Generalized Time Domain Prediction Model for Motor Imagery-based Wheelchair Movement Control

Autores
Al Qaysi, Z. T.; Suzani, M. S; Abdul Rashid, Nazre Bin; Ismail, Reem D.; Ahmed, M.A.; Aljanabi, Rasha A.; Gil Costa, Graciela Verónica
Año de publicación
2024
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Brain-computer interface (BCI-MI)-based wheelchair control is, in principle, an appropriate method for completely paralyzed people with a healthy brain. In a BCI-based wheelchair control system, pattern recognition in terms of preprocessing, feature extraction, and classification plays a significant role in avoiding recognition errors, which can lead to the initiation of the wrong command that will put the user in unsafe condition. Therefore, this research´s goal is to create a time-domain generic pattern recognition model (GPRM) of two-class EEG-MI signals for use in a wheelchair control system. This GPRM has the advantage of having a model that is applicable to unknown subjects, not just one. This GPRM has been developed, evaluated, and validated by utilizing two datasets, namely, the BCI Competition IV and the Emotive EPOC datasets. Initially, fifteen-time windows were investigated with seven machine learning methods to determine the optimal time window as well as the best classification method with strong generalizability. Evidently, the experimental results of this study revealed that the duration of the EEG-MI signal in the range of 4–6 seconds (4–6 s) has a high impact on the classification accuracy while extracting the signal features using five statistical methods. Additionally, the results demonstrate a one-second latency after each command cue when using the eight-second EEG-MI signal that the Graz protocol used in this study. This one-second latency is inevitable because it is practically impossible for the subjects to imagine their MI hand movement instantly. Therefore, at least one second is required for subjects to prepare to initiate their motor imagery hand movement. Practically, the five statistical methods are efficient and viable for decoding the EEG-MI signal in the time domain. Evidently, the GPRM model based on the LR classifier showed its ability to achieve an impressive classification accuracy of 90%, which was validated on the Emotive EPOC dataset. The GPRM developed in this study is highly adaptable and recommended for deployment in real-time EEG-MIbased wheelchair control systems.
Fil: Al Qaysi, Z. T.. Tikrit University; Iraq
Fil: Suzani, M. S. Tikrit University; Iraq
Fil: Abdul Rashid, Nazre Bin. Tikrit University; Iraq
Fil: Ismail, Reem D.. Tikrit University; Iraq
Fil: Ahmed, M.A.. Tikrit University; Iraq
Fil: Aljanabi, Rasha A.. Tikrit University; Iraq
Fil: Gil Costa, Graciela Verónica. Universidad Nacional de San Luis; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Luis; Argentina
Materia
Big Data
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/265127

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network_name_str CONICET Digital (CONICET)
spelling Generalized Time Domain Prediction Model for Motor Imagery-based Wheelchair Movement ControlAl Qaysi, Z. T.Suzani, M. SAbdul Rashid, Nazre BinIsmail, Reem D.Ahmed, M.A.Aljanabi, Rasha A.Gil Costa, Graciela VerónicaBig Datahttps://purl.org/becyt/ford/1.2https://purl.org/becyt/ford/1Brain-computer interface (BCI-MI)-based wheelchair control is, in principle, an appropriate method for completely paralyzed people with a healthy brain. In a BCI-based wheelchair control system, pattern recognition in terms of preprocessing, feature extraction, and classification plays a significant role in avoiding recognition errors, which can lead to the initiation of the wrong command that will put the user in unsafe condition. Therefore, this research´s goal is to create a time-domain generic pattern recognition model (GPRM) of two-class EEG-MI signals for use in a wheelchair control system. This GPRM has the advantage of having a model that is applicable to unknown subjects, not just one. This GPRM has been developed, evaluated, and validated by utilizing two datasets, namely, the BCI Competition IV and the Emotive EPOC datasets. Initially, fifteen-time windows were investigated with seven machine learning methods to determine the optimal time window as well as the best classification method with strong generalizability. Evidently, the experimental results of this study revealed that the duration of the EEG-MI signal in the range of 4–6 seconds (4–6 s) has a high impact on the classification accuracy while extracting the signal features using five statistical methods. Additionally, the results demonstrate a one-second latency after each command cue when using the eight-second EEG-MI signal that the Graz protocol used in this study. This one-second latency is inevitable because it is practically impossible for the subjects to imagine their MI hand movement instantly. Therefore, at least one second is required for subjects to prepare to initiate their motor imagery hand movement. Practically, the five statistical methods are efficient and viable for decoding the EEG-MI signal in the time domain. Evidently, the GPRM model based on the LR classifier showed its ability to achieve an impressive classification accuracy of 90%, which was validated on the Emotive EPOC dataset. The GPRM developed in this study is highly adaptable and recommended for deployment in real-time EEG-MIbased wheelchair control systems.Fil: Al Qaysi, Z. T.. Tikrit University; IraqFil: Suzani, M. S. Tikrit University; IraqFil: Abdul Rashid, Nazre Bin. Tikrit University; IraqFil: Ismail, Reem D.. Tikrit University; IraqFil: Ahmed, M.A.. Tikrit University; IraqFil: Aljanabi, Rasha A.. Tikrit University; IraqFil: Gil Costa, Graciela Verónica. Universidad Nacional de San Luis; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Luis; ArgentinaMesopotamian Academic Press2024-06info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/265127Al Qaysi, Z. T.; Suzani, M. S; Abdul Rashid, Nazre Bin; Ismail, Reem D.; Ahmed, M.A.; et al.; Generalized Time Domain Prediction Model for Motor Imagery-based Wheelchair Movement Control; Mesopotamian Academic Press; Mesopotamian Journal of Big Data; 2024; 6-2024; 68-812958-6453CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://mesopotamian.press/journals/index.php/bigdata/article/view/429info:eu-repo/semantics/altIdentifier/doi/10.58496/MJBD/2024/006info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T09:55:33Zoai:ri.conicet.gov.ar:11336/265127instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-09-29 09:55:33.475CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Generalized Time Domain Prediction Model for Motor Imagery-based Wheelchair Movement Control
title Generalized Time Domain Prediction Model for Motor Imagery-based Wheelchair Movement Control
spellingShingle Generalized Time Domain Prediction Model for Motor Imagery-based Wheelchair Movement Control
Al Qaysi, Z. T.
Big Data
title_short Generalized Time Domain Prediction Model for Motor Imagery-based Wheelchair Movement Control
title_full Generalized Time Domain Prediction Model for Motor Imagery-based Wheelchair Movement Control
title_fullStr Generalized Time Domain Prediction Model for Motor Imagery-based Wheelchair Movement Control
title_full_unstemmed Generalized Time Domain Prediction Model for Motor Imagery-based Wheelchair Movement Control
title_sort Generalized Time Domain Prediction Model for Motor Imagery-based Wheelchair Movement Control
dc.creator.none.fl_str_mv Al Qaysi, Z. T.
Suzani, M. S
Abdul Rashid, Nazre Bin
Ismail, Reem D.
Ahmed, M.A.
Aljanabi, Rasha A.
Gil Costa, Graciela Verónica
author Al Qaysi, Z. T.
author_facet Al Qaysi, Z. T.
Suzani, M. S
Abdul Rashid, Nazre Bin
Ismail, Reem D.
Ahmed, M.A.
Aljanabi, Rasha A.
Gil Costa, Graciela Verónica
author_role author
author2 Suzani, M. S
Abdul Rashid, Nazre Bin
Ismail, Reem D.
Ahmed, M.A.
Aljanabi, Rasha A.
Gil Costa, Graciela Verónica
author2_role author
author
author
author
author
author
dc.subject.none.fl_str_mv Big Data
topic Big Data
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.2
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv Brain-computer interface (BCI-MI)-based wheelchair control is, in principle, an appropriate method for completely paralyzed people with a healthy brain. In a BCI-based wheelchair control system, pattern recognition in terms of preprocessing, feature extraction, and classification plays a significant role in avoiding recognition errors, which can lead to the initiation of the wrong command that will put the user in unsafe condition. Therefore, this research´s goal is to create a time-domain generic pattern recognition model (GPRM) of two-class EEG-MI signals for use in a wheelchair control system. This GPRM has the advantage of having a model that is applicable to unknown subjects, not just one. This GPRM has been developed, evaluated, and validated by utilizing two datasets, namely, the BCI Competition IV and the Emotive EPOC datasets. Initially, fifteen-time windows were investigated with seven machine learning methods to determine the optimal time window as well as the best classification method with strong generalizability. Evidently, the experimental results of this study revealed that the duration of the EEG-MI signal in the range of 4–6 seconds (4–6 s) has a high impact on the classification accuracy while extracting the signal features using five statistical methods. Additionally, the results demonstrate a one-second latency after each command cue when using the eight-second EEG-MI signal that the Graz protocol used in this study. This one-second latency is inevitable because it is practically impossible for the subjects to imagine their MI hand movement instantly. Therefore, at least one second is required for subjects to prepare to initiate their motor imagery hand movement. Practically, the five statistical methods are efficient and viable for decoding the EEG-MI signal in the time domain. Evidently, the GPRM model based on the LR classifier showed its ability to achieve an impressive classification accuracy of 90%, which was validated on the Emotive EPOC dataset. The GPRM developed in this study is highly adaptable and recommended for deployment in real-time EEG-MIbased wheelchair control systems.
Fil: Al Qaysi, Z. T.. Tikrit University; Iraq
Fil: Suzani, M. S. Tikrit University; Iraq
Fil: Abdul Rashid, Nazre Bin. Tikrit University; Iraq
Fil: Ismail, Reem D.. Tikrit University; Iraq
Fil: Ahmed, M.A.. Tikrit University; Iraq
Fil: Aljanabi, Rasha A.. Tikrit University; Iraq
Fil: Gil Costa, Graciela Verónica. Universidad Nacional de San Luis; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Luis; Argentina
description Brain-computer interface (BCI-MI)-based wheelchair control is, in principle, an appropriate method for completely paralyzed people with a healthy brain. In a BCI-based wheelchair control system, pattern recognition in terms of preprocessing, feature extraction, and classification plays a significant role in avoiding recognition errors, which can lead to the initiation of the wrong command that will put the user in unsafe condition. Therefore, this research´s goal is to create a time-domain generic pattern recognition model (GPRM) of two-class EEG-MI signals for use in a wheelchair control system. This GPRM has the advantage of having a model that is applicable to unknown subjects, not just one. This GPRM has been developed, evaluated, and validated by utilizing two datasets, namely, the BCI Competition IV and the Emotive EPOC datasets. Initially, fifteen-time windows were investigated with seven machine learning methods to determine the optimal time window as well as the best classification method with strong generalizability. Evidently, the experimental results of this study revealed that the duration of the EEG-MI signal in the range of 4–6 seconds (4–6 s) has a high impact on the classification accuracy while extracting the signal features using five statistical methods. Additionally, the results demonstrate a one-second latency after each command cue when using the eight-second EEG-MI signal that the Graz protocol used in this study. This one-second latency is inevitable because it is practically impossible for the subjects to imagine their MI hand movement instantly. Therefore, at least one second is required for subjects to prepare to initiate their motor imagery hand movement. Practically, the five statistical methods are efficient and viable for decoding the EEG-MI signal in the time domain. Evidently, the GPRM model based on the LR classifier showed its ability to achieve an impressive classification accuracy of 90%, which was validated on the Emotive EPOC dataset. The GPRM developed in this study is highly adaptable and recommended for deployment in real-time EEG-MIbased wheelchair control systems.
publishDate 2024
dc.date.none.fl_str_mv 2024-06
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/265127
Al Qaysi, Z. T.; Suzani, M. S; Abdul Rashid, Nazre Bin; Ismail, Reem D.; Ahmed, M.A.; et al.; Generalized Time Domain Prediction Model for Motor Imagery-based Wheelchair Movement Control; Mesopotamian Academic Press; Mesopotamian Journal of Big Data; 2024; 6-2024; 68-81
2958-6453
CONICET Digital
CONICET
url http://hdl.handle.net/11336/265127
identifier_str_mv Al Qaysi, Z. T.; Suzani, M. S; Abdul Rashid, Nazre Bin; Ismail, Reem D.; Ahmed, M.A.; et al.; Generalized Time Domain Prediction Model for Motor Imagery-based Wheelchair Movement Control; Mesopotamian Academic Press; Mesopotamian Journal of Big Data; 2024; 6-2024; 68-81
2958-6453
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/https://mesopotamian.press/journals/index.php/bigdata/article/view/429
info:eu-repo/semantics/altIdentifier/doi/10.58496/MJBD/2024/006
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Mesopotamian Academic Press
publisher.none.fl_str_mv Mesopotamian Academic Press
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
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