Cross-subject EEG emotion classification based on few-label adversarial domain adaption

作者：

Highlights：

• Groups forming between source and target data tackles the small data adaption.

• A shared feature extractor is proposed between the target model and the source model.

• Multi-source domain adaption obtains the best results with a proper number of source.

• Six groups with six labels maintains the high accuracy.

摘要

•Groups forming between source and target data tackles the small data adaption.•A shared feature extractor is proposed between the target model and the source model.•Multi-source domain adaption obtains the best results with a proper number of source.•Six groups with six labels maintains the high accuracy.

论文关键词：Electroencephalogram (EEG),Emotion classification,Cross-subject,Few label adversarial domain adaption

论文评审过程：Received 22 February 2021, Revised 6 July 2021, Accepted 7 July 2021, Available online 21 July 2021, Version of Record 26 July 2021.

论文官网地址：https://doi.org/10.1016/j.eswa.2021.115581