A Novel Multidimensional Feature Extraction Method Based on VMD and WPD for Emotion Recognition

Min Zhang; Bin Hu; Xiangwei Zheng; Tiantian Li

doi:10.1109/BIBM49941.2020.9313220

A Novel Multidimensional Feature Extraction Method Based on VMD and WPD for Emotion Recognition

Min Zhang
, Bin Hu
, Xiangwei Zheng
, Tiantian Li

Shandong Normal University

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

12 Citations (Scopus)

Abstract

Emotion plays an indispensable role in the process of human cognition and decision-making, but it is often neglected in human-computer interaction (HCI). Although many researchers have applied some decomposition algorithms to extract features from EEG, there are several shortcomings including the modal aliasing, a high computational cost and so on. In this paper, we introduce variational mode decomposition (VMD) and wavelet packet decomposition (WPD) algorithms and propose a multidimensional feature extraction method based on VMD and WPD for emotion recognition. Firstly, we apply VMD to decompose the EEG signal into a specific number of variational mode functions (VMF). Secondly, WPD is executed to generate an emotional frequency band. Then, we continue to extract the wavelet packet entropy (WPE), modified multi-scale sample entropy (MMSE), fractal dimension (FD) and first difference (1ST) of each emotional VMF to construct a new feature form. Finally, the random forest (RF) is utilized to classify the extracted emotional states. The experiment results demonstrate that the proposed method is more competitive and universal for emotion recognition, which provides a novel idea for the application of multidimensional feature extraction.

Original language	English
Title of host publication	Proceedings - 2020 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2020
Editors	Taesung Park, Young-Rae Cho, Xiaohua Tony Hu, Illhoi Yoo, Hyun Goo Woo, Jianxin Wang, Julio Facelli, Seungyoon Nam, Mingon Kang
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1216-1220
Number of pages	5
ISBN (Electronic)	9781728162157
DOIs	https://doi.org/10.1109/BIBM49941.2020.9313220
Publication status	Published - 16 Dec 2020
Externally published	Yes
Event	2020 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2020 - Virtual, Seoul, Korea, Republic of Duration: 16 Dec 2020 → 19 Dec 2020

Publication series

Name	Proceedings - 2020 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2020

Conference

Conference	2020 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2020
Country/Territory	Korea, Republic of
City	Virtual, Seoul
Period	16/12/20 → 19/12/20

Keywords

EEG
Emotion recognition
Feature extraction
Variational Mode Decomposition
Wavelet Packet Decomposition

Access to Document

10.1109/BIBM49941.2020.9313220

Cite this

Zhang, M., Hu, B., Zheng, X., & Li, T. (2020). A Novel Multidimensional Feature Extraction Method Based on VMD and WPD for Emotion Recognition. In T. Park, Y.-R. Cho, X. T. Hu, I. Yoo, H. G. Woo, J. Wang, J. Facelli, S. Nam, & M. Kang (Eds.), Proceedings - 2020 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2020 (pp. 1216-1220). Article 9313220 (Proceedings - 2020 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2020). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BIBM49941.2020.9313220

Zhang, Min ; Hu, Bin ; Zheng, Xiangwei et al. / A Novel Multidimensional Feature Extraction Method Based on VMD and WPD for Emotion Recognition. Proceedings - 2020 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2020. editor / Taesung Park ; Young-Rae Cho ; Xiaohua Tony Hu ; Illhoi Yoo ; Hyun Goo Woo ; Jianxin Wang ; Julio Facelli ; Seungyoon Nam ; Mingon Kang. Institute of Electrical and Electronics Engineers Inc., 2020. pp. 1216-1220 (Proceedings - 2020 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2020).

@inproceedings{6441b87e43a54980a7c981a3f4b30308,

title = "A Novel Multidimensional Feature Extraction Method Based on VMD and WPD for Emotion Recognition",

abstract = "Emotion plays an indispensable role in the process of human cognition and decision-making, but it is often neglected in human-computer interaction (HCI). Although many researchers have applied some decomposition algorithms to extract features from EEG, there are several shortcomings including the modal aliasing, a high computational cost and so on. In this paper, we introduce variational mode decomposition (VMD) and wavelet packet decomposition (WPD) algorithms and propose a multidimensional feature extraction method based on VMD and WPD for emotion recognition. Firstly, we apply VMD to decompose the EEG signal into a specific number of variational mode functions (VMF). Secondly, WPD is executed to generate an emotional frequency band. Then, we continue to extract the wavelet packet entropy (WPE), modified multi-scale sample entropy (MMSE), fractal dimension (FD) and first difference (1ST) of each emotional VMF to construct a new feature form. Finally, the random forest (RF) is utilized to classify the extracted emotional states. The experiment results demonstrate that the proposed method is more competitive and universal for emotion recognition, which provides a novel idea for the application of multidimensional feature extraction.",

keywords = "EEG, Emotion recognition, Feature extraction, Variational Mode Decomposition, Wavelet Packet Decomposition",

author = "Min Zhang and Bin Hu and Xiangwei Zheng and Tiantian Li",

note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 2020 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2020 ; Conference date: 16-12-2020 Through 19-12-2020",

year = "2020",

month = dec,

day = "16",

doi = "10.1109/BIBM49941.2020.9313220",

language = "English",

series = "Proceedings - 2020 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2020",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "1216--1220",

editor = "Taesung Park and Young-Rae Cho and Hu, \{Xiaohua Tony\} and Illhoi Yoo and Woo, \{Hyun Goo\} and Jianxin Wang and Julio Facelli and Seungyoon Nam and Mingon Kang",

booktitle = "Proceedings - 2020 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2020",

address = "United States",

}

Zhang, M, Hu, B, Zheng, X & Li, T 2020, A Novel Multidimensional Feature Extraction Method Based on VMD and WPD for Emotion Recognition. in T Park, Y-R Cho, XT Hu, I Yoo, HG Woo, J Wang, J Facelli, S Nam & M Kang (eds), Proceedings - 2020 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2020., 9313220, Proceedings - 2020 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2020, Institute of Electrical and Electronics Engineers Inc., pp. 1216-1220, 2020 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2020, Virtual, Seoul, Korea, Republic of, 16/12/20. https://doi.org/10.1109/BIBM49941.2020.9313220

A Novel Multidimensional Feature Extraction Method Based on VMD and WPD for Emotion Recognition. / Zhang, Min; Hu, Bin; Zheng, Xiangwei et al.
Proceedings - 2020 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2020. ed. / Taesung Park; Young-Rae Cho; Xiaohua Tony Hu; Illhoi Yoo; Hyun Goo Woo; Jianxin Wang; Julio Facelli; Seungyoon Nam; Mingon Kang. Institute of Electrical and Electronics Engineers Inc., 2020. p. 1216-1220 9313220 (Proceedings - 2020 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2020).

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

TY - GEN

T1 - A Novel Multidimensional Feature Extraction Method Based on VMD and WPD for Emotion Recognition

AU - Zhang, Min

AU - Hu, Bin

AU - Zheng, Xiangwei

AU - Li, Tiantian

PY - 2020/12/16

Y1 - 2020/12/16

N2 - Emotion plays an indispensable role in the process of human cognition and decision-making, but it is often neglected in human-computer interaction (HCI). Although many researchers have applied some decomposition algorithms to extract features from EEG, there are several shortcomings including the modal aliasing, a high computational cost and so on. In this paper, we introduce variational mode decomposition (VMD) and wavelet packet decomposition (WPD) algorithms and propose a multidimensional feature extraction method based on VMD and WPD for emotion recognition. Firstly, we apply VMD to decompose the EEG signal into a specific number of variational mode functions (VMF). Secondly, WPD is executed to generate an emotional frequency band. Then, we continue to extract the wavelet packet entropy (WPE), modified multi-scale sample entropy (MMSE), fractal dimension (FD) and first difference (1ST) of each emotional VMF to construct a new feature form. Finally, the random forest (RF) is utilized to classify the extracted emotional states. The experiment results demonstrate that the proposed method is more competitive and universal for emotion recognition, which provides a novel idea for the application of multidimensional feature extraction.

AB - Emotion plays an indispensable role in the process of human cognition and decision-making, but it is often neglected in human-computer interaction (HCI). Although many researchers have applied some decomposition algorithms to extract features from EEG, there are several shortcomings including the modal aliasing, a high computational cost and so on. In this paper, we introduce variational mode decomposition (VMD) and wavelet packet decomposition (WPD) algorithms and propose a multidimensional feature extraction method based on VMD and WPD for emotion recognition. Firstly, we apply VMD to decompose the EEG signal into a specific number of variational mode functions (VMF). Secondly, WPD is executed to generate an emotional frequency band. Then, we continue to extract the wavelet packet entropy (WPE), modified multi-scale sample entropy (MMSE), fractal dimension (FD) and first difference (1ST) of each emotional VMF to construct a new feature form. Finally, the random forest (RF) is utilized to classify the extracted emotional states. The experiment results demonstrate that the proposed method is more competitive and universal for emotion recognition, which provides a novel idea for the application of multidimensional feature extraction.

KW - EEG

KW - Emotion recognition

KW - Feature extraction

KW - Variational Mode Decomposition

KW - Wavelet Packet Decomposition

UR - https://www.scopus.com/pages/publications/85100329291

U2 - 10.1109/BIBM49941.2020.9313220

DO - 10.1109/BIBM49941.2020.9313220

M3 - Conference contribution

AN - SCOPUS:85100329291

T3 - Proceedings - 2020 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2020

SP - 1216

EP - 1220

BT - Proceedings - 2020 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2020

A2 - Park, Taesung

A2 - Cho, Young-Rae

A2 - Hu, Xiaohua Tony

A2 - Yoo, Illhoi

A2 - Woo, Hyun Goo

A2 - Wang, Jianxin

A2 - Facelli, Julio

A2 - Nam, Seungyoon

A2 - Kang, Mingon

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2020 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2020

Y2 - 16 December 2020 through 19 December 2020

ER -

Zhang M, Hu B, Zheng X, Li T. A Novel Multidimensional Feature Extraction Method Based on VMD and WPD for Emotion Recognition. In Park T, Cho YR, Hu XT, Yoo I, Woo HG, Wang J, Facelli J, Nam S, Kang M, editors, Proceedings - 2020 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2020. Institute of Electrical and Electronics Engineers Inc. 2020. p. 1216-1220. 9313220. (Proceedings - 2020 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2020). doi: 10.1109/BIBM49941.2020.9313220

A Novel Multidimensional Feature Extraction Method Based on VMD and WPD for Emotion Recognition

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Keywords

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