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Hettich DT, Bolinger E, Matuz T, et al. EEG Responses to Auditory Stimuli for Automatic Affect Recognition. Front Neurosci. 2016;10:244doi: 10.3389/fnins.2016.00244.
Hettich, D. T., Bolinger, E., Matuz, T., Birbaumer, N., Rosenstiel, W., & Spüler, M. (2016). EEG Responses to Auditory Stimuli for Automatic Affect Recognition. Frontiers in neuroscience, 10244. https://doi.org/10.3389/fnins.2016.00244
Hettich, Dirk T, et al. "EEG Responses to Auditory Stimuli for Automatic Affect Recognition." Frontiers in neuroscience vol. 10 (2016): 244. doi: https://doi.org/10.3389/fnins.2016.00244
Hettich DT, Bolinger E, Matuz T, Birbaumer N, Rosenstiel W, Spüler M. EEG Responses to Auditory Stimuli for Automatic Affect Recognition. Front Neurosci. 2016 Jun 10;10:244. doi: 10.3389/fnins.2016.00244. eCollection 2016. PMID: 27375410; PMCID: PMC4901068.
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Front Neurosci. 2016 Jun 10;10:244. doi: 10.3389/fnins.2016.00244. eCollection 2016.

EEG Responses to Auditory Stimuli for Automatic Affect Recognition.

Frontiers in neuroscience

Dirk T Hettich, Elaina Bolinger, Tamara Matuz, Niels Birbaumer, Wolfgang Rosenstiel, Martin Spüler

Affiliations

  1. Developmental Aspects of Sleep, Memory, and Emotion, Institute of Medical Psychology and Behavioural Neurobiology, University of TübingenTübingen, Germany; Neural Interfaces and Brain Signal Decoding, Wilhelm-Schickard-Institute for Computer Science, University of TübingenTübingen, Germany.
  2. Developmental Aspects of Sleep, Memory, and Emotion, Institute of Medical Psychology and Behavioural Neurobiology, University of Tübingen Tübingen, Germany.
  3. Developmental Aspects of Sleep, Memory, and Emotion, Institute of Medical Psychology and Behavioural Neurobiology, University of TübingenTübingen, Germany; Ospedale San Camillo, IRCCSVenezia, Italy.
  4. Neural Interfaces and Brain Signal Decoding, Wilhelm-Schickard-Institute for Computer Science, University of Tübingen Tübingen, Germany.

PMID: 27375410 PMCID: PMC4901068 DOI: 10.3389/fnins.2016.00244

Abstract

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.

Keywords: affective computing; brain-computer interface; classification; event-related potential; late positive potential; machine learning; support vector machine

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