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

dc.creatormilanés hermosilla, daily
dc.creatorTrujillo Codorniú, Rafael
dc.creatorLópez Baracaldo, René
dc.creatorSagaro Zamora, Roberto
dc.creatorDelisle-Rodriguez, Denis
dc.creatorLlosas Albuerne, Yolanda
dc.creatorNúñez Alvarez, José Ricardo
dc.date2021-07-23T22:18:21Z
dc.date2021-07-23T22:18:21Z
dc.date2021
dc.date.accessioned2023-10-03T19:08:37Z
dc.date.available2023-10-03T19:08:37Z
dc.identifier2169-3536
dc.identifierhttps://hdl.handle.net/11323/8475
dc.identifier10.1109/ACCESS.2021.3091399
dc.identifierCorporación Universidad de la Costa
dc.identifierREDICUC - Repositorio CUC
dc.identifierhttps://repositorio.cuc.edu.co/
dc.identifier.urihttps://repositorioslatinoamericanos.uchile.cl/handle/2250/9168168
dc.descriptionMany 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.
dc.formatapplication/pdf
dc.formatapplication/pdf
dc.languageeng
dc.publisherIEEE Xplore
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dc.rightsCC0 1.0 Universal
dc.rightshttp://creativecommons.org/publicdomain/zero/1.0/
dc.rightsinfo:eu-repo/semantics/openAccess
dc.rightshttp://purl.org/coar/access_right/c_abf2
dc.sourceIEEE Access
dc.sourcehttps://ieeexplore.ieee.org/document/9461749
dc.subjectBrain-computer interface
dc.subjectEEG
dc.subjectMotor imagery
dc.subjectShallow convolutional neural networks
dc.titleShallow convolutional network excel for classifying motor imagery EEG in BCI applications
dc.typeArtículo de revista
dc.typehttp://purl.org/coar/resource_type/c_6501
dc.typeText
dc.typeinfo:eu-repo/semantics/article
dc.typeinfo:eu-repo/semantics/publishedVersion
dc.typehttp://purl.org/redcol/resource_type/ART
dc.typeinfo:eu-repo/semantics/acceptedVersion
dc.typehttp://purl.org/coar/version/c_ab4af688f83e57aa


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