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Abstract：
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.
摘要：
动机。异常脑电检测是脑电信号分析中一个长期存在的问题。此问题的基本前提是考虑两个非平稳EEG记录之间的相似性。一个完善的方案是基于序列匹配，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特征提取和决策，其中很少涉及相似性度量/量化。一般来说,为了设计适当的相似性度量，与所考虑的问题/数据兼容，也是这种检测系统设计中的一个重要问题。然而,不可能在不考虑域特异性的情况下直接将那些现有度量应用于异常EEG检测。方法论。这项工作的主要目的是研究不同相似性度量对异常脑电检测的影响。通过仔细审查相关工作，已从其他领域收集了一些可能用于EEG分析的指标。所谓的功率谱是作为脑电信号的特征提取,并采用零假设测试来做出最终决定。已经使用两个指标来评估检测性能。一种是反映两个比较的EEG信号之间的测量相似性水平，二是量化检测精度。结果。在两个数据集上进行了实验，分别。结果表明，不同的相似性度量对异常脑电检测的积极影响。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，伯尔尼-巴塞罗那脑电图数据集的准确度为0.9667。HD和BD度量都是基于Bhattacharyya系数构建的，暗示着处理高度嘈杂的EEG信号时，Bhattacharyya系数的优先级。在今后的工作中,我们将利用结合HD和BD的集成度量来进行EEG信号的相似性度量。
