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

Carlos Mugruza-Vassallo

Producción científica: Capítulo del libro/informe/acta de congresoContribución a la conferenciarevisión exhaustiva

6 Citas (Scopus)

Resumen

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.

Idioma originalInglés
Título de la publicación alojadaBSN 2016 - 13th Annual Body Sensor Networks Conference
EditorialInstitute of Electrical and Electronics Engineers Inc.
Páginas260-265
Número de páginas6
ISBN (versión digital)9781509030873
DOI
EstadoPublicada - 18 jul. 2016
Publicado de forma externa
Evento13th Annual Body Sensor Networks Conference, BSN 2016 - San Francisco, Estados Unidos
Duración: 14 jun. 201617 jun. 2016

Serie de la publicación

NombreBSN 2016 - 13th Annual Body Sensor Networks Conference

Conferencia

Conferencia13th Annual Body Sensor Networks Conference, BSN 2016
País/TerritorioEstados Unidos
CiudadSan Francisco
Período14/06/1617/06/16

Huella

Profundice en los temas de investigación de 'Different regressors for linear modelling of ElectroEncephaloGraphic recordings in visual and auditory tasks'. En conjunto forman una huella única.

Citar esto