MGFKD: A semi-supervised multi-source domain adaptation algorithm for cross-subject EEG emotion recognition

Author:

Zhang RuiORCID,Guo Huifeng,Xu Zongxin,Hu Yuxia,Chen Mingming,Zhang Lipeng

Funder

Zhengzhou University

Publisher

Elsevier BV

Reference39 articles.

1. Berthelot, D., Roelofs, R., Sohn, K., Carlini, N., Kurakin, A., 2022. AdaMatch: A Unified Approach to Semi-Supervised Learning and Domain Adaptation. 〈http://arxiv.org/abs/2106.04732〉.

2. Bhatti, A., Behinaein, B., Rodenburg, D., Hungler, P., Etemad, A., 2021. Attentive Cross-modal Connections for Deep Multimodal Wearable-based Emotion Recognition. 〈http://arxiv.org/abs/2108.02241〉.

3. Geodesic flow kernel for unsupervised domain adaptation;Boqing,2012

4. A fast, efficient domain adaptation technique for cross-domain electroencephalography(EEG)-based emotion recognition;Chai;Sensors,2017

5. Unsupervised domain adaptation techniques based on auto-encoder for non-stationary EEG-based emotion recognition;Chai;Comput. Biol. Med.,2016

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