University of Hertfordshire

By the same authors

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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Standard

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 Verlag, 2018. p. 504-511 (Advances in Intelligent Systems and Computing; Vol. 679).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Harvard

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 Verlag, 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

APA

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 Verlag. https://doi.org/10.1007/978-3-319-68321-8_52

Vancouver

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 Verlag. 2018. p. 504-511. (Advances in Intelligent Systems and Computing). https://doi.org/10.1007/978-3-319-68321-8_52

Author

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 Verlag, 2018. pp. 504-511 (Advances in Intelligent Systems and Computing).

Bibtex

@inproceedings{d8368ec4e0a94368b2469f60ee1d41c6,
title = "Evaluation of cepstral coefficients as features in EEG-based recognition of emotional States",
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.",
keywords = "Cepstral coefficients, EEG, Emotion recognition, LFCC features",
author = "Firgan Feradov and Iosif Mporas and Todor Ganchev",
year = "2018",
month = jan,
day = "1",
doi = "10.1007/978-3-319-68321-8_52",
language = "English",
isbn = "9783319683201",
series = "Advances in Intelligent Systems and Computing",
publisher = "Springer Verlag",
pages = "504--511",
editor = "Sergey Kovalev and Andrey Sukhanov and Margreta Vasileva and Valery Tarassov and Vaclav Snasel and Ajith Abraham",
booktitle = "Proceedings of the 2nd International Scientific Conference on Intelligent Information Technologies for Industry, IITI 2017",
address = "Germany",
note = "2nd International Conference on Intelligent Information Technologies for Industry, IITI 2017 ; Conference date: 14-09-2017 Through 16-09-2017",

}

RIS

TY - GEN

T1 - Evaluation of cepstral coefficients as features in EEG-based recognition of emotional States

AU - Feradov, Firgan

AU - Mporas, Iosif

AU - Ganchev, Todor

PY - 2018/1/1

Y1 - 2018/1/1

N2 - 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.

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.

KW - Cepstral coefficients

KW - EEG

KW - Emotion recognition

KW - LFCC features

UR - http://www.scopus.com/inward/record.url?scp=85031402223&partnerID=8YFLogxK

U2 - 10.1007/978-3-319-68321-8_52

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

M3 - Conference contribution

AN - SCOPUS:85031402223

SN - 9783319683201

T3 - Advances in Intelligent Systems and Computing

SP - 504

EP - 511

BT - Proceedings of the 2nd International Scientific Conference on Intelligent Information Technologies for Industry, IITI 2017

A2 - Kovalev, Sergey

A2 - Sukhanov, Andrey

A2 - Vasileva, Margreta

A2 - Tarassov, Valery

A2 - Snasel, Vaclav

A2 - Abraham, Ajith

PB - Springer Verlag

T2 - 2nd International Conference on Intelligent Information Technologies for Industry, IITI 2017

Y2 - 14 September 2017 through 16 September 2017

ER -