital divide between the disabled and the non-disabled people. The deployment of novel assistive technologies using brain-computer interface (BCI) will go a long way toward achieving this lofty goal, especially after the successes demonstrated by these technologies in the daily life of people with severe disabilities. This paper contributes in this direction by proposing an integrated framework to control the operating system functionalities using Electroencephalography signals. Different signal processing algorithms were applied to remove artifacts, extract features, and classify trials. The proposed approach includes different classification algorithms dedicated to detecting the P300 responses efficiently. The predicted commands passed through a socket to the API system, permitting the control of the operating system functionalities. The proposed system outperformed those obtained by the winners of the BCI competition and reached an accuracy average of 94.5% according to the offline approach. The framework was evaluated according to the online process and achieved an excellent accuracy attaining 97% for some users but not less than 90% for others. The suggested framework enhances the information accessibility for people with severe disabilities and helps them perform their daily tasks efficiently. It permits the interaction between the user and personal computers through the brain signals without any muscular efforts.
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spelling pubmed-93131992022-07-26 A Brain Controlled Command-Line Interface to Enhance the Accessibility of Severe Motor Disabled People to Personnel Computer Gannouni, Sofien Belwafi, Kais Al-Sulmi, Mohammad Reshood Al-Farhood, Meshal Dawood Al-Obaid, Omar Ali Al-Awadh, Abdullah Mohammed Aboalsamh, Hatim Belghith, Abdelfettah Brain Sci Article There are many applications controlled by the brain signals to bridge the gap in the digital divide between the disabled and the non-disabled people. The deployment of novel assistive technologies using brain-computer interface (BCI) will go a long way toward achieving this lofty goal, especially after the successes demonstrated by these technologies in the daily life of people with severe disabilities. This paper contributes in this direction by proposing an integrated framework to control the operating system functionalities using Electroencephalography signals. Different signal processing algorithms were applied to remove artifacts, extract features, and classify trials. The proposed approach includes different classification algorithms dedicated to detecting the P300 responses efficiently. The predicted commands passed through a socket to the API system, permitting the control of the operating system functionalities. The proposed system outperformed those obtained by the winners of the BCI competition and reached an accuracy average of 94.5% according to the offline approach. The framework was evaluated according to the online process and achieved an excellent accuracy attaining 97% for some users but not less than 90% for others. The suggested framework enhances the information accessibility for people with severe disabilities and helps them perform their daily tasks efficiently. It permits the interaction between the user and personal computers through the brain signals without any muscular efforts. MDPI 2022-07-15 /pmc/articles/PMC9313199/ /pubmed/35884732 http://dx.doi.org/10.3390/brainsci12070926 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Gannouni, Sofien
Belwafi, Kais
Al-Sulmi, Mohammad Reshood
Al-Farhood, Meshal Dawood
Al-Obaid, Omar Ali
Al-Awadh, Abdullah Mohammed
Aboalsamh, Hatim
Belghith, Abdelfettah
A Brain Controlled Command-Line Interface to Enhance the Accessibility of Severe Motor Disabled People to Personnel Computer
title A Brain Controlled Command-Line Interface to Enhance the Accessibility of Severe Motor Disabled People to Personnel Computer
title_full A Brain Controlled Command-Line Interface to Enhance the Accessibility of Severe Motor Disabled People to Personnel Computer
title_fullStr A Brain Controlled Command-Line Interface to Enhance the Accessibility of Severe Motor Disabled People to Personnel Computer
title_full_unstemmed A Brain Controlled Command-Line Interface to Enhance the Accessibility of Severe Motor Disabled People to Personnel Computer
title_short A Brain Controlled Command-Line Interface to Enhance the Accessibility of Severe Motor Disabled People to Personnel Computer
title_sort brain controlled command-line interface to enhance the accessibility of severe motor disabled people to personnel computer
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9313199/
https://www.ncbi.nlm.nih.gov/pubmed/35884732
http://dx.doi.org/10.3390/brainsci12070926
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