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

Carlos Mugruza-Vassallo

doi:10.1109/BSN.2016.7516270

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

Carlos Mugruza-Vassallo (Lead / Corresponding author)

Psychology

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

6 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 language	English
Title of host publication	BSN 2016 - 13th Annual Body Sensor Networks Conference
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	260-265
Number of pages	6
ISBN (Electronic)	9781509030873
DOIs	https://doi.org/10.1109/BSN.2016.7516270
Publication status	Published - 18 Jul 2016
Event	13th Annual Body Sensor Networks Conference, BSN 2016 - San Francisco, United States Duration: 14 Jun 2016 → 17 Jun 2016

Conference

Conference	13th Annual Body Sensor Networks Conference, BSN 2016
Country/Territory	United States
City	San Francisco
Period	14/06/16 → 17/06/16

ASJC Scopus subject areas

Computer Networks and Communications
Instrumentation
Biomedical Engineering

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10.1109/BSN.2016.7516270

Cite this

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title = "Different regressors for linear modelling of ElectroEncephaloGraphic recordings in visual and auditory tasks",

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.",

author = "Carlos Mugruza-Vassallo",

year = "2016",

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doi = "10.1109/BSN.2016.7516270",

language = "English",

pages = "260--265",

booktitle = "BSN 2016 - 13th Annual Body Sensor Networks Conference",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

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note = "13th Annual Body Sensor Networks Conference, BSN 2016 ; Conference date: 14-06-2016 Through 17-06-2016",

}

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., 7516270, Institute of Electrical and Electronics Engineers Inc., pp. 260-265, 13th Annual Body Sensor Networks Conference, BSN 2016, San Francisco, United States, 14/06/16. https://doi.org/10.1109/BSN.2016.7516270

Different regressors for linear modelling of ElectroEncephaloGraphic recordings in visual and auditory tasks. / Mugruza-Vassallo, Carlos (Lead / Corresponding author).
BSN 2016 - 13th Annual Body Sensor Networks Conference. Institute of Electrical and Electronics Engineers Inc., 2016. p. 260-265 7516270.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Different regressors for linear modelling of ElectroEncephaloGraphic recordings in visual and auditory tasks

AU - Mugruza-Vassallo, Carlos

PY - 2016/7/18

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N2 - 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.

AB - 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.

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Y2 - 14 June 2016 through 17 June 2016

ER -

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

Abstract

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ASJC Scopus subject areas

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