Accurate eye blink artifact detection is essential for electroencephalogram (EEG) analysis and auxiliary analysis of nervous system diseases, especially in the presence of the frontal epileptiform discharges. In this paper, we develop a novel eye blink artifact detection algorithm based on optimally selected multi-dimensional EEG features. Specific efforts have been paid to filtering the frontal epileptiform discharges, where an unsupervised learning exploiting the EEG signal physiological characteristics and smooth nonlinear energy operator (SNEO) based on the K-means clustering has been firstly proposed. Multiple statistical EEG features derived from the frontal electrodes and other electrodes are then extracted to characterize eye blink artifacts. Discriminative feature selection scheme based on the variance filtering and Relief algorithms has been respectively studied, and the average correlation coefficient (ACC) is applied for feature optimization evaluation. The eye blink artifact detection is finally achieved based on the support vector machine (SVM) trained on the optimized EEG features. The effectiveness of the proposed algorithm is demonstrated by experiments carried out on the EEG database of 11 subjects recorded from the Children’s Hospital, Zhejiang University School of Medicine (CHZU). Comparisons to several state-of-the-art (SOTA) eye blink artifact detection methods are also presented.

中文翻译：

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具有新颖优化的多维脑电图特征的眨眼伪影检测

准确的眨眼伪影检测对于脑电图 (EEG) 分析和神经系统疾病的辅助分析至关重要，尤其是在存在额叶癫痫样放电的情况下。在本文中，我们开发了一种基于优化选择的多维 EEG 特征的新型眨眼伪影检测算法。在过滤额叶癫痫样放电方面做出了具体的努力，其中首次提出了一种利用 EEG 信号生理特征和基于 K 均值聚类的平滑非线性能量算子 (SNEO) 的无监督学习。然后提取来自额电极和其他电极的多个统计 EEG 特征来表征眨眼伪影。分别研究了基于方差滤波和缓解算法的判别特征选择方案，并应用平均相关系数(ACC)进行特征优化评价。眨眼伪影检测最终基于在优化的 EEG 特征上训练的支持向量机 (SVM) 实现。通过对浙江大学医学院儿童医院（CHZU）记录的 11 名受试者的 EEG 数据库进行的实验证明了所提出算法的有效性。还介绍了与几种最先进 (SOTA) 眨眼伪影检测方法的比较。眨眼伪影检测最终基于在优化的 EEG 特征上训练的支持向量机 (SVM) 实现。通过对浙江大学医学院儿童医院（CHZU）记录的 11 名受试者的 EEG 数据库进行的实验证明了所提出算法的有效性。还介绍了与几种最先进 (SOTA) 眨眼伪影检测方法的比较。眨眼伪影检测最终基于在优化的 EEG 特征上训练的支持向量机 (SVM) 实现。通过在浙江大学医学院儿童医院（CHZU）记录的 11 名受试者的 EEG 数据库上进行的实验证明了所提出算法的有效性。还介绍了与几种最先进 (SOTA) 眨眼伪影检测方法的比较。