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
- Institución
- Consejo Nacional de Investigaciones Científicas y Técnicas
- OAI Identificador
- oai:ri.conicet.gov.ar:11336/265127
Ver los metadatos del registro completo
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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) |
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Consejo Nacional de Investigaciones Científicas y Técnicas |
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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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13.070432 |