电子科技2025，Vol.38Issue(5)：46-52,7.DOI:10.16180/j.cnki.issn1007-7820.2025.05.007
基于脑电信号和瞬时情感强度的情感角色划分
Emotional Role Classification Based on the EEG and Instantaneous Affective Intensities
摘要
Abstract
Existing affective computing studies use fixed affective labels to train an affective classifier.Howev-er,the EEG(Electroencephalogram)and true affective changes of individuals experiencing emotional stimuli are dy-namic 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 inten-sity 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 u-sing electroencephalogram signal features and affective intensity labels across four machine learning models.Accord-ing to the regression results,the applicability of two kinds of affective role groups to two kinds of affective intensity la-bels 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 correspond-ing emotional intensity label,and the division of emotional roles can better assist the analysis of individual instantane-ous emotional changes.关键词
机器学习/瞬时情感强度/情感角色划分/高低情感代入/情感强度标签/情感强度回归/脑电信号/情感计算/情感强度变化Key words
machine learning/instantaneous affective intensities/emotional roles classification/high and low af-fective into/affective intensity labels/affective intensity regression/EEG/affective computing/affective intensity change分类
计算机与自动化引用本文复制引用
李汭钉,甘开宇,尹钟..基于脑电信号和瞬时情感强度的情感角色划分[J].电子科技,2025,38(5):46-52,7.基金项目
国家自然科学基金(61703277) （61703277）
上海青年科技英才扬帆计划(17YF1427000) National Natural Science Foundation of China(61703277) （17YF1427000）
Shang-hai Sailing Program(17YF1427000) （17YF1427000）
