Different regressors for linear modelling of ElectroEncephaloGraphic recordings in visual and auditory tasks
Descripción del Articulo
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...
| Autor: | |
|---|---|
| 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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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 |
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reponame:UCH-Institucional instname:Universidad de Ciencias y Humanidades instacron:UCH |
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Universidad de Ciencias y Humanidades |
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UCH |
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UCH |
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UCH-Institucional |
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UCH-Institucional |
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Repositorio UCH |
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uch.dspace@gmail.com |
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13.932913 |
Nota importante:
La información contenida en este registro es de entera responsabilidad de la institución que gestiona el repositorio institucional donde esta contenido este documento o set de datos. El CONCYTEC no se hace responsable por los contenidos (publicaciones y/o datos) accesibles a través del Repositorio Nacional Digital de Ciencia, Tecnología e Innovación de Acceso Abierto (ALICIA).
La información contenida en este registro es de entera responsabilidad de la institución que gestiona el repositorio institucional donde esta contenido este documento o set de datos. El CONCYTEC no se hace responsable por los contenidos (publicaciones y/o datos) accesibles a través del Repositorio Nacional Digital de Ciencia, Tecnología e Innovación de Acceso Abierto (ALICIA).