半监督低秩表示的脑电情感识别方法
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TP181

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江苏省自然科学基金(BK20211333);未来网络科学研究基金项目(FNSRFP-2021-YB-36);江苏省媒体设计与软件技术重点实验室开放项目(2023年)


Semi-supervised low-rank representation for EEG emotion recognition
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    摘要:

    脑电图能客观反映人的情绪状态,但由于脑电信号具有复杂性和非平稳性等特点,使得采集大量标记脑电样本较困难,因此在一定程度上限制了脑电情感识别方法的效果和泛化性能.针对以上问题,提出一种半监督低秩表示的脑电情感识别方法(Semi-Supervised Low-Rank Representation,SSLRR).利用少量标记脑电样本的估计标签设计一个回归形式的目标函数,以此来有效估计未标记样本的标签.使用ε-拖拽技术确保标签与标签之间的分离性,并对松弛标签施加低秩约束,以提高其类内紧密度和相似度.对提出的方法融入一个类邻接图,以此捕获所有脑电样本数据的局部邻域信息.在SEED-Ⅳ和SEED-Ⅴ两个公开数据集上进行对比实验,结果表明,相比现有半监督算法,所提出的方法在脑电情感识别问题上具有更好的性能.

    Abstract:

    EEG,as a direct response to brain activity,can objectively reflect a person's emotional state.However,the non-smoothness and complexity of EEG signals make it difficult to collect a large number of labelled EEG samples,thus limiting the effectiveness and generalization performance of EEG emotion recognition methods.Here,a Semi-Supervised Low-Rank Representation (SSLRR) approach for EEG emotion recognition is proposed to address the above issues.First,an objective function in regression form is designed using the estimated labels of a small number of labelled EEG samples to effectively estimate the labels of unlabelled samples.Second,an ε-drag-and-drop technique is used to ensure label-to-label separability,and in addition,low-rank constraints are imposed on the slack labels to improve their intra-class tightness and similarity.Then,a class neighborhood graph is incorporated into the proposed approach to capture the local neighborhood information of all EEG sample data.Comparative experiments are conducted on two public datasets of SEED-Ⅳ and SEED-Ⅴ,and the results show that the proposed approach performs well in EEG emotion recognition.

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王雨彤,顾晓清.半监督低秩表示的脑电情感识别方法[J].南京信息工程大学学报(自然科学版),2024,16(5):643-653
WANG Yutong, GU Xiaoqing. Semi-supervised low-rank representation for EEG emotion recognition[J]. Journal of Nanjing University of Information Science & Technology, 2024,16(5):643-653

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  • 收稿日期:2023-11-02
  • 在线发布日期: 2024-10-30
  • 出版日期: 2024-09-28

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