Nuclear Norm Regularized Deep Neural Network for EEG-Based Emotion Recognition

被引:2
|
作者
Liang, Shuang [1 ,2 ]
Yin, Mingbo [3 ]
Huang, Yecheng [3 ]
Dai, Xiubin [1 ]
Wang, Qiong [4 ]
机构
[1] Nanjing Univ Posts & Telecommun, Smart Hlth Big Data Anal & Locat Serv Engn Lab Jia, Nanjing, Peoples R China
[2] Nanjing Univ, State Key Lab Novel Software Technol, Nanjing, Peoples R China
[3] Nanjing Tech Univ, Sch Comp Sci & Technol, Nanjing, Peoples R China
[4] Chinese Acad Sci, Shenzhen Inst Adv Technol, Guangdong Prov Key Lab Comp Vis & Virtual Real Tec, Shenzhen, Peoples R China
来源
FRONTIERS IN PSYCHOLOGY | 2022年 / 13卷
基金
中国国家自然科学基金;
关键词
electroencephalography (EEG); emotion recognition; affective brain-computer interface (aBCI); structural information; nuclear norm regularization; SINGLE TRIAL EEG; DECOMPOSITION;
D O I
10.3389/fpsyg.2022.924793
中图分类号
B84 [心理学];
学科分类号
04 ; 0402 ;
摘要
Electroencephalography (EEG) based emotion recognition enables machines to perceive users' affective states, which has attracted increasing attention. However, most of the current emotion recognition methods neglect the structural information among different brain regions, which can lead to the incorrect learning of high-level EEG feature representation. To mitigate possible performance degradation, we propose a novel nuclear norm regularized deep neural network framework (NRDNN) that can capture the structural information among different brain regions in EEG decoding. The proposed NRDNN first utilizes deep neural networks to learn high-level feature representations of multiple brain regions, respectively. Then, a set of weights indicating the contributions of each brain region can be automatically learned using a region-attention layer. Subsequently, the weighted feature representations of multiple brain regions are stacked into a feature matrix, and the nuclear norm regularization is adopted to learn the structural information within the feature matrix. The proposed NRDNN method can learn the high-level representations of EEG signals within multiple brain regions, and the contributions of them can be automatically adjusted by assigning a set of weights. Besides, the structural information among multiple brain regions can be captured in the learning procedure. Finally, the proposed NRDNN can perform in an efficient end-to-end manner. We conducted extensive experiments on publicly available emotion EEG dataset to evaluate the effectiveness of the proposed NRDNN. Experimental results demonstrated that the proposed NRDNN can achieve state-of-the-art performance by leveraging the structural information.
引用
收藏
页数:11
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