Yan Jun An
Lanzhou Jiaotong University
11 Papers
Yan Jun An is an academic researcher from Lanzhou Jiaotong University. The author has contributed to research in topics: Pattern recognition (psychology) & Computer science. The author has co-authored 1 publications.
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Papers
EEG emotion recognition based on the attention mechanism and pre-trained convolution capsule network
TL;DR: In this paper , the authors proposed an EEG emotion recognition model based on the attention mechanism and a pre-trained convolutional capsule network, which employs coordinate attention to endow the input signal with relative spatial information and then maps the EEG signal to higher dimensional space.
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GLFANet: A global to local feature aggregation network for EEG emotion recognition
Shuaiqi Liu,Yingying Zhao,Yan Jun An,Jie Zhao,Shuihua Wang +4 more
TL;DR: Zhang et al. as mentioned in this paper proposed an EEG emotion recognition algorithm based on a global to local feature aggregation network (GLFANet), which firstly uses the spatial location of the channels of EEG signals and the frequency domain features of each channel to construct an undirected topological graph to represent the spatial connection relationship between channels.
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DA-CapsNet: A Multi-Branch Capsule Network Based on Adversarial Domain Adaption for Cross-Subject EEG Emotion Recognition
Shuaiqi Liu,Zeyao Wang,Yan Jun An,Bing Li,Xinrui Wang,Yudong Zhang +5 more
TL;DR: This paper proposes DA-CapsNet, a multi-branch Capsule network for cross-subject EEG emotion recognition, utilizing domain adaptation and adversarial training to improve performance, achieving better results on three public EEG datasets.
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Cross-Subject EEG Emotion Recognition Based on Interconnected Dynamic Domain Adaptation
Yan Jun An,Shaohai Hu,Shuaiqi Liu,Xinrui Wang,Xiaole Ma +4 more
- 14 Apr 2024
TL;DR: A cross subject emotion recognition method based on interconnection dynamic domain adaptation (IDDA), which enhances the emotion discrimination ability of domain invariant features, thereby improving the accuracy of cross-subject EEG emotion recognition.
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Cross-subject emotion recognition by EEG driven spatio-temporal hybrid network based on domain adaptation and dynamic graph attention
Shuaiqi Liu,Xinrui Wang,Zhihui Gu,Yan Jun An,Shuhuan Zhao,Bing Li,Yudong Zhang +6 more
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