[1]李景聪,潘伟健,林镇远,等.采用多路图注意力网络的情绪脑电信号识别方法[J].智能系统学报,2022,17(3):531-539.[doi:10.11992/tis.202107003]
LI Jingcong,PAN Weijian,LIN Zhenyuan,et al.Emotional EEG signal recognition method using multi-path graph attention network[J].CAAI Transactions on Intelligent Systems,2022,17(3):531-539.[doi:10.11992/tis.202107003]
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LI Jingcong,PAN Weijian,LIN Zhenyuan,et al.Emotional EEG signal recognition method using multi-path graph attention network[J].CAAI Transactions on Intelligent Systems,2022,17(3):531-539.[doi:10.11992/tis.202107003]
采用多路图注意力网络的情绪脑电信号识别方法
《智能系统学报》[ISSN 1673-4785/CN 23-1538/TP] 卷:
17
期数:
2022年第3期
页码:
531-539
栏目:
学术论文—机器感知与模式识别
出版日期:
2022-05-05
- Title:
- Emotional EEG signal recognition method using multi-path graph attention network
- Keywords:
- emotion recognition; cross-subject; graph convolutional neural network; graph attention network; electroencephalogram signal; brain-computer interface; neural network; deep learning
- 分类号:
- TP391
- 摘要:
- 情绪是一种大脑产生的主观认知的概括。脑信号解码技术可以以一种较客观的方式来有效地研究人的情绪及其相关认知行为。本文提出了一种基于图注意力网络的脑电情绪识别方法(multi-path graph attention networks, MPGAT)，该方法通过对脑电信号通道建图，利用卷积层提取脑电信号的时域特征以及各频带的特征，使用图注意力网络进一步捕捉情绪脑电信号的局部特征以及各脑区之间的内在功能关系，进而构建出更好的脑电信号表征。MPGAT在SEED和SEED-IV数据集的跨被试情绪识别平均准确率分别为86.03%、72.71%，在DREAMER数据集的效价(valence)和唤醒(arousal)维度的跨被试平均准确率分别为76.35%和75.46%，达到并部分超过了目前最先进脑电情绪识别方法的性能。本文所提出的脑电信号处理方法有望为情绪认知科学研究与情绪脑机接口系统提供新的技术手段。
- Abstract:
- Emotion is a generalization of subjective cognition produced by the brain. Brain signal decoding technology can effectively study human emotions and related cognitive behaviors more objectively. This paper proposes a multi-path graph attention networks (MPGAT) method for electroencephalogram (EEG) emotion recognition based on graph attention networks. This method uses convolutional layers to extract the time domain features of EEG signals and the features of each frequency band by mapping EEG signal channels; moreover, it uses the graph attention network to further capture the local features of emotional EEG signals and the internal functional relationships among the brain regions; constructing a better representation of EEG signals later. The average accuracy of MPGAT’s cross-subject emotion recognition in the SEED and SEED-IV datasets were 86.03% and 72.71%, respectively, and the average cross-subject accuracy rates of the valence and arousal dimensions in the DREAMER dataset were 76.35% and 75.46%, respectively, meeting and partially exceeding the performance of the most advanced EEG emotion recognition methods. The EEG signal processing method proposed in this paper can provide novel technical means for the scientific research of emotional cognition and emotional brain-computer interface system.
- 参考文献/References:
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- 相似文献/References:
-
[1]潘家辉,何志鹏,李自娜,等.多模态情绪识别研究综述[J].智能系统学报,2020,15(4):633.[doi:10.11992/tis.202001032]
PAN Jiahui,HE Zhipeng,LI Zina,et al.A review of multimodal emotion recognition[J].CAAI Transactions on Intelligent Systems,2020,15():633.[doi:10.11992/tis.202001032]
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ZHANG Hangkui,LIU Dongjun,KONG Wanzeng.ERP detection of multi-feature embedding in the low-dimensional subspace for cross-subject RSVP[J].CAAI Transactions on Intelligent Systems,2022,17():1054.[doi:10.11992/tis.202111059]
备注/Memo
收稿日期:2021-07-02。
基金项目:国家自然科学基金青年项目（62006082）；广东省自然科学基金项目（2021A1515011600，2020A1515110294）；广州市科技计划项目（202102020877）.
作者简介:李景聪，副研究员，博士，主要研究方向为机器学习、深度学习与混合神经网络、脑信号等生物医学信号。曾荣获国际脑组织分割竞赛MRBrainS Challenge第一名。发表学术论文6篇;潘伟健，硕士研究生，主要研究方向为情绪脑电识别、图网络;林镇远，硕士研究生，主要研究方向为脑电伪迹识别
通讯作者:李景聪.E-mail：lijingcong@hotmail.com
更新日期/Last Update:
1900-01-01