Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/87699
Full metadata record
DC FieldValueLanguage
dc.contributor.authorSakhavi, Siavashen
dc.contributor.authorGuan, Cuntaien
dc.contributor.authorYan, Shuichengen
dc.date.accessioned2018-08-07T01:42:23Zen
dc.date.accessioned2019-12-06T16:47:30Z-
dc.date.available2018-08-07T01:42:23Zen
dc.date.available2019-12-06T16:47:30Z-
dc.date.issued2018en
dc.identifier.citationSakhavi, S., Guan, C., & Yan, S. Learning Temporal Information for Brain-Computer Interface Using Convolutional Neural Networks. IEEE Transactions on Neural Networks and Learning Systems, in press.en
dc.identifier.issn2162-237Xen
dc.identifier.urihttps://hdl.handle.net/10356/87699-
dc.description.abstractDeep learning (DL) methods and architectures have been the state-of-the-art classification algorithms for computer vision and natural language processing problems. However, the successful application of these methods in motor imagery (MI) brain-computer interfaces (BCIs), in order to boost classification performance, is still limited. In this paper, we propose a classification framework for MI data by introducing a new temporal representation of the data and also utilizing a convolutional neural network (CNN) architecture for classification. The new representation is generated from modifying the filter-bank common spatial patterns method, and the CNN is designed and optimized accordingly for the representation. Our framework outperforms the best classification method in the literature on the BCI competition IV-2a 4-class MI data set by 7% increase in average subject accuracy. Furthermore, by studying the convolutional weights of the trained networks, we gain an insight into the temporal characteristics of EEG.en
dc.description.sponsorshipASTAR (Agency for Sci., Tech. and Research, S’pore)en
dc.format.extent12 p.en
dc.language.isoenen
dc.relation.ispartofseriesIEEE Transactions on Neural Networks and Learning Systemsen
dc.rights© 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: [http://dx.doi.org/10.1109/TNNLS.2018.2789927].en
dc.subjectBrain-computer Interface (BCI)en
dc.subjectConvolutional Neural Network (CNN)en
dc.titleLearning temporal information for brain-computer interface using convolutional neural networksen
dc.typeJournal Articleen
dc.contributor.schoolSchool of Computer Science and Engineeringen
dc.identifier.doi10.1109/TNNLS.2018.2789927en
dc.description.versionAccepted versionen
item.fulltextWith Fulltext-
item.grantfulltextopen-
Appears in Collections:SCSE Journal Articles

SCOPUSTM   
Citations 1

414
Updated on Mar 28, 2024

Web of ScienceTM
Citations 1

288
Updated on Oct 27, 2023

Page view(s) 50

520
Updated on Mar 27, 2024

Download(s) 5

1,024
Updated on Mar 27, 2024

Google ScholarTM

Check

Altmetric


Plumx

Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.