Evaluation of cepstral coefficients as features in EEG-based recognition of emotional States

Firgan Feradov; Iosif Mporas; Todor Ganchev

doi:10.1007/978-3-319-68321-8_52

Evaluation of cepstral coefficients as features in EEG-based recognition of emotional States

Firgan Feradov, Iosif Mporas, Todor Ganchev

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

2 Citations (Scopus)

Abstract

The study of physiological signals and the evaluation of their features are of great importance for the automated emotion detection, as these are directly connected with the successful modelling and classification of the states of interest. In the presented work, we present an evaluation of the appropriateness of LFCC and the logarithmic energy of signals as features for automated recognition of negative emotional states in terms of recognition accuracy. In particular, three sets of features are compared – features computed after frame-level segmentation of the signal; features computed after averaging of frame level descriptors; and features extracted from an entire EEG recording. The performance of the extracted features is evaluated using C4.5 classifier for 10, 15, 20, 30, 45, and 60 filters.

Original language	English
Title of host publication	Proceedings of the 2nd International Scientific Conference on Intelligent Information Technologies for Industry, IITI 2017
Editors	Sergey Kovalev, Andrey Sukhanov, Margreta Vasileva, Valery Tarassov, Vaclav Snasel, Ajith Abraham
Publisher	Springer Nature
Pages	504-511
Number of pages	8
ISBN (Print)	9783319683201
DOIs	https://doi.org/10.1007/978-3-319-68321-8_52
Publication status	Published - 1 Jan 2018
Event	2nd International Conference on Intelligent Information Technologies for Industry, IITI 2017 - Varna, Bulgaria Duration: 14 Sept 2017 → 16 Sept 2017

Publication series

Name	Advances in Intelligent Systems and Computing
Volume	679
ISSN (Print)	2194-5357

Conference

Conference	2nd International Conference on Intelligent Information Technologies for Industry, IITI 2017
Country/Territory	Bulgaria
City	Varna
Period	14/09/17 → 16/09/17

Keywords

Cepstral coefficients
EEG
Emotion recognition
LFCC features

Access to Document

10.1007/978-3-319-68321-8_52

Cite this

Feradov, F., Mporas, I., & Ganchev, T. (2018). Evaluation of cepstral coefficients as features in EEG-based recognition of emotional States. In S. Kovalev, A. Sukhanov, M. Vasileva, V. Tarassov, V. Snasel, & A. Abraham (Eds.), Proceedings of the 2nd International Scientific Conference on Intelligent Information Technologies for Industry, IITI 2017 (pp. 504-511). (Advances in Intelligent Systems and Computing; Vol. 679). Springer Nature. https://doi.org/10.1007/978-3-319-68321-8_52

Feradov, Firgan ; Mporas, Iosif ; Ganchev, Todor. / Evaluation of cepstral coefficients as features in EEG-based recognition of emotional States. Proceedings of the 2nd International Scientific Conference on Intelligent Information Technologies for Industry, IITI 2017. editor / Sergey Kovalev ; Andrey Sukhanov ; Margreta Vasileva ; Valery Tarassov ; Vaclav Snasel ; Ajith Abraham. Springer Nature, 2018. pp. 504-511 (Advances in Intelligent Systems and Computing).

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abstract = "The study of physiological signals and the evaluation of their features are of great importance for the automated emotion detection, as these are directly connected with the successful modelling and classification of the states of interest. In the presented work, we present an evaluation of the appropriateness of LFCC and the logarithmic energy of signals as features for automated recognition of negative emotional states in terms of recognition accuracy. In particular, three sets of features are compared – features computed after frame-level segmentation of the signal; features computed after averaging of frame level descriptors; and features extracted from an entire EEG recording. The performance of the extracted features is evaluated using C4.5 classifier for 10, 15, 20, 30, 45, and 60 filters.",

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Feradov, F, Mporas, I & Ganchev, T 2018, Evaluation of cepstral coefficients as features in EEG-based recognition of emotional States. in S Kovalev, A Sukhanov, M Vasileva, V Tarassov, V Snasel & A Abraham (eds), Proceedings of the 2nd International Scientific Conference on Intelligent Information Technologies for Industry, IITI 2017. Advances in Intelligent Systems and Computing, vol. 679, Springer Nature, pp. 504-511, 2nd International Conference on Intelligent Information Technologies for Industry, IITI 2017, Varna, Bulgaria, 14/09/17. https://doi.org/10.1007/978-3-319-68321-8_52

Evaluation of cepstral coefficients as features in EEG-based recognition of emotional States. / Feradov, Firgan; Mporas, Iosif; Ganchev, Todor.
Proceedings of the 2nd International Scientific Conference on Intelligent Information Technologies for Industry, IITI 2017. ed. / Sergey Kovalev; Andrey Sukhanov; Margreta Vasileva; Valery Tarassov; Vaclav Snasel; Ajith Abraham. Springer Nature, 2018. p. 504-511 (Advances in Intelligent Systems and Computing; Vol. 679).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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AB - The study of physiological signals and the evaluation of their features are of great importance for the automated emotion detection, as these are directly connected with the successful modelling and classification of the states of interest. In the presented work, we present an evaluation of the appropriateness of LFCC and the logarithmic energy of signals as features for automated recognition of negative emotional states in terms of recognition accuracy. In particular, three sets of features are compared – features computed after frame-level segmentation of the signal; features computed after averaging of frame level descriptors; and features extracted from an entire EEG recording. The performance of the extracted features is evaluated using C4.5 classifier for 10, 15, 20, 30, 45, and 60 filters.

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Feradov F, Mporas I, Ganchev T. Evaluation of cepstral coefficients as features in EEG-based recognition of emotional States. In Kovalev S, Sukhanov A, Vasileva M, Tarassov V, Snasel V, Abraham A, editors, Proceedings of the 2nd International Scientific Conference on Intelligent Information Technologies for Industry, IITI 2017. Springer Nature. 2018. p. 504-511. (Advances in Intelligent Systems and Computing). doi: 10.1007/978-3-319-68321-8_52

Evaluation of cepstral coefficients as features in EEG-based recognition of emotional States

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Keywords

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