基于多源域领域适应字典学习和稀疏表示的脑电情感识别方法
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1.常州工业职业技术学院 信息工程学院;2.江苏理工学院计算机工程学院

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国家自然科学基金资助项目(12005182)


Multi-source Domain Adaptive Dictionary Learning and Sparse Representation Approach for Electroencephalogram-based Emotion identification
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1.College of Information Engineering,Changzhou Institute of Industry Technology,Changzhou;2.School of Computer Engineering,Jiangsu University of Technology

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    摘要:

    由于人机交互的迅速发展,情感计算近年来越来越受到人们的关注。脑电信号容易记录且不易伪装,在情感识别方面发挥着重要作用。然而,由于人类情感的多样性和个体可变性,基于脑电信号的情感识别至今仍是情感计算领域的难题。针对这一问题,提出了种多源域领域适应字典学习和稀疏表示方法。为减少源领域和目标领域数据分布的差异,将所有领域的数据投影到共享子空间,并在子空间中学习一个共有字典,建立源领域和目标领域之间的桥梁。共有字典的学习准则是最小化类内稀疏重建误差和最大化类间稀疏重建误差,可以保证学习到的稀疏表示具有识别能力。此外,为避免负迁移的发生,每个源域对应一个领域适应权重,且可以在自适应学习中得到权重最佳值。模型参数的求解通过参数交替优化的方法,所有的参数可同时达到最优解。在脑电情感DEAP数据集的实验验证了本文方法的有效性。

    Abstract:

    Due to the rapid development of human-computer interaction, affective computing has attracted more and more attention in recent years. Electroencephalogram (EEG) signals are easy to record and difficult to camouflage, it plays an important role in emotion recognition. However, due to the diversity of human emotions and individual variability, EEG-based emotion recognition is still a difficult problem in the field of affective computing. Aiming at this problem, a multi-source domain adaptive dictionary learning and sparse representation approach is proposed in this study. To reduce the difference of data distribution between the source domains and the target domain, the data of all domains are projected into the shared subspace, and a common dictionary is learned in the subspace to establish the relationship between the source domains and the target domain. The learning criterion of common dictionary is to minimize the intra-class sparse reconstruction error and maximize the inter-class sparse reconstruction error, which can ensure that the learned sparse representation has more recognition ability. In addition, to avoid the occurrence of negative transfer, each source domain corresponds to a domain adaptive weight, and the optimal value of the weight can be obtained in the adaptive learning. The model parameters are solved by the method of parameter alternate optimization, and all parameters can reach their optimal solutions at the same time. Experiments on the DEAP dataset verify the effectiveness of our method.

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  • 收稿日期:2022-06-23
  • 最后修改日期:2022-08-28
  • 录用日期:2022-09-01
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