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Authors: A. M. Tomé 1 ; A. R. Hidalgo-Muñoz 2 ; M. M. López 1 ; A. R. Teixeira 1 ; I. M. Santos 1 ; A. T. Pereira 1 ; M. Vázquez-Marrufo 2 and E. W. Lang 3

Affiliations: 1 University of Aveiro, Portugal ; 2 University of Seville, Spain ; 3 University of Regensburg, Germany

ISBN: 978-989-8565-36-5

Keyword(s): Valence Detection, Random Forest, ERD/ERS.

Related Ontology Subjects/Areas/Topics: Applications and Services ; Artificial Intelligence ; Biomedical Engineering ; Biomedical Signal Processing ; Computer Vision, Visualization and Computer Graphics ; Data Manipulation ; Health Engineering and Technology Applications ; Human-Computer Interaction ; Medical Image Detection, Acquisition, Analysis and Processing ; Methodologies and Methods ; Neurocomputing ; Neurotechnology, Electronics and Informatics ; Pattern Recognition ; Physiological Computing Systems ; Sensor Networks ; Soft Computing

Abstract: In this work a valence recognition system based on electroencephalograms is presented. The performance of the system is evaluated for two settings: single subjects (intra-subject) and between subjects (inter-subject). The feature extraction is based on measures of relative energies computed in short time intervals and certain frequency bands. The feature extraction is performed either on signals averaged over an ensemble of trials or on single-trial response signals. The subsequent classification stage is based on an ensemble classifier, i. e. a random forest of tree classifiers. The classification is performed considering the ensemble average responses of all subjects (inter-subject) or considering the single-trial responses of single subjects (intra-subject). Applying a proper importance measure of the classifier, feature elimination has been used to identify the most relevant features of the decision making.

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Paper citation in several formats:
M. Tomé, A.; R. Hidalgo-Muñoz, A.; M. López, M.; R. Teixeira, A.; M. Santos, I.; T. Pereira, A.; Vázquez-Marrufo, M. and W. Lang, E. (2013). Feature Extraction and Classification of Biosignals - Emotion Valence Detection from EEG Signals.In Proceedings of the International Conference on Bio-inspired Systems and Signal Processing - Volume 1: BIOSIGNALS, (BIOSTEC 2013) ISBN 978-989-8565-36-5, pages 54-60. DOI: 10.5220/0004233100540060

@conference{biosignals13,
author={A. M. Tomé. and A. R. Hidalgo{-}Muñoz. and M. M. López. and A. R. Teixeira. and I. M. Santos. and A. T. Pereira. and M. Vázquez{-}Marrufo. and E. W. Lang.},
title={Feature Extraction and Classification of Biosignals - Emotion Valence Detection from EEG Signals},
booktitle={Proceedings of the International Conference on Bio-inspired Systems and Signal Processing - Volume 1: BIOSIGNALS, (BIOSTEC 2013)},
year={2013},
pages={54-60},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004233100540060},
isbn={978-989-8565-36-5},
}

TY - CONF

JO - Proceedings of the International Conference on Bio-inspired Systems and Signal Processing - Volume 1: BIOSIGNALS, (BIOSTEC 2013)
TI - Feature Extraction and Classification of Biosignals - Emotion Valence Detection from EEG Signals
SN - 978-989-8565-36-5
AU - M. Tomé, A.
AU - R. Hidalgo-Muñoz, A.
AU - M. López, M.
AU - R. Teixeira, A.
AU - M. Santos, I.
AU - T. Pereira, A.
AU - Vázquez-Marrufo, M.
AU - W. Lang, E.
PY - 2013
SP - 54
EP - 60
DO - 10.5220/0004233100540060

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