Different regressors for linear modelling of ElectroEncephaloGraphic recordings in visual and auditory tasks

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The use of hierarchical linear modelling has been increasing in the last 5 years to analyze EEG data. Until now, no clear comparison on linear modelling in different modalities has been done. Therefore, specific differences observed in both visual and auditory paradigms were computed with linear mod...

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Detalles Bibliográficos
Autor: Mugruza Vassallo, Carlos
Formato: objeto de conferencia
Fecha de Publicación:2016
Institución:Universidad de Ciencias y Humanidades
Repositorio:UCH-Institucional
Lenguaje:inglés
OAI Identifier:oai:repositorio.uch.edu.pe:uch/323
Enlace del recurso:http://repositorio.uch.edu.pe/handle/uch/323
https://ieeexplore.ieee.org/document/7516270
http://dx.doi.org/10.1109/BSN.2016.7516270
Nivel de acceso:acceso embargado
Materia:Electroencephalography
Auditory modality
Auditory tasks
Coefficient of determination
Eeg datum
Visual modalities
Visual tasks
Body sensor networks
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spelling Mugruza Vassallo, Carlos14 June 2016 through 17 June 20162019-08-18T01:31:58Z2019-08-18T01:31:58Z2016-06Mugruza Vassallo, C. (Junio, 2016). Different regressors for linear modelling of ElectroEncephaloGraphic recordings in visual and auditory tasks. En 13th International Conference on Wearable and Implantable Body Sensor Networks, USA.http://repositorio.uch.edu.pe/handle/uch/323https://ieeexplore.ieee.org/document/7516270http://dx.doi.org/10.1109/BSN.2016.751627010.1109/BSN.2016.7516270Annual Body Sensor Networks Conference, BSN2-s2.0-84983381628The use of hierarchical linear modelling has been increasing in the last 5 years to analyze EEG data. Until now, no clear comparison on linear modelling in different modalities has been done. Therefore, specific differences observed in both visual and auditory paradigms were computed with linear modelling. The Coefficient of Determination through the explained variance (R2) in Linear Modelling was sought in visual and auditory modalities. ERP scalp series of time from 100 to 300 ms for the visual task and around 150 ms to 400 for the auditory task were also plotted. Although these paradigms use different regressors, both paradigms showed reliable R2 signatures across the participants and reliable ERP scalp maps. Results accounted for different magnitudes in greater R2 values for visual modality. Auditory R2 results appeared with a reliable linear modelling when compared with R2 studies in other subjects.Submitted by sistemas uch (sistemas@uch.edu.pe) on 2019-08-18T01:31:58Z No. of bitstreams: 1 REPOSITORIO.pdf: 29656 bytes, checksum: 04319d67592b306412ce804f495f0004 (MD5)Made available in DSpace on 2019-08-18T01:31:58Z (GMT). No. of bitstreams: 1 REPOSITORIO.pdf: 29656 bytes, checksum: 04319d67592b306412ce804f495f0004 (MD5) Previous issue date: 2016-06engInstitute of Electrical and Electronics Engineers Inc.info:eu-repo/semantics/article13th Annual Body Sensor Networks Conference, BSN 2016info:eu-repo/semantics/embargoedAccessRepositorio Institucional - UCHUniversidad de Ciencias y Humanidadesreponame:UCH-Institucionalinstname:Universidad de Ciencias y Humanidadesinstacron:UCHElectroencephalographyAuditory modalityAuditory tasksCoefficient of determinationEeg datumVisual modalitiesVisual tasksBody sensor networksDifferent regressors for linear modelling of ElectroEncephaloGraphic recordings in visual and auditory tasksinfo:eu-repo/semantics/conferenceObjectuch/323oai:repositorio.uch.edu.pe:uch/3232019-12-20 18:34:00.789Repositorio UCHuch.dspace@gmail.com
dc.title.en_PE.fl_str_mv Different regressors for linear modelling of ElectroEncephaloGraphic recordings in visual and auditory tasks
title Different regressors for linear modelling of ElectroEncephaloGraphic recordings in visual and auditory tasks
spellingShingle Different regressors for linear modelling of ElectroEncephaloGraphic recordings in visual and auditory tasks
Mugruza Vassallo, Carlos
Electroencephalography
Auditory modality
Auditory tasks
Coefficient of determination
Eeg datum
Visual modalities
Visual tasks
Body sensor networks
title_short Different regressors for linear modelling of ElectroEncephaloGraphic recordings in visual and auditory tasks
title_full Different regressors for linear modelling of ElectroEncephaloGraphic recordings in visual and auditory tasks
title_fullStr Different regressors for linear modelling of ElectroEncephaloGraphic recordings in visual and auditory tasks
title_full_unstemmed Different regressors for linear modelling of ElectroEncephaloGraphic recordings in visual and auditory tasks
title_sort Different regressors for linear modelling of ElectroEncephaloGraphic recordings in visual and auditory tasks
author Mugruza Vassallo, Carlos
author_facet Mugruza Vassallo, Carlos
author_role author
dc.contributor.author.fl_str_mv Mugruza Vassallo, Carlos
dc.subject.en.fl_str_mv Electroencephalography
Auditory modality
Auditory tasks
Coefficient of determination
Eeg datum
Visual modalities
Visual tasks
Body sensor networks
topic Electroencephalography
Auditory modality
Auditory tasks
Coefficient of determination
Eeg datum
Visual modalities
Visual tasks
Body sensor networks
description The use of hierarchical linear modelling has been increasing in the last 5 years to analyze EEG data. Until now, no clear comparison on linear modelling in different modalities has been done. Therefore, specific differences observed in both visual and auditory paradigms were computed with linear modelling. The Coefficient of Determination through the explained variance (R2) in Linear Modelling was sought in visual and auditory modalities. ERP scalp series of time from 100 to 300 ms for the visual task and around 150 ms to 400 for the auditory task were also plotted. Although these paradigms use different regressors, both paradigms showed reliable R2 signatures across the participants and reliable ERP scalp maps. Results accounted for different magnitudes in greater R2 values for visual modality. Auditory R2 results appeared with a reliable linear modelling when compared with R2 studies in other subjects.
publishDate 2016
dc.date.accessioned.none.fl_str_mv 2019-08-18T01:31:58Z
dc.date.available.none.fl_str_mv 2019-08-18T01:31:58Z
dc.date.issued.fl_str_mv 2016-06
dc.type.none.fl_str_mv info:eu-repo/semantics/conferenceObject
format conferenceObject
dc.identifier.citation.en_PE.fl_str_mv Mugruza Vassallo, C. (Junio, 2016). Different regressors for linear modelling of ElectroEncephaloGraphic recordings in visual and auditory tasks. En 13th International Conference on Wearable and Implantable Body Sensor Networks, USA.
dc.identifier.uri.none.fl_str_mv http://repositorio.uch.edu.pe/handle/uch/323
https://ieeexplore.ieee.org/document/7516270
http://dx.doi.org/10.1109/BSN.2016.7516270
dc.identifier.doi.en_PE.fl_str_mv 10.1109/BSN.2016.7516270
dc.identifier.journal.en_PE.fl_str_mv Annual Body Sensor Networks Conference, BSN
dc.identifier.scopus.none.fl_str_mv 2-s2.0-84983381628
identifier_str_mv Mugruza Vassallo, C. (Junio, 2016). Different regressors for linear modelling of ElectroEncephaloGraphic recordings in visual and auditory tasks. En 13th International Conference on Wearable and Implantable Body Sensor Networks, USA.
10.1109/BSN.2016.7516270
Annual Body Sensor Networks Conference, BSN
2-s2.0-84983381628
url http://repositorio.uch.edu.pe/handle/uch/323
https://ieeexplore.ieee.org/document/7516270
http://dx.doi.org/10.1109/BSN.2016.7516270
dc.language.iso.none.fl_str_mv eng
language eng
dc.relation.en_PE.fl_str_mv info:eu-repo/semantics/article
dc.relation.ispartof.none.fl_str_mv 13th Annual Body Sensor Networks Conference, BSN 2016
dc.rights.en_PE.fl_str_mv info:eu-repo/semantics/embargoedAccess
eu_rights_str_mv embargoedAccess
dc.coverage.temporal.none.fl_str_mv 14 June 2016 through 17 June 2016
dc.publisher.en_PE.fl_str_mv Institute of Electrical and Electronics Engineers Inc.
dc.source.en_PE.fl_str_mv Repositorio Institucional - UCH
Universidad de Ciencias y Humanidades
dc.source.none.fl_str_mv reponame:UCH-Institucional
instname:Universidad de Ciencias y Humanidades
instacron:UCH
instname_str Universidad de Ciencias y Humanidades
instacron_str UCH
institution UCH
reponame_str UCH-Institucional
collection UCH-Institucional
repository.name.fl_str_mv Repositorio UCH
repository.mail.fl_str_mv uch.dspace@gmail.com
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score 13.932913
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