Eye Blink Artifact Detection With Novel Optimized Multi-Dimensional Electroencephalogram Features

• Published in: IEEE transactions on neural systems and rehabilitation engineering Vol. 29; pp. 1494 - 1503
• Main Authors: Wang, Jianhui, Cao, Jiuwen, Hu, Dinghan, Jiang, Tiejia, Gao, Feng
• Format: Journal Article
• Language: English
• Published: New York IEEE 2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Cluster analysis; Clustering; Correlation coefficient; Correlation coefficients; Detection algorithms; Discharge; Discharges (electric); EEG; Electrodes; Electroencephalography; Epilepsy; Eye blink artifact detection; Feature extraction; Filtration; Firing pattern; frontal epileptiform discharge screening; multi-dimensional feature extraction and optimization; Nervous system; Nervous system diseases; Optimization; SNEO; Support vector machines; Vector quantization
• DOI: 10.1109/TNSRE.2021.3099232
• ISSN: 1534-4320; 1558-0210

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.