A study on the combination of functional connection features and Riemannian manifold in EEG emotion recognition.
A study on the combination of functional connection features and Riemannian manifold in EEG emotion recognition.
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DOI：
10.3389/fnins.2023.1345770
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发表时间：
2023
影响因子：
4.3
通讯作者：
Li, Ping
中科院分区：
文献类型：
--
作者：
Wu, Minchao;Ouyang, Rui;Zhou, Chang;Sun, Zitong;Li, Fan;Li, Ping
关键词：
情感计算是人机界面智能化的核心，基于脑电信号的情感识别是当前的主要研究方向之一。此外，在脑机接口领域，黎曼流形也是一种鲁棒性强、有效的方法。然而，对称的正定性（SPD）的功能限制了它的应用。在目前的工作中，我们引入了拉普拉斯矩阵来转换功能连接特征，即，相位锁定值（PLV）、皮尔逊相关系数（PCC）、谱相干（COH）、互信息（MI）等特征量转化为半正特征量，最大化算子保证变换后的特征量为正特征量。然后利用SPD网络提取深层空间信息，并采用全连通层验证提取特征的有效性。特别地，采用决策层融合策略以获得更准确和稳定的识别结果，并研究了不同特征组合的分类性能差异。此外，还研究了应用于功能连接特征的最佳阈值。采用主题相关交叉验证策略，在公共情感数据集SEED上对所提方法进行了测试。四个特征的平均准确率结果表明，PCC优于其他三个特征。该模型对PLV、PCC和COH的融合达到了最好的91.05%的准确率，其次是所有四个特征的融合，准确率为90.16%。实验结果表明，四个功能连接特征的最佳阈值总是保持在一个固定的区间内相对稳定。实验结果证明了该方法的有效性。
Affective computing is the core for Human-computer interface (HCI) to be more intelligent, where electroencephalogram (EEG) based emotion recognition is one of the primary research orientations. Besides, in the field of brain-computer interface, Riemannian manifold is a highly robust and effective method. However, the symmetric positive definiteness (SPD) of the features limits its application. In the present work, we introduced the Laplace matrix to transform the functional connection features, i.e., phase locking value (PLV), Pearson correlation coefficient (PCC), spectral coherent (COH), and mutual information (MI), to into semi-positive, and the max operator to ensure the transformed feature be positive. Then the SPD network is employed to extract the deep spatial information and a fully connected layer is employed to validate the effectiveness of the extracted features. Particularly, the decision layer fusion strategy is utilized to achieve more accurate and stable recognition results, and the differences of classification performance of different feature combinations are studied. What's more, the optimal threshold value applied to the functional connection feature is also studied. The public emotional dataset, SEED, is adopted to test the proposed method with subject dependent cross-validation strategy. The result of average accuracies for the four features indicate that PCC outperform others three features. The proposed model achieve best accuracy of 91.05% for the fusion of PLV, PCC, and COH, followed by the fusion of all four features with the accuracy of 90.16%. The experimental results demonstrate that the optimal thresholds for the four functional connection features always kept relatively stable within a fixed interval. In conclusion, the experimental results demonstrated the effectiveness of the proposed method.
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DOI：
10.3390/s21051678
发表时间：
2021-03-01
期刊：
Sensors (Basel, Switzerland)
影响因子：
--
作者：
Keelawat P;Thammasan N;Numao M;Kijsirikul B
通讯作者：
Kijsirikul B
影响因子：
6.1
作者：
Lin, Xuefen;Chen, Jielin;Wang, Yuchen
通讯作者：
Wang, Yuchen
影响因子：
--
作者：
Plutchik, R
通讯作者：
Plutchik, R
影响因子：
11.2
作者：
Koelstra, Sander;Muhl, Christian;Patras, Ioannis (Yiannis)
通讯作者：
Patras, Ioannis (Yiannis)
影响因子：
6
作者：
Mohammadi, Zeynab;Frounchi, Javad;Amiri, Mahmood
通讯作者：
Amiri, Mahmood