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dc.contributor.authormilanés hermosilla, dailyspa
dc.contributor.authorTrujillo Codorniú, Rafaelspa
dc.contributor.authorLópez-Baracaldo, Renéspa
dc.contributor.authorSagaro Zamora, Robertospa
dc.contributor.authorDelisle-Rodriguez, Denisspa
dc.contributor.authorVillarejo Mayor, John Jairospa
dc.contributor.authorNúñez Alvarez, José Ricardospa
dc.date.accessioned2021-11-26T13:54:23Z
dc.date.available2021-11-26T13:54:23Z
dc.date.issued2021-10-30
dc.identifier.issn1424-3210spa
dc.identifier.issn1424-8220spa
dc.identifier.urihttps://hdl.handle.net/11323/8928spa
dc.description.abstractMotor Imagery (MI)-based Brain–Computer Interfaces (BCIs) have been widely used as an alternative communication channel to patients with severe motor disabilities, achieving high classification accuracy through machine learning techniques. Recently, deep learning techniques have spotlighted the state-of-the-art of MI-based BCIs. These techniques still lack strategies to quantify predictive uncertainty and may produce overconfident predictions. In this work, methods to enhance the performance of existing MI-based BCIs are proposed in order to obtain a more reliable system for real application scenarios. First, the Monte Carlo dropout (MCD) method is proposed on MI deep neural models to improve classification and provide uncertainty estimation. This approach was implemented using Shallow Convolutional Neural Network (SCNN-MCD) and with an ensemble model (E-SCNN-MCD). As another contribution, to discriminate MI task predictions of high uncertainty, a threshold approach is introduced and tested for both SCNN-MCD and E-SCNN-MCD approaches. The BCI Competition IV Databases 2a and 2b were used to evaluate the proposed methods for both subject-specific and non-subject-specific strategies, obtaining encouraging results for MI recognition.spa
dc.format.mimetypeapplication/pdfspa
dc.language.isoeng
dc.publisherCorporación Universidad de la Costaspa
dc.rightsCC0 1.0 Universalspa
dc.rights.urihttp://creativecommons.org/publicdomain/zero/1.0/spa
dc.sourceSensorsspa
dc.subjectBrain–computer interfacesspa
dc.subjectMonte Carlo dropoutspa
dc.subjectMotor imageryspa
dc.subjectShallow convolutional neural networkspa
dc.subjectUncertainty estimationspa
dc.titleMonte Carlo dropout for uncertainty estimation and motor imagery classificationspa
dc.typeArtículo de revistaspa
dc.source.urlhttps://www.mdpi.com/1424-8220/21/21/7241spa
dc.rights.accessrightsinfo:eu-repo/semantics/openAccessspa
dc.identifier.doihttps://doi.org/10.3390/s21217241spa
dc.identifier.instnameCorporación Universidad de la Costaspa
dc.identifier.reponameREDICUC - Repositorio CUCspa
dc.identifier.repourlhttps://repositorio.cuc.edu.co/spa
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