基于脑电信号和瞬时情感强度的情感角色划分

doi:10.16180/j.cnki.issn1007-7820.2025.05.007

Abstract

Abstract:

Existing affective computing studies use fixed affective labels to train an affective classifier. However, the EEG(Electroencephalogram) and true affective changes of individuals experiencing emotional stimuli are dynamic rather than constant, and different individuals have different physiological and subjective dynamic responses triggered by emotional stimuli. To address these issues, this study uses self-collected data on emotional click intensity and EEG data from the public dataset SEED-IV to categorize subjects into high and low emotional roles,which generates two types of affective intensity labels. Instantaneous affective intensity regression prediction is performed using electroencephalogram signal features and affective intensity labels across four machine learning models. According to the regression results, the applicability of two kinds of affective role groups to two kinds of affective intensity labels is analyzed. The regression results show that different emotional roles have different changes in the instantaneous emotional intensity under different emotions, and emotional roles can show better regression results on the corresponding emotional intensity label, and the division of emotional roles can better assist the analysis of individual instantaneous emotional changes.

Key words: machine learning, instantaneous affective intensities, emotional roles classification, high and low affective into, affective intensity labels, affective intensity regression, EEG, affective computing, affective intensity change

CLC Number:

TP391

LI Ruiding, GAN Kaiyu, YIN Zhong. Emotional Role Classification Based on the EEG and Instantaneous Affective Intensities[J].Electronic Science and Technology, 2025, 38(5): 46-52.

Figures/Tables 12

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References 22

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视频编号	情绪类别标签	电影片段来源
1	1	《半音》
2	2	《见鬼》
3	3	《宝贝计划》
4	0	《舌尖上的中国S2EP4》
5	2	《童眼》
6	0	《舌尖上的中国S2EP6》1
7	0	《舌尖上的中国S2EP6》2
8	1	《我家有喜EP32》
9	0	《舌尖上的中国S2EP2》
10	1	《分手合约》
11	2	《笔仙2》
12	1	《亲爱的》
13	1	《唐山大地震》
14	1	《养父EP3》
15	2	《笔仙3》
16	3	《北京2022冬奥会申奥宣传片》
17	2	《盂兰神功》1
18	2	《盂兰神功》2
19	3	《我家有喜EP64》
20	3	《你是我的生命EP10》
21	0	《舌尖上的中国S1EP1》
22	3	《听说》
23	0	《舌尖上的中国S1EP2》
24	3	《我家有喜EP5》

情感角色群体	被试者序号
H	4、6、9、12、15
L	1、2、3、5、7、8、10、11、13、14

情感角色群体	被试者序号
H	1、2、7、13
L	3、4、5、6、8、9、10、11、12,14、15、16、17、18、19、20

群体	标签	中性	开心	伤心	恐惧	平均值
高情感代入角色群体	H	0.175 5	0.120 8	0.135 0	0.213 5	0.161 2
高情感代入角色群体	L	0.159 4	0.075 9	0.115 8	0.218 4	0.142 4
低情感代入角色群体	H	0.220 1	0.221 8	0.206 1	0.168 8	0.204 2
低情感代入角色群体	L	0.219 6	0.203 6	0.201 8	0.144 5	0.192 4

群体	标签	中性	开心	伤心	恐惧	平均值
高情感代入角色群体	H	0.186 8	0.135 3	0.120 3	0.134 9	0.144 3
高情感代入角色群体	L	0.092 5	0.094 4	0.147 8	0.197 5	0.133 1
低情感代入角色群体	H	0.151 2	0.175 1	0.156 7	0.170 8	0.163 5
低情感代入角色群体	L	0.158 5	0.174 8	0.165 3	0.153 5	0.163 0

Emotional Role Classification Based on the EEG and Instantaneous Affective Intensities

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群体	标签	中性	开心	伤心	恐惧	平均值
高情感代入角色群体	H	0.212 3	0.122 8	0.146 3	0.185 6	0.166 8
高情感代入角色群体	L	0.093 1	0.118 8	0.136 7	0.169 0	0.129 4
低情感代入角色群体	H	0.134 7	0.162 6	0.159 4	0.142 5	0.148 9
低情感代入角色群体	L	0.145 9	0.156 4	0.175 3	0.143 3	0.155 2

群体	标签	中性	开心	伤心	恐惧	平均值
高情感代入角色群体	H	0.147 1	0.092 7	0.134 4	0.088 4	0.115 7
高情感代入角色群体	L	0.107 9	0.067 4	0.112 9	0.082 5	0.092 7
低情感代入角色群体	H	0.127 7	0.129 2	0.115 2	0.131 3	0.125 9
低情感代入角色群体	L	0.155 2	0.123 8	0.133 4	0.149 9	0.140 6

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