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Authors: Jonghwa Kim and Elisabeth André

Affiliation: Institute of Computer Science, University of Augsburg, Germany

ISBN: 978-989-8111-18-0

Keyword(s): Biosignal, emotion recognition, physiological measures, skin conductance, electrocardiogram, electromyogram, respiration, affective computing, human-computer interaction, musical emotion, autonomic nervous system, arousal, valence, feature extraction, pattern recognition.

Related Ontology Subjects/Areas/Topics: Applications and Services ; Artificial Intelligence ; Biomedical Engineering ; Biomedical Signal Processing ; Computational Intelligence ; Computer Vision, Visualization and Computer Graphics ; Cybernetics and User Interface Technologies ; Data Manipulation ; Devices ; Health Engineering and Technology Applications ; Human-Computer Interaction ; Information and Systems Security ; Medical Image Detection, Acquisition, Analysis and Processing ; Methodologies and Methods ; Neurocomputing ; Neurotechnology, Electronics and Informatics ; Pattern Recognition ; Physiological Computing Systems ; Physiological Processes and Bio-Signal Modeling, Non-Linear Dynamics ; Sensor Networks ; Soft Computing

Abstract: This paper investigates the potential of physiological signals as a reliable channel for automatic recognition of user’s emotial state. For the emotion recognition, little attention has been paid so far to physiological signals compared to audio-visual emotion channels such as facial expression or speech. All essential stages of automatic recognition system using biosignals are discussed, from recording physiological dataset up to feature-based multiclass classification. Four-channel biosensors are used to measure electromyogram, electrocardiogram, skin conductivity and respiration changes. A wide range of physiological features from various analysis domains, including time/frequency, entropy, geometric analysis, subband spectra, multiscale entropy, etc., is proposed in order to search the best emotion-relevant features and to correlate them with emotional states. The best features extracted are specified in detail and their effectiveness is proven by emotion recognition results.

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Paper citation in several formats:
Kim J.; André E. and (2008). MULTI-CHANNEL BIOSIGNAL ANALYSIS FOR AUTOMATIC EMOTION RECOGNITION.In Proceedings of the First International Conference on Bio-inspired Systems and Signal Processing - Volume 1: BIOSIGNALS, (BIOSTEC 2008) ISBN 978-989-8111-18-0, pages 124-131. DOI: 10.5220/0001063301240131

@conference{biosignals08,
author={Jonghwa Kim and Elisabeth André},
title={MULTI-CHANNEL BIOSIGNAL ANALYSIS FOR AUTOMATIC EMOTION RECOGNITION},
booktitle={Proceedings of the First International Conference on Bio-inspired Systems and Signal Processing - Volume 1: BIOSIGNALS, (BIOSTEC 2008)},
year={2008},
pages={124-131},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0001063301240131},
isbn={978-989-8111-18-0},
}

TY - CONF

JO - Proceedings of the First International Conference on Bio-inspired Systems and Signal Processing - Volume 1: BIOSIGNALS, (BIOSTEC 2008)
TI - MULTI-CHANNEL BIOSIGNAL ANALYSIS FOR AUTOMATIC EMOTION RECOGNITION
SN - 978-989-8111-18-0
AU - Kim, J.
AU - André, E.
PY - 2008
SP - 124
EP - 131
DO - 10.5220/0001063301240131

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