Shallow convolutional network excel for classifying motor imagery EEG in BCI applications

Many studies applying Brain-Computer Interfaces (BCIs) based on Motor Imagery (MI) tasks for rehabilitation have demonstrated the important role of detecting the Event-Related Desynchronization (ERD) to recognize the user’s motor intention. Nowadays, the development of MI-based BCI approaches withou...

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Autores:
milanés hermosilla, daily
Trujillo Codorniú, Rafael
López Baracaldo, René
Sagaro Zamora, Roberto
Delisle-Rodriguez, Denis
Llosas Albuerne, Yolanda
Núñez Alvarez, José Ricardo
Tipo de recurso:
Article of journal
Fecha de publicación:
2021
Institución:
Corporación Universidad de la Costa
Repositorio:
REDICUC - Repositorio CUC
Idioma:
eng
OAI Identifier:
oai:repositorio.cuc.edu.co:11323/8475
Acceso en línea:
https://hdl.handle.net/11323/8475
https://repositorio.cuc.edu.co/
Palabra clave:
Brain-computer interface
EEG
Motor imagery
Shallow convolutional neural networks
Rights
openAccess
License
CC0 1.0 Universal
id RCUC2_53b97f108f218546e96d2ba9be77b1db
oai_identifier_str oai:repositorio.cuc.edu.co:11323/8475
network_acronym_str RCUC2
network_name_str REDICUC - Repositorio CUC
repository_id_str
dc.title.spa.fl_str_mv Shallow convolutional network excel for classifying motor imagery EEG in BCI applications
title Shallow convolutional network excel for classifying motor imagery EEG in BCI applications
spellingShingle Shallow convolutional network excel for classifying motor imagery EEG in BCI applications
Brain-computer interface
EEG
Motor imagery
Shallow convolutional neural networks
title_short Shallow convolutional network excel for classifying motor imagery EEG in BCI applications
title_full Shallow convolutional network excel for classifying motor imagery EEG in BCI applications
title_fullStr Shallow convolutional network excel for classifying motor imagery EEG in BCI applications
title_full_unstemmed Shallow convolutional network excel for classifying motor imagery EEG in BCI applications
title_sort Shallow convolutional network excel for classifying motor imagery EEG in BCI applications
dc.creator.fl_str_mv milanés hermosilla, daily
Trujillo Codorniú, Rafael
López Baracaldo, René
Sagaro Zamora, Roberto
Delisle-Rodriguez, Denis
Llosas Albuerne, Yolanda
Núñez Alvarez, José Ricardo
dc.contributor.author.spa.fl_str_mv milanés hermosilla, daily
Trujillo Codorniú, Rafael
López Baracaldo, René
Sagaro Zamora, Roberto
Delisle-Rodriguez, Denis
Llosas Albuerne, Yolanda
Núñez Alvarez, José Ricardo
dc.subject.spa.fl_str_mv Brain-computer interface
EEG
Motor imagery
Shallow convolutional neural networks
topic Brain-computer interface
EEG
Motor imagery
Shallow convolutional neural networks
description Many studies applying Brain-Computer Interfaces (BCIs) based on Motor Imagery (MI) tasks for rehabilitation have demonstrated the important role of detecting the Event-Related Desynchronization (ERD) to recognize the user’s motor intention. Nowadays, the development of MI-based BCI approaches without or with very few calibration stages session-by-session for different days or weeks is still an open and emergent scope. In this work, a new scheme is proposed by applying Convolutional Neural Networks (CNN) for MI classification, using an end-to-end Shallow architecture that contains two convolutional layers for temporal and spatial feature extraction. We hypothesize that a BCI designed for capturing event-related desynchronization/synchronization (ERD/ERS) at the CNN input, with an adequate network design, may enhance the MI classification with fewer calibration stages. The proposed system using the same architecture was tested on three public datasets through multiple experiments, including both subject-specific and non-subject-specific training. Comparable and also superior results with respect to the state-of-the-art were obtained. On subjects whose EEG data were never used in the training process, our scheme also achieved promising results with respect to existing non-subject-specific BCIs, which shows greater progress in facilitating clinical applications.
publishDate 2021
dc.date.accessioned.none.fl_str_mv 2021-07-23T22:18:21Z
dc.date.available.none.fl_str_mv 2021-07-23T22:18:21Z
dc.date.issued.none.fl_str_mv 2021
dc.type.spa.fl_str_mv Artículo de revista
dc.type.coar.fl_str_mv http://purl.org/coar/resource_type/c_2df8fbb1
dc.type.coar.spa.fl_str_mv http://purl.org/coar/resource_type/c_6501
dc.type.content.spa.fl_str_mv Text
dc.type.driver.spa.fl_str_mv info:eu-repo/semantics/article
dc.type.redcol.spa.fl_str_mv http://purl.org/redcol/resource_type/ART
dc.type.version.spa.fl_str_mv info:eu-repo/semantics/acceptedVersion
format http://purl.org/coar/resource_type/c_6501
status_str acceptedVersion
dc.identifier.issn.spa.fl_str_mv 2169-3536
dc.identifier.uri.spa.fl_str_mv https://hdl.handle.net/11323/8475
dc.identifier.doi.spa.fl_str_mv 10.1109/ACCESS.2021.3091399
dc.identifier.instname.spa.fl_str_mv Corporación Universidad de la Costa
dc.identifier.reponame.spa.fl_str_mv REDICUC - Repositorio CUC
dc.identifier.repourl.spa.fl_str_mv https://repositorio.cuc.edu.co/
identifier_str_mv 2169-3536
10.1109/ACCESS.2021.3091399
Corporación Universidad de la Costa
REDICUC - Repositorio CUC
url https://hdl.handle.net/11323/8475
https://repositorio.cuc.edu.co/
dc.language.iso.none.fl_str_mv eng
language eng
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spelling milanés hermosilla, dailyTrujillo Codorniú, RafaelLópez Baracaldo, RenéSagaro Zamora, RobertoDelisle-Rodriguez, DenisLlosas Albuerne, YolandaNúñez Alvarez, José Ricardo2021-07-23T22:18:21Z2021-07-23T22:18:21Z20212169-3536https://hdl.handle.net/11323/847510.1109/ACCESS.2021.3091399Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/Many studies applying Brain-Computer Interfaces (BCIs) based on Motor Imagery (MI) tasks for rehabilitation have demonstrated the important role of detecting the Event-Related Desynchronization (ERD) to recognize the user’s motor intention. Nowadays, the development of MI-based BCI approaches without or with very few calibration stages session-by-session for different days or weeks is still an open and emergent scope. In this work, a new scheme is proposed by applying Convolutional Neural Networks (CNN) for MI classification, using an end-to-end Shallow architecture that contains two convolutional layers for temporal and spatial feature extraction. We hypothesize that a BCI designed for capturing event-related desynchronization/synchronization (ERD/ERS) at the CNN input, with an adequate network design, may enhance the MI classification with fewer calibration stages. The proposed system using the same architecture was tested on three public datasets through multiple experiments, including both subject-specific and non-subject-specific training. Comparable and also superior results with respect to the state-of-the-art were obtained. On subjects whose EEG data were never used in the training process, our scheme also achieved promising results with respect to existing non-subject-specific BCIs, which shows greater progress in facilitating clinical applications.milanés hermosilla, daily-will be generated-orcid-0000-0003-4463-9263-600Trujillo Codorniú, RafaelLópez Baracaldo, RenéSagaro Zamora, Roberto-will be generated-orcid-0000-0001-5808-1999-600Delisle-Rodriguez, Denis-will be generated-orcid-0000-0002-8937-031X-600Llosas Albuerne, Yolanda-will be generated-orcid-0000-0002-5713-0565-600Núñez Alvarez, José Ricardo-will be generated-orcid-0000-0002-6607-7305-600application/pdfengIEEE XploreCC0 1.0 Universalhttp://creativecommons.org/publicdomain/zero/1.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2IEEE Accesshttps://ieeexplore.ieee.org/document/9461749Brain-computer interfaceEEGMotor imageryShallow convolutional neural networksShallow convolutional network excel for classifying motor imagery EEG in BCI applicationsArtículo de revistahttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1Textinfo:eu-repo/semantics/articlehttp://purl.org/redcol/resource_type/ARTinfo:eu-repo/semantics/acceptedVersion[1] D. 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