Classification of Emotion Stimulation via Iranian Music Using Sparse Representation of EEG Signal

DOI

10.2991/978-94-6463-094-7_11How to use a DOI?

Keywords

Emotion classification; EEG signal; Compressed sensing; Dictionary learning; Sparse representation; Update dictionary; Classification

Abstract

To interpret actions and communications in a correct way, emotion is very crucial. Emotion class recognition capability without using conventional approaches such as Self-Assessment Manikin (SAM) has been provided by Emotion Recognition EEG. Emotion Recognition with no medical and clinical examinations, as another merit for the EEG method, plays a key role in the completion of the structure of the Brain Computer Interface (BCI). One of the major challenges in this field is the selection of proper features of EEG signals in a way that makes an acceptable change among different emotion classes. Another challenge is the selection of a suitable classifier labeling algorithm for correct labeling and segregation of signals of every class. This article proposes a method based on compressed sensing (CS) theory, which resolves the mentioned challenges and provides the classifier performance results in accordance with sparse representation-based classification (SRC). Furthermore, recognition is assumed for two positive and negative classes according to valence-arousal emotion model (two of the three valence-arousal-dominance spaces). The results of the proposed method on the laboratory signal recorded by stimulating Iranian music show that the proposed method can compete with previous methods.

Copyright

© 2022 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Mohammad Abdollahi
AU  - Saeed Meshgini
AU  - Reza Afrouzian
AU  - Ali Farzamnia
PY  - 2022
DA  - 2022/12/27
TI  - Classification of Emotion Stimulation via Iranian Music Using Sparse Representation of EEG Signal
BT  - Proceedings of the International Conference on Computer, Information Technology and Intelligent Computing (CITIC 2022)
PB  - Atlantis Press
SP  - 133
EP  - 144
SN  - 2589-4900
UR  - https://doi.org/10.2991/978-94-6463-094-7_11
DO  - 10.2991/978-94-6463-094-7_11
ID  - Abdollahi2022
ER  -