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1. Identity statement
Reference TypeConference Paper (Conference Proceedings)
Sitesibgrapi.sid.inpe.br
Holder Codeibi 8JMKD3MGPEW34M/46T9EHH
Identifier8JMKD3MGPAW/3PMKTFL
Repositorysid.inpe.br/sibgrapi/2017/09.26.13.42
Last Update2017:09.26.13.42.56 (UTC) administrator
Metadata Repositorysid.inpe.br/sibgrapi/2017/09.26.13.42.56
Metadata Last Update2022:06.14.00.09.05 (UTC) administrator
DOI10.1109/SIBGRAPI.2017.30
Citation KeyCarvalho:2017:DeLeAp
TitleA Deep Learning Approach for Classification of Reaching Targets from EEG Images
FormatOn-line
Year2017
Access Date2024, Oct. 08
Number of Files1
Size2215 KiB
2. Context
AuthorCarvalho, Schubert R
AffiliationInstituto Tecnológico Vale
EditorTorchelsen, Rafael Piccin
Nascimento, Erickson Rangel do
Panozzo, Daniele
Liu, Zicheng
Farias, Mylène
Viera, Thales
Sacht, Leonardo
Ferreira, Nivan
Comba, João Luiz Dihl
Hirata, Nina
Schiavon Porto, Marcelo
Vital, Creto
Pagot, Christian Azambuja
Petronetto, Fabiano
Clua, Esteban
Cardeal, Flávio
e-Mail Addressschubert.carvalho@itv.org
Conference NameConference on Graphics, Patterns and Images, 30 (SIBGRAPI)
Conference LocationNiterói, RJ, Brazil
Date17-20 Oct. 2017
PublisherIEEE Computer Society
Publisher CityLos Alamitos
Book TitleProceedings
Tertiary TypeFull Paper
History (UTC)2017-09-26 13:42:56 :: schubert.carvalho@itv.org -> administrator ::
2022-06-14 00:09:05 :: administrator -> :: 2017
3. Content and structure
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
Version Typefinaldraft
KeywordsDeep Learning
EEG
BCI
Reaching Targets
AbstractIn this paper, we propose a new approach for the classification of reaching targets before movement onset, during visually-guided reaching in 3D space. Our approach combines the discriminant power of two-dimensional Electroencephalography (EEG) signals (i.e., EEG images) built from short epochs, with the feature extraction and classification capabilities of deep learning (DL) techniques, such as the Convolutional Neural Networks (CNN). In this work, reaching motions are performed into four directions: left, right, up and down. To allow more natural reaching movements, we explore the use of Virtual Reality (VR) to build an experimental setup that allows the subject to perform self-paced reaching in 3D space while standing. Our results reported an increase both in classification performance and early detection in the majority of our experiments. To our knowledge this is the first time that EEG images and CNN are combined for the classification of reaching targets before movement onset.
Arrangement 1urlib.net > SDLA > Fonds > SIBGRAPI 2017 > A Deep Learning...
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4. Conditions of access and use
data URLhttp://urlib.net/ibi/8JMKD3MGPAW/3PMKTFL
zipped data URLhttp://urlib.net/zip/8JMKD3MGPAW/3PMKTFL
Languageen
Target FilePID4959895.pdf
User Groupschubert.carvalho@itv.org
Visibilityshown
Update Permissionnot transferred
5. Allied materials
Mirror Repositorysid.inpe.br/banon/2001/03.30.15.38.24
Next Higher Units8JMKD3MGPAW/3PKCC58
8JMKD3MGPEW34M/4742MCS
Citing Item Listsid.inpe.br/sibgrapi/2017/09.12.13.04 37
sid.inpe.br/sibgrapi/2022/06.10.21.49 2
sid.inpe.br/banon/2001/03.30.15.38.24 1
Host Collectionsid.inpe.br/banon/2001/03.30.15.38
6. Notes
Empty Fieldsarchivingpolicy archivist area callnumber contenttype copyholder copyright creatorhistory descriptionlevel dissemination edition electronicmailaddress group isbn issn label lineage mark nextedition notes numberofvolumes orcid organization pages parameterlist parentrepositories previousedition previouslowerunit progress project readergroup readpermission resumeid rightsholder schedulinginformation secondarydate secondarykey secondarymark secondarytype serieseditor session shorttitle sponsor subject tertiarymark type url volume


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