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

Carlos Mugruza-Vassallo (Lead / Corresponding author)

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

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.

Original languageEnglish
Title of host publicationBSN 2016 - 13th Annual Body Sensor Networks Conference
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages260-265
Number of pages6
ISBN (Electronic)9781509030873
DOIs
Publication statusPublished - 18 Jul 2016
Event13th Annual Body Sensor Networks Conference, BSN 2016 - San Francisco, United States
Duration: 14 Jun 201617 Jun 2016

Conference

Conference13th Annual Body Sensor Networks Conference, BSN 2016
CountryUnited States
CitySan Francisco
Period14/06/1617/06/16

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    Mugruza-Vassallo, C. (2016). Different regressors for linear modelling of ElectroEncephaloGraphic recordings in visual and auditory tasks. In BSN 2016 - 13th Annual Body Sensor Networks Conference (pp. 260-265). [7516270] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BSN.2016.7516270