Brain EEG Time Series Selection: A Novel Graph-Based Approach for Classification

TL;DR

This paper introduces a graph-based EEG selection method that enhances classification accuracy by selecting similar and discriminative EEG segments, addressing noise issues in cerebral disease analysis.

Contribution

A novel maximum weight clique-based EEG selection approach that considers both edge and vertex weights for improved classification performance.

Findings

Outperforms state-of-the-art EEG selection algorithms.

Effectively selects intra-clique similar EEGs and inter-clique discriminative EEGs.

Demonstrates robustness on real-world EEG datasets.

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 Fr\'{e}chet 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.