计算机工程2026，Vol.52Issue(6)：109-120,12.DOI:10.19678/j.issn.1000-3428.0070039
基于主动迁移学习的脑力负荷识别研究
Research on Mental Workload Recognition Based on Active Transfer Learning
摘要
Abstract
Unsupervised transfer learning has been widely used in cross-subject mental workload recognition studies based on physiological signals;however,the model performance is limited by the lack of labeled target domain data.To address this problem,a cross-subject mental workload recognition method that combines transfer learning with active learning is proposed.Using an Electroencephalogram(EEG)as the research object,source domains with distributions similar to the target domain are selected by calculating the maximum mean discrepancy between the source and target domains.Second,a one-to-one cross-subject mental workload recognition model is constructed for each selected source and target domain.The feature distributions of the two domains are brought closer by an adversarial network,and a small number of target domain samples,considering both uncertainty and diversity,are labeled by active learning based on uncertainty-weighted clustering,which participated in the subsequent training of the model classification layers.Finally,ensemble learning is utilized to synthesize the recognition results of multiple single-source domain models.Experiments on the publicly available WAUC dataset reveal that source domain selection reduces the incidence of negative transfers and computational costs.The introduction of active learning effectively improves the performance of cross-subject transfer learning.Compared to unsupervised transfer learning,the average recognition accuracy is improved by 14.7％in the task of recognizing mental workload under different levels of physical workload.Ensemble learning overcomes the shortcomings of the limited knowledge learned by single-source domain models,further improving the recognition performance of the model and achieving an average recognition of 86.1％.关键词
脑力负荷/跨个体/主动迁移学习/源域优选/集成学习Key words
mental workload/cross subject/active transfer learning/source domain selection/ensemble learning分类
信息技术与安全科学引用本文复制引用
蒋欣怡,陈兰岚,郑时蓬..基于主动迁移学习的脑力负荷识别研究[J].计算机工程,2026,52(6):109-120,12.基金项目
国家自然科学基金(62376095). （62376095）
