Attention-Temporal Convolutional Networks for EEG-based Emotion Recognition

Danho Kim; Haneul Kim; Chang Gyun Jin; Seong Eun Kim

doi:10.1109/ICCE-Asia59966.2023.10326369

Attention-Temporal Convolutional Networks for EEG-based Emotion Recognition

Danho Kim, Haneul Kim, Chang Gyun Jin, Seong Eun Kim

Dept. of Applied Artificial Intelligence

Seoul National University of Science and Technology (SNUST)

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

Abstract

Emotion recognition is an emerging technology that employs various types of data modalities to detect and interpret human emotional states. Among them, electroencephalogram (EEG) has gained attention for its ability to capture underlying emotions related to specific brain activities. In this study, we adopt the attention-temporal convolutional network (ATCNet), previously recognized for its efficacy in decoding motor imagery EEG data, to develop a high-performance emotion recognition method. Our modified ATCNet achieved an average accuracy of 95.71% using the SEED dataset, which has three emotional classes, including positive, negative, and neutral.

Original language	English
Title of host publication	2023 IEEE International Conference on Consumer Electronics-Asia, ICCE-Asia 2023
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9798350344318
DOIs	https://doi.org/10.1109/ICCE-Asia59966.2023.10326369
State	Published - 2023
Event	2023 IEEE International Conference on Consumer Electronics-Asia, ICCE-Asia 2023 - Busan, Korea, Republic of Duration: 23 Oct 2023 → 25 Oct 2023

Publication series

Name	2023 IEEE International Conference on Consumer Electronics-Asia, ICCE-Asia 2023

Conference

Conference	2023 IEEE International Conference on Consumer Electronics-Asia, ICCE-Asia 2023
Country/Territory	Korea, Republic of
City	Busan
Period	23/10/23 → 25/10/23

Keywords

ATCNet
Classification
Deep learning
EEG
Emotion recognition
SEED

Access to Document

10.1109/ICCE-Asia59966.2023.10326369

Cite this

Kim, D., Kim, H., Jin, C. G., & Kim, S. E. (2023). Attention-Temporal Convolutional Networks for EEG-based Emotion Recognition. In 2023 IEEE International Conference on Consumer Electronics-Asia, ICCE-Asia 2023 (2023 IEEE International Conference on Consumer Electronics-Asia, ICCE-Asia 2023). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICCE-Asia59966.2023.10326369

@inproceedings{8045bb156d2a4f5cb2477cd534736a28,

title = "Attention-Temporal Convolutional Networks for EEG-based Emotion Recognition",

abstract = "Emotion recognition is an emerging technology that employs various types of data modalities to detect and interpret human emotional states. Among them, electroencephalogram (EEG) has gained attention for its ability to capture underlying emotions related to specific brain activities. In this study, we adopt the attention-temporal convolutional network (ATCNet), previously recognized for its efficacy in decoding motor imagery EEG data, to develop a high-performance emotion recognition method. Our modified ATCNet achieved an average accuracy of 95.71\% using the SEED dataset, which has three emotional classes, including positive, negative, and neutral.",

keywords = "ATCNet, Classification, Deep learning, EEG, Emotion recognition, SEED",

author = "Danho Kim and Haneul Kim and Jin, \{Chang Gyun\} and Kim, \{Seong Eun\}",

note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 2023 IEEE International Conference on Consumer Electronics-Asia, ICCE-Asia 2023 ; Conference date: 23-10-2023 Through 25-10-2023",

year = "2023",

doi = "10.1109/ICCE-Asia59966.2023.10326369",

language = "English",

series = "2023 IEEE International Conference on Consumer Electronics-Asia, ICCE-Asia 2023",

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

booktitle = "2023 IEEE International Conference on Consumer Electronics-Asia, ICCE-Asia 2023",

}

Kim, D, Kim, H, Jin, CG & Kim, SE 2023, Attention-Temporal Convolutional Networks for EEG-based Emotion Recognition. in 2023 IEEE International Conference on Consumer Electronics-Asia, ICCE-Asia 2023. 2023 IEEE International Conference on Consumer Electronics-Asia, ICCE-Asia 2023, Institute of Electrical and Electronics Engineers Inc., 2023 IEEE International Conference on Consumer Electronics-Asia, ICCE-Asia 2023, Busan, Korea, Republic of, 23/10/23. https://doi.org/10.1109/ICCE-Asia59966.2023.10326369

Attention-Temporal Convolutional Networks for EEG-based Emotion Recognition. / Kim, Danho; Kim, Haneul; Jin, Chang Gyun et al.
2023 IEEE International Conference on Consumer Electronics-Asia, ICCE-Asia 2023. Institute of Electrical and Electronics Engineers Inc., 2023. (2023 IEEE International Conference on Consumer Electronics-Asia, ICCE-Asia 2023).

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

TY - GEN

T1 - Attention-Temporal Convolutional Networks for EEG-based Emotion Recognition

AU - Kim, Danho

AU - Kim, Haneul

AU - Jin, Chang Gyun

AU - Kim, Seong Eun

PY - 2023

Y1 - 2023

N2 - Emotion recognition is an emerging technology that employs various types of data modalities to detect and interpret human emotional states. Among them, electroencephalogram (EEG) has gained attention for its ability to capture underlying emotions related to specific brain activities. In this study, we adopt the attention-temporal convolutional network (ATCNet), previously recognized for its efficacy in decoding motor imagery EEG data, to develop a high-performance emotion recognition method. Our modified ATCNet achieved an average accuracy of 95.71% using the SEED dataset, which has three emotional classes, including positive, negative, and neutral.

AB - Emotion recognition is an emerging technology that employs various types of data modalities to detect and interpret human emotional states. Among them, electroencephalogram (EEG) has gained attention for its ability to capture underlying emotions related to specific brain activities. In this study, we adopt the attention-temporal convolutional network (ATCNet), previously recognized for its efficacy in decoding motor imagery EEG data, to develop a high-performance emotion recognition method. Our modified ATCNet achieved an average accuracy of 95.71% using the SEED dataset, which has three emotional classes, including positive, negative, and neutral.

KW - ATCNet

KW - Classification

KW - Deep learning

KW - EEG

KW - Emotion recognition

KW - SEED

UR - https://www.scopus.com/pages/publications/85179882410

U2 - 10.1109/ICCE-Asia59966.2023.10326369

DO - 10.1109/ICCE-Asia59966.2023.10326369

M3 - Conference contribution

AN - SCOPUS:85179882410

T3 - 2023 IEEE International Conference on Consumer Electronics-Asia, ICCE-Asia 2023

BT - 2023 IEEE International Conference on Consumer Electronics-Asia, ICCE-Asia 2023

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2023 IEEE International Conference on Consumer Electronics-Asia, ICCE-Asia 2023

Y2 - 23 October 2023 through 25 October 2023

ER -

Kim D, Kim H, Jin CG, Kim SE. Attention-Temporal Convolutional Networks for EEG-based Emotion Recognition. In 2023 IEEE International Conference on Consumer Electronics-Asia, ICCE-Asia 2023. Institute of Electrical and Electronics Engineers Inc. 2023. (2023 IEEE International Conference on Consumer Electronics-Asia, ICCE-Asia 2023). doi: 10.1109/ICCE-Asia59966.2023.10326369

Attention-Temporal Convolutional Networks for EEG-based Emotion Recognition

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