dc.creatorZablocki, Luciano Ivan
dc.creatorMendoza, Agustín Nicolás
dc.creatorNieto, Nicolás
dc.date2022-10
dc.date2022
dc.date2023-04-18T14:52:32Z
dc.date.accessioned2023-07-15T10:12:04Z
dc.date.available2023-07-15T10:12:04Z
dc.identifierhttp://sedici.unlp.edu.ar/handle/10915/151632
dc.identifierhttps://publicaciones.sadio.org.ar/index.php/JAIIO/article/download/263/214
dc.identifierissn:2451-7496
dc.identifier.urihttps://repositorioslatinoamericanos.uchile.cl/handle/2250/7490965
dc.descriptionBrain Computer Interfaces are useful devices that can partially restore the communication from severe compromised patients. Although the advances in deep learning have significantly improved brain pattern recognition, a large amount of data is required for training these deep architectures. In the last years, the inner speech paradigm has drew much attention, as it can potentially allow a natural control of different devices. However, as of the date of this publication, there is only a small amount of data available in this paradigm. In this work we show that it is possible, by means of transfer learning and domain adaptation methods, to make the most of the scarce data, enhancing the training process of a deep learning architecture used in brain computer interfaces.
dc.descriptionSociedad Argentina de Informática e Investigación Operativa
dc.formatapplication/pdf
dc.format54-60
dc.languageen
dc.rightshttp://creativecommons.org/licenses/by-nc-sa/4.0/
dc.rightsCreative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)
dc.subjectCiencias Informáticas
dc.subjectDeep Learning
dc.subjectDomain Adaptation
dc.subjectTransfer Learning
dc.subjectConvolutional Neural Network
dc.titleDomain Adaptation and Transfer Learning methods enhance Deep Learning Models used in Inner Speech Based Brain Computer Interfaces
dc.typeObjeto de conferencia
dc.typeObjeto de conferencia


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