Multi-channel EEG classification method according to music tempo stimuli using 3D convolutional bidirectional gated recurrent neural network

Min-Soo Kim; Gi Yong Lee; Hyoung-Gook Kim

doi:10.7776/ASK.2021.40.3.228

All Issue

2021 Vol.40, Issue 3 Preview Page Next Page

Research Article

Multi-channel EEG classification method according to music tempo stimuli using 3D convolutional bidirectional gated recurrent neural network 3차원 합성곱 양방향 게이트 순환 신경망을 이용한 음악 템포 자극에 따른 다채널 뇌파 분류 방식

31 May 2021. pp. 228-233

PDF XML

Abstract

In this paper, we propose a method to extract and classify features of multi-channel ElectroEncephalo Graphy (EEG) that change according to various musical tempo stimuli. In the proposed method, a 3D convolutional bidirectional gated recurrent neural network extracts spatio-temporal and long time-dependent features from the 3D EEG input representation transformed through the preprocessing. The experimental results show that the proposed tempo stimuli classification method is superior to the existing method and the possibility of constructing a music-based brain-computer interface.

Keywords

ElectroEncephaloGraphy (EEG)

Tempo stimuli

3D convolutional bidirectional gated recurrent neural network

Gated recurrent unit

본 논문에서는 다양한 음악 템포 자극에 따라 변화하는 다채널 ElectroEncephaloGraphy(EEG)의 특징을 추출하고 분류하는 방식을 제안한다. 제안하는 방식에서 3차원 합성곱 양방향 게이트 순환 신경망은 전처리 과정 통해 변환된 3차원 EEG 입력 표현으로부터 시공간 및 긴 시간 종속적 특징을 추출한다. 실험 결과는 제안된 템포 자극 분류 방식이 기존의 방식보다 우수하며 음악 기반 뇌-컴퓨터 인터페이스를 구축할 수 있는 가능성을 보여준다.

키워드

뇌파

템포 자극

3차원 합성곱 양방향 게이트 순환 신경망

게이트 순환 유닛

References

T. Greer, B. Ma, M. Sachs, A. Habibi, and S. S. Narayanan, "A multimodal view into music's effect on human neural, physiological, and emotional experience," Proc. ACM Int. Conf. Multimedia, 167-175 (2019). 10.1145/3343031.3350867

S. Losorelli, D. T. Nguyen, J. P. Dmochowski, and B. Kaneshiro, "NMED-T: A tempo-focused dataset of cortical and behavioral responses to naturalistic music," Proc. ISMIR. 339-346 (2017).

I. N. Clark and J. Tamplin, "How music can influence the body: Perspectives from current research," Voices: A World Forum for Music Therapy, 16, 1-15 (2016). 10.15845/voices.v16i2.871

J. L. Hsu, Y. L. Zhen, T. C. Lin, and Y. S. Chiu, "Affective content analysis of music emotion through EEG," Multimed. Syst. 24, 195-210 (2018). 10.1007/s00530-017-0542-0

E. S. Salama, R. A. El-Khoribi, M. E. Shoman, and M. A. Shalaby, "EEG-based emotion recognition using 3D convolutional neural networks," Int. J. Adv. Comput. Sci. Appl. 9, 329-337 (2018).

Y. Guo, W. Liu, D. wei, and Q. Chen, "Emotional recognition based on EEG signals comparing long-term and short-term memory with gated recurrent unit using Batch Normalization," Proc. Int. Conf. MEET. 1-9 (2019).

Information

Publisher :The Acoustical Society of Korea
Publisher(Ko) :한국음향학회
Journal Title :The Journal of the Acoustical Society of Korea
Journal Title(Ko) :한국음향학회지
Volume : 40
No :3
Pages :228-233
Received Date : 2021-03-17
Revised Date : 2021-05-10
Accepted Date : 2021-05-21
DOI :https://doi.org/10.7776/ASK.2021.40.3.228

The Journal of the Acoustical Society of Korea ISSN:1225-4428(Print) 2287-3775(Online) 한국음향학회지

All Issue