Background

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.

New method

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).

Results

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.

Comparison with existing methods

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.

Conclusions

Our unsupervised SSM-based method combined with EEG source localization showed robust performance in detecting the onset of acute pain signals.

中文翻译：

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检测人类脑电图的急性疼痛信号

 背景

人类神经影像学的进步使我们能够研究疼痛状态下不同大脑区域之间的功能连接。尽管进行了大量高解剖分辨率的研究，但疼痛发生时间的确切神经信号仍然知之甚少。识别来自分布式皮质回路的疼痛信号的出现可以揭示疼痛反应的时间动态，并随后为疼痛的闭环神经调节提供重要的反馈。

 新方法

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脑电图记录顺序检测急性疼痛信号。在脑电图源定位之后，我们使用状态空间模型（SSM）根据局部感兴趣区域（ROI）检测急性疼痛信号的发作。

 结果

我们使用两个人类脑电图数据集（包括 50 名受试者的公开脑电图记录）验证了基于 SSM 的检测策略。我们发现，不同测试对象和检测方法的检测准确度有所不同。我们还证明了检测急性疼痛信号的跨受试者和跨模态预测的可行性。

与现有方法的比较

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更少的训练试验次数，并且表现出与监督方法相当或改进的性能。

 结论

我们基于 SSM 的无监督方法与 EEG 源定位相结合，在检测急性疼痛信号的发作方面表现出强大的性能。