Motivation. Anomaly EEG detection is a long-standing problem in analysis of EEG signals. The basic premise of this problem is consideration of the similarity between two nonstationary EEG recordings. A well-established scheme is based on sequence matching, typically including three steps: feature extraction, similarity measure, and decision-making. Current approaches mainly focus on EEG feature extraction and decision-making, and few of them involve the similarity measure/quantification. Generally, to design an appropriate similarity metric, that is compatible with the considered problem/data, is also an important issue in the design of such detection systems. It is however impossible to directly apply those existing metrics to anomaly EEG detection without any consideration of domain specificity. Methodology. The main objective of this work is to investigate the impacts of different similarity metrics on anomaly EEG detection. A few metrics that are potentially available for the EEG analysis have been collected from other areas by a careful review of related works. The so-called power spectrum is extracted as features of EEG signals, and a null hypothesis testing is employed to make the final decision. Two indicators have been used to evaluate the detection performance. One is to reflect the level of measured similarity between two compared EEG signals, and the other is to quantify the detection accuracy. Results. Experiments were conducted on two data sets, respectively. The results demonstrate the positive impacts of different similarity metrics on anomaly EEG detection. The Hellinger distance (HD) and Bhattacharyya distance (BD) metrics show excellent performances: an accuracy of 0.9167 for our data set and an accuracy of 0.9667 for the Bern-Barcelona EEG data set. Both of HD and BD metrics are constructed based on the Bhattacharyya coefficient, implying the priority of the Bhattacharyya coefficient when dealing with the highly noisy EEG signals. In future work, we will exploit an integrated metric that combines HD and BD for the similarity measure of EEG signals.

中文翻译：

---

脑电图信号中的异常检测：相似性测量的案例研究。


动机。异常脑电图检测是脑电图信号分析中长期存在的问题。该问题的基本前提是考虑两个非平稳脑电图记录之间的相似性。一个完善的方案基于序列匹配，通常包括三个步骤：特征提取、相似性度量和决策。目前的方法主要集中在脑电图特征提取和决策上，很少涉及相似性测量/量化。一般来说，设计与所考虑的问题/数据兼容的适当的相似性度量也是此类检测系统设计中的一个重要问题。然而，在不考虑领域特异性的情况下，不可能直接将这些现有指标应用于异常脑电图检测。方法论。这项工作的主要目的是研究不同相似性指标对异常脑电图检测的影响。通过仔细审查相关工作，从其他领域收集了一些可能可用于脑电图分析的指标。提取所谓的功率谱作为脑电图信号的特征，并采用零假设检验来做出最终决定。使用两个指标来评估检测性能。一是反映两个比较的脑电信号之间测量的相似程度，二是量化检测精度。结果。分别在两个数据集上进行实验。结果证明了不同相似性度量对异常脑电图检测的积极影响。 Hellinger 距离 (HD) 和 Bhattacharyya 距离 (BD) 指标显示出出色的性能：我们的数据集的准确度为 0.9167，准确度为 0。9667 为伯尔尼-巴塞罗那脑电图数据集。 HD 和 BD 指标都是基于 Bhattacharyya 系数构建的，这意味着在处理高噪声脑电信号时 Bhattacharyya 系数优先。在未来的工作中，我们将利用一种结合 HD 和 BD 的集成度量来测量 EEG 信号的相似性。