Enhancing performance of EEG based emotion recognition systems using feature smoothing

Trung Duy PHAM, Dat TRAN, Wanli MA, Nga Tran

Research output: A Conference proceeding or a Chapter in BookConference contribution

6 Citations (Scopus)

Abstract

Electroencephalography (EEG) has been used recently in emotion recognition. However, the drawback of current EEG-based emotion recognition systems is that the correlation between EEG and emotion characteristics is not taken into account. There are the differences among EEG features, even with the same emotion state in adjacent time because EEG extracted features usually change dramatically, while emotion states vary gradually or smoothly. In addition, EEG signals are very weak and subject to contamination from many artefact signals, thus leading to an accuracy reduction of emotion recognition systems. In this paper, we study on feature smoothing on EEG-based Emotion Recognition Model to overcome those disadvantages. The proposed methodology was examined on two useful kinds of features: power spectral density (PSD) and autoregressive (AR) for two-level class and three-level class using DEAP database. Our experimental results showed that feature smoothing affects to both the feature sets, and increases the emotion recognition accuracy. The highest accuracies are 77.38 % for two-level classes and 71.75 % for three-level classes, respectively in valence space.
Original languageEnglish
Title of host publicationInternational Conference on Neural Information processing (ICONIP 2015)
Subtitle of host publicationLecture notes in computer science
EditorsSabri Arik, Tingwen Huang, Weng Kin Lai, Qingshan Liu
Place of PublicationSwitzerland
PublisherSpringer
Pages95-102
Number of pages8
Volume9492
ISBN (Electronic)9783319265612
ISBN (Print)9783319265605
DOIs
Publication statusPublished - 18 Nov 2015
Event22nd International Conference on Neural Information Processing ICONIP 2015 - Istanbul, Istanbul, Turkey
Duration: 9 Nov 201512 Nov 2015

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9492
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference22nd International Conference on Neural Information Processing ICONIP 2015
Abbreviated titleICONIP 2015
CountryTurkey
CityIstanbul
Period9/11/1512/11/15

Fingerprint

Electroencephalography
Power spectral density
Contamination

Cite this

PHAM, T. D., TRAN, D., MA, W., & Tran, N. (2015). Enhancing performance of EEG based emotion recognition systems using feature smoothing. In S. Arik, T. Huang, W. K. Lai, & Q. Liu (Eds.), International Conference on Neural Information processing (ICONIP 2015): Lecture notes in computer science (Vol. 9492, pp. 95-102). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9492). Switzerland: Springer. https://doi.org/10.1007/978-3-319-26561-2_12
PHAM, Trung Duy ; TRAN, Dat ; MA, Wanli ; Tran, Nga. / Enhancing performance of EEG based emotion recognition systems using feature smoothing. International Conference on Neural Information processing (ICONIP 2015): Lecture notes in computer science. editor / Sabri Arik ; Tingwen Huang ; Weng Kin Lai ; Qingshan Liu. Vol. 9492 Switzerland : Springer, 2015. pp. 95-102 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
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title = "Enhancing performance of EEG based emotion recognition systems using feature smoothing",
abstract = "Electroencephalography (EEG) has been used recently in emotion recognition. However, the drawback of current EEG-based emotion recognition systems is that the correlation between EEG and emotion characteristics is not taken into account. There are the differences among EEG features, even with the same emotion state in adjacent time because EEG extracted features usually change dramatically, while emotion states vary gradually or smoothly. In addition, EEG signals are very weak and subject to contamination from many artefact signals, thus leading to an accuracy reduction of emotion recognition systems. In this paper, we study on feature smoothing on EEG-based Emotion Recognition Model to overcome those disadvantages. The proposed methodology was examined on two useful kinds of features: power spectral density (PSD) and autoregressive (AR) for two-level class and three-level class using DEAP database. Our experimental results showed that feature smoothing affects to both the feature sets, and increases the emotion recognition accuracy. The highest accuracies are 77.38 {\%} for two-level classes and 71.75 {\%} for three-level classes, respectively in valence space.",
keywords = "EEG, Emotion recognition, Feature smoothing, Saviztky-Golay",
author = "PHAM, {Trung Duy} and Dat TRAN and Wanli MA and Nga Tran",
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PHAM, TD, TRAN, D, MA, W & Tran, N 2015, Enhancing performance of EEG based emotion recognition systems using feature smoothing. in S Arik, T Huang, WK Lai & Q Liu (eds), International Conference on Neural Information processing (ICONIP 2015): Lecture notes in computer science. vol. 9492, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9492, Springer, Switzerland, pp. 95-102, 22nd International Conference on Neural Information Processing ICONIP 2015, Istanbul, Turkey, 9/11/15. https://doi.org/10.1007/978-3-319-26561-2_12

Enhancing performance of EEG based emotion recognition systems using feature smoothing. / PHAM, Trung Duy; TRAN, Dat; MA, Wanli; Tran, Nga.

International Conference on Neural Information processing (ICONIP 2015): Lecture notes in computer science. ed. / Sabri Arik; Tingwen Huang; Weng Kin Lai; Qingshan Liu. Vol. 9492 Switzerland : Springer, 2015. p. 95-102 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9492).

Research output: A Conference proceeding or a Chapter in BookConference contribution

TY - GEN

T1 - Enhancing performance of EEG based emotion recognition systems using feature smoothing

AU - PHAM, Trung Duy

AU - TRAN, Dat

AU - MA, Wanli

AU - Tran, Nga

PY - 2015/11/18

Y1 - 2015/11/18

N2 - Electroencephalography (EEG) has been used recently in emotion recognition. However, the drawback of current EEG-based emotion recognition systems is that the correlation between EEG and emotion characteristics is not taken into account. There are the differences among EEG features, even with the same emotion state in adjacent time because EEG extracted features usually change dramatically, while emotion states vary gradually or smoothly. In addition, EEG signals are very weak and subject to contamination from many artefact signals, thus leading to an accuracy reduction of emotion recognition systems. In this paper, we study on feature smoothing on EEG-based Emotion Recognition Model to overcome those disadvantages. The proposed methodology was examined on two useful kinds of features: power spectral density (PSD) and autoregressive (AR) for two-level class and three-level class using DEAP database. Our experimental results showed that feature smoothing affects to both the feature sets, and increases the emotion recognition accuracy. The highest accuracies are 77.38 % for two-level classes and 71.75 % for three-level classes, respectively in valence space.

AB - Electroencephalography (EEG) has been used recently in emotion recognition. However, the drawback of current EEG-based emotion recognition systems is that the correlation between EEG and emotion characteristics is not taken into account. There are the differences among EEG features, even with the same emotion state in adjacent time because EEG extracted features usually change dramatically, while emotion states vary gradually or smoothly. In addition, EEG signals are very weak and subject to contamination from many artefact signals, thus leading to an accuracy reduction of emotion recognition systems. In this paper, we study on feature smoothing on EEG-based Emotion Recognition Model to overcome those disadvantages. The proposed methodology was examined on two useful kinds of features: power spectral density (PSD) and autoregressive (AR) for two-level class and three-level class using DEAP database. Our experimental results showed that feature smoothing affects to both the feature sets, and increases the emotion recognition accuracy. The highest accuracies are 77.38 % for two-level classes and 71.75 % for three-level classes, respectively in valence space.

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VL - 9492

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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BT - International Conference on Neural Information processing (ICONIP 2015)

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A2 - Huang, Tingwen

A2 - Lai, Weng Kin

A2 - Liu, Qingshan

PB - Springer

CY - Switzerland

ER -

PHAM TD, TRAN D, MA W, Tran N. Enhancing performance of EEG based emotion recognition systems using feature smoothing. In Arik S, Huang T, Lai WK, Liu Q, editors, International Conference on Neural Information processing (ICONIP 2015): Lecture notes in computer science. Vol. 9492. Switzerland: Springer. 2015. p. 95-102. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-319-26561-2_12