基于主动迁移学习的脑力负荷识别研究OA

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％.