郑州大学学报（工学版）2026，Vol.47Issue(1)：73-80,8.DOI:10.13705/j.issn.1671-6833.2026.01.005
基于时间序列成像的多任务学习驱动情感识别
Multi-task Learning-driven Emotion Recognition Based on Time Series Imaging
摘要
Abstract
To overcome the high computational complexity of EEG-based emotion recognition methods based on fea-ture extraction or time-frequency representations,a multi-task learning-driven method for emotion recognition based on time series imaging(TSI)was proposed.EEG signals were directly transformed into two-dimensional images using Gramian angular field,Markov transition field,and motif difference field.Built upon the ResNet18 architec-ture,a multi-task feature fusion framework was designed to integrate features from the three imaging methods to en-hance emotional feature representation.Experimental results showed that with the DEAP dataset,the proposed method achieved average classification accuracies of 96.51%and 97.22%for binary classification of Valence and Arousal,respectively,and with the AMIGOS dataset,the accuracies reached 98.59%and 99.64%.When extend-ed to four-class and eight-class classification tasks,the proposed method achieved average accuracies of 91.06%and 87.43%with DEAP,and 97.41%and 89.84%with AMIGOS,respectively.These results demonstrated the robustness of the proposed method in EEG-based emotion recognition.关键词
脑电/情感识别/时间序列成像/多任务/特征融合Key words
EEG/emotion recognition/time series imaging/multi-task/feature fusion分类
信息技术与安全科学引用本文复制引用
XU Shengxin,LIANG Bizheng,HU Fei,XU Huaxing..基于时间序列成像的多任务学习驱动情感识别[J].郑州大学学报（工学版）,2026,47(1):73-80,8.基金项目
国家重点研发计划(2022YFC3502400) （2022YFC3502400）
中央本级重大增减支项目(2060302-1802-03) （2060302-1802-03）
