基于多源域迁移学习的脑电情感识别

针对普通机器学习算法与单源域迁移学习在应用方面的局限性,利用多源域迁移学习算法解决跨被试情感识别中正确率低的问题。为提高迁移学习的计算效率并避免负迁移现象的产生,分别从样本和特征两个方面对迁移数据进行优化。用多源域选择算法筛选出最优源域集合,用迁移特征选择算法得到最优特征集合,训练出多个迁移学习模型并对之集成。在数据集SEED上对该算法进行验证,验证结果表明,该模型相比其它情感识别模型具有更优的跨被试情感识别能力。

EEG emotion recognition based on multi-source domain transfer learning

Considering the limited applications of the traditional machine learning algorithms and the single source domain transfer learning algorithm,multi-source domain transfer learning algorithm was adopted to improve the detection accuracy in the cross-subject emotion recognition.To improve the computational efficiency of transfer learning and to avoid the occurrence of negative transfer,the transfer data were optimized from both the sample and the feature aspects.The multi-source domain selection(MDS)algorithm was used to select the optimal source domain set.The transfer feature selection(TFS)algorithm was used to get the optimal feature subset.Multiple transfer learning models were trained and integrated.The model is validated on the SEED data set,which shows better cross-subject emotion recognition ability than other emotion recognition models.