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    陈长德, 陈兰岚, 张效艇. 基于多源域深度域自适应的脑力负荷识别[J]. 华东理工大学学报(自然科学版), 2023, 49(5): 744-753. DOI: 10.14135/j.cnki.1006-3080.20220723002
    引用本文: 陈长德, 陈兰岚, 张效艇. 基于多源域深度域自适应的脑力负荷识别[J]. 华东理工大学学报(自然科学版), 2023, 49(5): 744-753. DOI: 10.14135/j.cnki.1006-3080.20220723002
    CHEN Changde, CHEN Lanlan, ZHANG Xiaoting. Mental Workload Recognition Based on Multi-Source Deep Domain Adaptation[J]. Journal of East China University of Science and Technology, 2023, 49(5): 744-753. DOI: 10.14135/j.cnki.1006-3080.20220723002
    Citation: CHEN Changde, CHEN Lanlan, ZHANG Xiaoting. Mental Workload Recognition Based on Multi-Source Deep Domain Adaptation[J]. Journal of East China University of Science and Technology, 2023, 49(5): 744-753. DOI: 10.14135/j.cnki.1006-3080.20220723002

    基于多源域深度域自适应的脑力负荷识别

    Mental Workload Recognition Based on Multi-Source Deep Domain Adaptation

    • 摘要: 为了解决脑力负荷识别模型在跨被试场景下泛化性能差的问题,本文构建了基于多源域深度域自适应的脑力负荷识别模型。使用预处理后的脑电和心电信号,首先通过基于最大均值差异的源域优选算法筛选出与目标域被试数据分布相近的源域被试集合;然后引入动态对抗域自适应网络,以对抗训练的形式同时适配源域和目标域数据的边缘分布与条件分布;最后采用集成学习策略对不同源域训练出的模型分类结果进行投票集成,以提高脑力负荷识别的准确性和稳定性。实验结果表明,该模型在WAUC数据集的跨被试脑力负荷识别任务中具有较好的识别准确率和鲁棒性。

       

      Abstract: In automated human-machine collaboration systems, operators play an increasingly important role in making decisions and strategies. Recently, measures based on physiological signals, such as electroencephalogram (EEG) and electrocardiogram (ECG), have received increasing attention due to their ability to objectively deduce the inherent mental states and sensitively monitor mental workload level within continuous time interval. However, obvious individual differences affect the transferability of trained model to a new subject. In order to solve the problem of poor generalization performance of recognition model in cross subject scenarios, a mental workload recognition model based on multi-source deep domain adaptation is constructed. In view of the excessive number of source domains containing redundant information and the cost of model training, the source domains with similar data distribution to the target domain are firstly selected by the optimization algorithm based on Maximum Mean Discrepancy. Then, a dynamic adversarial domain adaptive network is introduced to simultaneously adapt the marginal and conditional distribution of source domain and target domain data in the form of countermeasure training. Finally, an ensemble learning strategy is used to vote and integrate the classification results of models trained in different source domains, improving the accuracy and stability of mental workload recognition. The experimental results show that the model has good recognition accuracy and robustness in cross subject mental workload recognition task in the WAUC dataset and can be further developed as the operator's mental workload identification system to reasonably allocate work tasks and improve work performance.

       

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