1. Brain EEG Time-Series Clustering Using Maximum-Weight Clique
- Author
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Jia Wu, Qin Zhang, Dechang Pi, Stefanie I. Becker, Blake W. Johnson, Chenglong Dai, and Lin Cui
- Subjects
0209 industrial biotechnology ,Time Factors ,Computer science ,Physics::Medical Physics ,02 engineering and technology ,Electroencephalography ,020901 industrial engineering & automation ,Similarity (network science) ,0202 electrical engineering, electronic engineering, information engineering ,medicine ,Cluster Analysis ,Artificial Intelligence & Image Processing ,Electrical and Electronic Engineering ,Cluster analysis ,Independence (probability theory) ,Clique ,Quantitative Biology::Neurons and Cognition ,medicine.diagnostic_test ,business.industry ,Brain ,0102 Applied Mathematics, 0801 Artificial Intelligence and Image Processing, 0906 Electrical and Electronic Engineering ,Pattern recognition ,Graph ,Computer Science Applications ,Human-Computer Interaction ,Control and Systems Engineering ,Brain-Computer Interfaces ,Unsupervised learning ,Graph (abstract data type) ,020201 artificial intelligence & image processing ,Artificial intelligence ,business ,Algorithms ,Software ,Information Systems - Abstract
Brain electroencephalography (EEG), the complex, weak, multivariate, nonlinear, and nonstationary time series, has been recently widely applied in neurocognitive disorder diagnoses and brain-machine interface developments. With its specific features, unlabeled EEG is not well addressed by conventional unsupervised time-series learning methods. In this article, we handle the problem of unlabeled EEG time-series clustering and propose a novel EEG clustering algorithm, that we call mwcEEGc. The idea is to map the EEG clustering to the maximum-weight clique (MWC) searching in an improved Fréchet similarity-weighted EEG graph. The mwcEEGc considers the weights of both vertices and edges in the constructed EEG graph and clusters EEG based on their similarity weights instead of calculating the cluster centroids. To the best of our knowledge, it is the first attempt to cluster unlabeled EEG trials using MWC searching. The mwcEEGc achieves high-quality clusters with respect to intracluster compactness as well as intercluster scatter. We demonstrate the superiority of mwcEEGc over ten state-of-the-art unsupervised learning/clustering approaches by conducting detailed experimentations with the standard clustering validity criteria on 14 real-world brain EEG datasets. We also present that mwcEEGc satisfies the theoretical properties of clustering, such as richness, consistency, and order independence.
- Published
- 2022
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