Title: Brain EEG Time Series Selection: A Novel Graph-Based Approach for Classification,
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
$\delta$. Experimental results demonstrate the algorithm effectiveness compared
with the state-of-the-art time series selection algorithms on real-world EEG
datasets.