Research on Support Tensor Machine Based on Synchronous Brain Network for Emotion Classification

Liya HUANG; Yibo SU; Junkai MA; Weiwei DING; Chuancheng SONG

doi:10.11999/JEIT190882

Volume 42 Issue 10

Oct. 2020

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Article Navigation > Journal of Electronics & Information Technology > 2020 > 42(10): 2462-2470

Liya HUANG, Yibo SU, Junkai MA, Weiwei DING, Chuancheng SONG. Research on Support Tensor Machine Based on Synchronous Brain Network for Emotion Classification[J]. Journal of Electronics & Information Technology, 2020, 42(10): 2462-2470. doi: 10.11999/JEIT190882

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Liya HUANG, Yibo SU, Junkai MA, Weiwei DING, Chuancheng SONG. Research on Support Tensor Machine Based on Synchronous Brain Network for Emotion Classification[J]. Journal of Electronics & Information Technology, 2020, 42(10): 2462-2470. doi: 10.11999/JEIT190882

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Research on Support Tensor Machine Based on Synchronous Brain Network for Emotion Classification

doi: 10.11999/JEIT190882

1.
School of Electronic and Optical Engineering & Microelectronics, Nanjing University of Posts and Telecommunications, Nanjing 210023, China
2.
Bell Honors Shool, Nanjing University of Posts and Telecommunications, Nanjing 210023, China

Funds: The National Natural Science Foundation of China (61977039)

Received Date: 2019-11-04
Rev Recd Date: 2020-03-04

Available Online: 2020-03-20

Publish Date: 2020-10-13

Abstract

Abstract

Emotion has always been a research hot spot in many disciplines such as psychology, education, and information science. Electro EncephaloGram(EEG) signal has received extensive attention in the field of emotion recognition because of its objective and not easy to disguise. Since human emotions are generated by the interaction of multiple brain regions in the brain, an algorithm of Support Tensor Machine based on Synchronous Brain Network (SBN-STM) for emotion classification is proposed. The algorithm uses Phase Locking Value (PLV) to construct a synchronous brain network, in order to analyze the synchronization and correlation between multi-channel EEG signals, and generate a second-order tensor sequence as a training set. The Support Tensor Machine (STM) model can distinguish a two-category of positive and negative emotions. Based on the DEAP EEG emotion database, this paper analyzes the selection method of synchronic brain network tensor sequence, the research on the size and position of the optimal tensor sequence window solves the problem of traditional emotion classification algorithm which always exists feature redundancy, and improves the model training speed. The results show that the accuracy of the emotional classification method based on SBN-STM is better than support vector machine, C4.5 decision tree, artificial neural network, and K-nearest neighbor which using vectors as input feature.
- Emotion classification,
- Synchronous Brain Network (SBN),
- Support Tensor Machine (STM),
- Phase Locking Value (PLV)

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References(25)

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