title: Kernel earth mover's distance for EEG classification.
abstract: Here, we propose a new kernel approach based on the earth mover's distance (EMD) for electroencephalography (EEG) signal classification. The EEG time series are first transformed into histograms in this approach. The distance between these histograms is then computed using the EMD in a pair-wise manner. We bring the distances into a kernel form called kernel EMD. The support vector classifier can then be used for the classification of EEG signals. The experimental results on the real EEG data show that the new kernel method is very effective, and can classify the data with higher accuracy than traditional methods.
authors.affiliation: Biomedical Engineering Department, Iran University of Science and Technology, Tehran, Iran. daliri@iust.ac.ir
authors.forename: Mohammad Reza
authors.initials: MR
authors.lastname: Daliri
mesh_terms: D000465:Algorithms; D001921:Brain / Q000502:physiology*; D004569:Electroencephalography / Q000145:classification / Q000379:methods*; D006801:Humans; D010363:Pattern Recognition, Automated; D013223:Statistics as Topic
keywords: EEG signals; brain signals classification; earth mover’s distance (EMD); kernel method; support vector machines