Earticle

초록

영어

EEG signals have been widely used in emotion recognition in recent years. However, a great challenge still exists for the practical applications of cross-subject emotion recognition. Inspired by recent neuroscience studies and the advantage of the DE feature applied in EEG emotion recognition, we proposed a combined DE feature and contrastive learning method to tackle the cross-subject emotion recognition problem. The proposed model can minimize the inter-subject differences by maximizing the similarity in EEG signal representations across subjects when they receive the same emotional stimuli in contrast to different ones and gain a better encoding. Finally, we conducted extensive experiments on SEED and SEED-IV. The cross-subject emotion recognition accuracy is 84.72 on the SEED and 69.24 on the SEED-IV. It experimentally verified the effectiveness of the model.

목차

Abstract
I. INTRODUCTION
II. THE PROPOSED MODEL
A. The Data Sampler
B. The Base Encoder
C. The Contrastive Loss
III. EXPERIMENTAL RESULT
IV. CONCLUSION
REFERENCES

키워드

저자

• Dengbing Huang [ Department of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing, China ] Corresponding Author
• Huimei Ou [ Department of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing, China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  한국차세대컴퓨팅학회 학술대회

• 간기

  반년간

• 수록기간

  2021~2025

• 십진분류

  KDC 566 DDC 004