Khomdet Phapatanaburi1, Kasidit kokkhunthod2, Longbiao Wang3, Talit Jumphoo2, Monthippa Uthansakul2, Anyaporn Boonmahitthisud4, Peerapong Uthansakul2,*
CMC-Computers, Materials & Continua, Vol.66, No.3, pp. 3029-3044, 2021, DOI:10.32604/cmc.2021.014433
Abstract A brainwave classification, which does not involve any limb movement and stimulus for character-writing applications, benefits impaired people, in terms of practical communication, because it allows users to command a device/computer directly via electroencephalogram signals. In this paper, we propose a new framework based on Empirical Mode Decomposition (EMD) features along with the Gaussian Mixture Model (GMM) and Kernel Extreme Learning Machine (KELM)-based classifiers. For this purpose, firstly, we introduce EMD to decompose EEG signals into Intrinsic Mode Functions (IMFs), which actually are used as the input features of the brainwave classification for the character-writing application. We hypothesize that EMD… More >
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