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EEG Responses to Auditory Stimuli for Automatic Affect Recognition

Brain state classification for communication and control has been well established in the area of brain-computer interfaces over the last decades. Recently, the passive and automatic extraction of additional information regarding the psychological state of users from neurophysiological signals has g...

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Autores principales: Hettich, Dirk T., Bolinger, Elaina, Matuz, Tamara, Birbaumer, Niels, Rosenstiel, Wolfgang, Spüler, Martin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4901068/
https://www.ncbi.nlm.nih.gov/pubmed/27375410
http://dx.doi.org/10.3389/fnins.2016.00244
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author Hettich, Dirk T.
Bolinger, Elaina
Matuz, Tamara
Birbaumer, Niels
Rosenstiel, Wolfgang
Spüler, Martin
author_facet Hettich, Dirk T.
Bolinger, Elaina
Matuz, Tamara
Birbaumer, Niels
Rosenstiel, Wolfgang
Spüler, Martin
author_sort Hettich, Dirk T.
collection PubMed
description Brain state classification for communication and control has been well established in the area of brain-computer interfaces over the last decades. Recently, the passive and automatic extraction of additional information regarding the psychological state of users from neurophysiological signals has gained increased attention in the interdisciplinary field of affective computing. We investigated how well specific emotional reactions, induced by auditory stimuli, can be detected in EEG recordings. We introduce an auditory emotion induction paradigm based on the International Affective Digitized Sounds 2nd Edition (IADS-2) database also suitable for disabled individuals. Stimuli are grouped in three valence categories: unpleasant, neutral, and pleasant. Significant differences in time domain domain event-related potentials are found in the electroencephalogram (EEG) between unpleasant and neutral, as well as pleasant and neutral conditions over midline electrodes. Time domain data were classified in three binary classification problems using a linear support vector machine (SVM) classifier. We discuss three classification performance measures in the context of affective computing and outline some strategies for conducting and reporting affect classification studies.
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spelling pubmed-49010682016-07-01 EEG Responses to Auditory Stimuli for Automatic Affect Recognition Hettich, Dirk T. Bolinger, Elaina Matuz, Tamara Birbaumer, Niels Rosenstiel, Wolfgang Spüler, Martin Front Neurosci Psychology Brain state classification for communication and control has been well established in the area of brain-computer interfaces over the last decades. Recently, the passive and automatic extraction of additional information regarding the psychological state of users from neurophysiological signals has gained increased attention in the interdisciplinary field of affective computing. We investigated how well specific emotional reactions, induced by auditory stimuli, can be detected in EEG recordings. We introduce an auditory emotion induction paradigm based on the International Affective Digitized Sounds 2nd Edition (IADS-2) database also suitable for disabled individuals. Stimuli are grouped in three valence categories: unpleasant, neutral, and pleasant. Significant differences in time domain domain event-related potentials are found in the electroencephalogram (EEG) between unpleasant and neutral, as well as pleasant and neutral conditions over midline electrodes. Time domain data were classified in three binary classification problems using a linear support vector machine (SVM) classifier. We discuss three classification performance measures in the context of affective computing and outline some strategies for conducting and reporting affect classification studies. Frontiers Media S.A. 2016-06-10 /pmc/articles/PMC4901068/ /pubmed/27375410 http://dx.doi.org/10.3389/fnins.2016.00244 Text en Copyright © 2016 Hettich, Bolinger, Matuz, Birbaumer, Rosenstiel and Spüler. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Psychology
Hettich, Dirk T.
Bolinger, Elaina
Matuz, Tamara
Birbaumer, Niels
Rosenstiel, Wolfgang
Spüler, Martin
EEG Responses to Auditory Stimuli for Automatic Affect Recognition
title EEG Responses to Auditory Stimuli for Automatic Affect Recognition
title_full EEG Responses to Auditory Stimuli for Automatic Affect Recognition
title_fullStr EEG Responses to Auditory Stimuli for Automatic Affect Recognition
title_full_unstemmed EEG Responses to Auditory Stimuli for Automatic Affect Recognition
title_short EEG Responses to Auditory Stimuli for Automatic Affect Recognition
title_sort eeg responses to auditory stimuli for automatic affect recognition
topic Psychology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4901068/
https://www.ncbi.nlm.nih.gov/pubmed/27375410
http://dx.doi.org/10.3389/fnins.2016.00244
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