Affiliation of Author(s):计算机科学与技术学院/人工智能学院/软件学院

Journal:SIAM Int. Conf. Data Min., SDM

Abstract:Brain Electroencephalography (EEG) classification is widely applied to analyze cerebral diseases in recent years. Unfortunately, invalid/noisy EEGs degrade the diagnosis performance and most previously developed methods ignore the necessity of EEG selection for classification. To this end, this paper proposes a novel maximum weight clique-based EEG selection approach, named mwcEEGs, to map EEG selection to searching maximum similarity-weighted cliques from an improved Fréchet distance-weighted undirected EEG graph simultaneously considering edge weights and vertex weights. Our mwcEEGs improves the classification performance by selecting intra-clique pairwise similar and inter-clique discriminative EEGs with similarity threshold δ. Experimental results demonstrate the algorithm effectiveness compared with the state-of the-art time series selection algorithms on real-world EEG datasets. © 2018 by SIAM.

Translation or Not:no

Date of Publication:2018-01-01

Co-author:代成龙,吴佳,崔琳

Correspondence Author:Pi Dechang