Knowledge Commons of Institute of Automation,CAS
EEG-Based Emotion Recognition with Similarity Learning Network | |
Yixin Wang; Shuang Qiu; Jinpeng Li; Xuelin Ma; Zhiyue Liang; Hui Li; Huiguang He | |
2019 | |
会议名称 | International Conference of the IEEE Engineering in Medicine and Biology Society |
会议日期 | 2019/07 |
会议地点 | 德国柏林 |
摘要 | Emotion recognition is an important field of research in Affective Computing (AC), and the EEG signal is one of useful signals in detecting and evaluating emotion. With the development of the deep learning, the neural network is widely used in constructing the EEG-based emotion recognition model. In this paper, we propose an effective similarity learning network, on the basis of a bidirectional long short term memory (BLSTM) network. The pairwise constrain loss will help to learn a more discriminative embedding feature space, combined with the traditional supervised classification loss function. The experiment result demonstrates that the pairwise constrain loss can significantly improve the emotion classification performance. In addition, our method outperforms the state-of-theart emotion classification approaches in the benchmark EEG emotion dataset–SEED dataset, which get a mean accuracy of 94.62%. |
收录类别 | EI |
七大方向——子方向分类 | 脑机接口 |
文献类型 | 会议论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/44921 |
专题 | 脑图谱与类脑智能实验室_神经计算与脑机交互 |
作者单位 | 1.Research Center for Brain-inspired Intelligence, Institute of Automation, Chinese Academy of Science, Beijing, China 2.National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Science, Beijing, China 3.University of Chinese Academy of Sciences, Beijing, China 4.Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Science, Beijing, China 5.Department of Educational technology, Capital Normal University, Beijing, China |
推荐引用方式 GB/T 7714 | Yixin Wang,Shuang Qiu,Jinpeng Li,et al. EEG-Based Emotion Recognition with Similarity Learning Network[C],2019. |
条目包含的文件 | ||||||
文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
08857499.pdf(1120KB) | 会议论文 | 开放获取 | CC BY-NC-SA | 浏览 |
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