文献类型：期刊文献

英文题名：EEG Emotion Recognition Based on 3-D Feature Representation and Dilated Fully Convolutional Networks

作者：Li, Dongdong[1,2];Chai, Bing[2];Wang, Zhe[2];Yang, Hai[2];Du, Wenli[3]

机构：[1]East China Univ Sci & Technol, Key Lab Adv Control & Optimizat Chem Proc, Minist Educ, Shanghai 200237, Peoples R China;[2]East China Univ Sci & Technol, Dept Comp Sci & Engn, Shanghai 200237, Peoples R China;[3]East China Univ Sci & Technol, KKey Lab Adv Control & Optimizat Chem Proc, Minist Educ, Shanghai 200237, Peoples R China

年份：2021

卷号：13

期号：4

起止页码：885

外文期刊名：IEEE TRANSACTIONS ON COGNITIVE AND DEVELOPMENTAL SYSTEMS

收录：;EI(收录号：20210409805625);WOS:【SCI-EXPANDED(收录号:WOS:000728925200017)】；

基金：This work was supported in part by the Natural Science Foundation of China under Grant 61806078, Grant 62076094, Grant 61976091, and Grant 61902126; in part by the National Major Scientific and Technological Special Project for "Significant New Drugs Development" under Grant 2019ZX09201004; and in part by the Shanghai Science and Technology Program "Distributed and Generative Few-Shot Algorithm and Theory Research" under Grant 20511100600.

语种：英文

外文关键词：Electroencephalography; Feature extraction; Electrodes; Brain modeling; Emotion recognition; Convolution; Task analysis; 3-D feature representation (3DFR); dilated fully convolutional networks (DFCN); electroencephalogram (EEG); spectral norm regularization (SNR)

摘要：Emotion recognition involving high-dimensional electroencephalogram (EEG) data demands urgently for a way to learn robust and representative EEG features for final classification. In this article, a novel framework combining 3-D feature representation and dilated fully convolutional network (3DFR-DFCN) is proposed for EEG emotion recognition (EER). To excavate the prior knowledge, such as interchannel and interfrequency-band correlation information, 1-D feature sequences are extended into 2-D electrode meshes of different frequency bands. Then, the acquired electrode meshes under multiple activation patterns are further constructed into 3-D EEG arrays to capture their complementary information. To realize cross-band and cross-channel feature learning, a dilated fully convolutional network (DFCN) is built to process the input feature array, then the spectral norm regularization (SNR) item is introduced to reduce the sensitivity to the disturbed EEG features. Both subject-dependent and subject-independent experiments have conducted on DEAP and DREAMER data sets. An average accuracy of 94.59%/81.03%, 95.32%/79.91%, 94.78%/80.23% are, respectively, obtained for valence, arousal, and dominance classifications for two kinds of experiments on the DEAP data set. The integration of spatial information and frequency-band information is meaningful for assessment of human emotional states in practical or clinical applications.