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所属单位：计算机科学与技术学院/人工智能学院/软件学院
发表刊物：Appl. Soft Comput. J.
摘要：This paper explores multi-trial EEG (Electroencephalography signal) clustering and proposes a novel centroid-based approach for it. It firstly utilizes an improved cross correlation to measure similarities of multi-trial EEGs and then proposes an optimal EEG feature extraction to seek cluster centroids based on the improved cross correlation similarities. Finally, it leads to a novel algorithm called MTEEGC for multi-trial EEG clustering. MTEEGC yields high-quality multi-trial EEG clustering with respect to the intra-cluster compactness as well as the inter-cluster scatter. Meanwhile, it also demonstrates the superiority of MTEEGC in clustering accuracy over 10 state-of-the-art time series clustering algorithms through a detailed experimentation using standard cluster validity criteria on 5 real-world multi-trial EEG datasets. Especially, compared with the worst and the best algorithms in the 10 baseline algorithms, MTEEGC respectively achieves 36.11% and 2.53% mean improvements with clustering accuracy (i.e., RI) on 5 multi-trial EEG datasets. © 2018 Elsevier B.V.
ISSN号：1568-4946
是否译文：否
发表时间：2018-10-01
合写作者：代成龙,崔琳,朱延龙
通讯作者：皮德常