Journal
IFAC PAPERSONLINE
Volume 53, Issue 5, Pages 458-463Publisher
ELSEVIER
DOI: 10.1016/j.ifacol.2021.04.125
Keywords
Emotion recognition; EEG; Functional connection; Sample entropy; Phase synchronization; Wavelet packet transformation
Categories
Funding
- Natural Science Foundation of Shandong Province, China [ZR2019MF0 71]
- National Natural Science Foundation of China [61373149, 61672329]
Ask authors/readers for more resources
Emotion recognition plays an indispensable role in the field of brain-computer interaction. Many researchers perform emotion recognition based on single channel feature extraction, which ignores the information interaction between different brain regions. In order to surmount this limitation, we propose a functional connectivity network based emotion recognition combining sample entropy (SE). Firstly, EEG data of DEAP is decomposed into four frequency bands of theta, alpha, beta, and gamma with WPT. Secondly, we build a functional connection network based on the phase synchronization index (PSI) and extract five features, namely global clustering coefficient, local clustering coefficient, global efficiency, character path length, and degree, then SE is extracted and combined with them. Finally, the extracted features are input into the random forest (RF) classifier for emotion classification. The experimental results on DEAP demonstrate that our proposed method is more effective for emotion recognition, and the best classification accuracy reaches 88.58%. Copyright (C) 2020 The Authors.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available