Detecting acute pain signals from human EEG.
Detecting acute pain signals from human EEG.
复制标题
检测人类脑电图的急性疼痛信号。
DOI：
10.1016/j.jneumeth.2020.108964
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发表时间：
2021-01-01
影响因子：
3
通讯作者：
Chen ZS
中科院分区：
文献类型：
--
作者：
Sun G;Wen Z;Ok D;Doan L;Wang J;Chen ZS
人类神经影像学的进步使我们能够研究疼痛状态下不同脑区之间的功能联系。尽管有大量的高解剖分辨率的研究，确切的神经信号的时间疼痛仍然知之甚少。识别来自分布式皮层回路的疼痛信号的发作可以揭示疼痛反应的时间动力学，并随后为疼痛的闭环神经调节提供重要的反馈。在这里，我们开发了一种无监督学习方法，用于基于多通道人类EEG记录的急性疼痛信号的顺序检测。在EEG源定位之后，我们使用状态空间模型（SSM）基于局部感兴趣区域（ROI）来检测急性疼痛信号的发作。我们使用两个人类EEG数据集验证了基于SSM的检测策略，其中包括50名受试者的公开EEG记录。我们发现，检测准确性因受试者和检测方法而异。我们还证明了跨学科和跨模态预测检测急性疼痛信号的可行性。与基于支持向量机（SVM）分类器的批量监督学习分析相比，无监督学习方法在在线实验中需要较少的训练试验次数，并且表现出与监督方法相当或改进的性能。我们的无监督SSM为基础的方法结合EEG源定位显示出强大的性能检测急性疼痛信号的发作。
Advances in human neuroimaging has enabled us to study functional connections among various brain regions in pain states. Despite a wealth of studies at high anatomic resolution, the exact neural signals for the timing of pain remain little known. Identifying the onset of pain signals from distributed cortical circuits may reveal the temporal dynamics of pain responses and subsequently provide important feedback for closed-loop neuromodulation for pain. Here we developed an unsupervised learning method for sequential detection of acute pain signals based on multichannel human EEG recordings. Following EEG source localization, we used a state-space model (SSM) to detect the onset of acute pain signals based on the localized regions of interest (ROIs). We validated the SSM-based detection strategy using two human EEG datasets, including one public EEG recordings of 50 subjects. We found that the detection accuracy varied across tested subjects and detection methods. We also demonstrated the feasibility for cross-subject and cross-modality prediction of detecting the acute pain signals. In contrast to the batch supervised learning analysis based on a support vector machine (SVM) classifier, the unsupervised learning method requires fewer number of training trials in the online experiment, and shows comparable or improved performance than the supervised method. Our unsupervised SSM-based method combined with EEG source localization showed robust performance in detecting the onset of acute pain signals.
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影响因子：
4.3
作者：
Gramfort A;Luessi M;Larson E;Engemann DA;Strohmeier D;Brodbeck C;Goj R;Jas M;Brooks T;Parkkonen L;Hämäläinen M
通讯作者：
Hämäläinen M
影响因子：
4
作者：
Chen Z;Zhang Q;Tong APS;Manders TR;Wang J
通讯作者：
Wang J
DOI：
10.1093/cercor/bhx237
发表时间：
2018-10-01
期刊：
Cerebral cortex (New York, N.Y. : 1991)
影响因子：
--
作者：
Liberati G;Klöcker A;Algoet M;Mulders D;Maia Safronova M;Ferrao Santos S;Ribeiro Vaz JG;Raftopoulos C;Mouraux A
通讯作者：
Mouraux A
影响因子：
5.7
作者：
Downar, J;Mikulis, DJ;Davis, KD
通讯作者：
Davis, KD
影响因子：
2.5
作者：
Hu, Sile;Zhang, Qiaosheng;Chen, Zhe
通讯作者：
Chen, Zhe