EEG-Based Emotion Recognition Using Spatial-Temporal-Connective Features via Multi-Scale CNN

被引：23
作者：
Li, Tianyi [1 ]
Fu, Baole [1 ]
Wu, Zixuan [1 ]
Liu, Yinhua [1 ]
机构：
[1] Qingdao Univ, Inst Future, Qingdao 266000, Peoples R China
来源：
IEEE ACCESS | 2023年 / 11卷
关键词：
Feature extraction; Electroencephalography; Emotion recognition; Convolutional neural networks; Brain modeling; EEG; connective features; STC-CNN; CONVOLUTIONAL NEURAL-NETWORK; MULTICHANNEL EEG; ATTENTION;
D O I：
10.1109/ACCESS.2023.3270317
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
Electroencephalography (EEG) signals from each channel mainly reflect activities of the brain region close to the channel position, and the activities cooperated by various brain regions are response to the emotion-induced stimuli. In this paper, temporal, spatial and connective features are extracted from EEG signals gotten around the head, and used for emotion recognition via a proposed model, spatial-temporal-connective muti-scale convolutional neural network (STC-CNN). The channel-to-channel connectivity is gotten to describe brain region-to-region cooperation under emotion stimuli. The model obtained an average accuracy of 96.79% and 96.89% in classifying the two emotional dimensions of valence and arousal.
引用
收藏
页码：41859 / 41867
页数：9
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