Classification and Prediction of EEG based on Empirical Mode Decomposition
LI Dongmei
ZHANG Yang
YANG Ridong
CHEN Ziyi
TIAN Xianghua
DONG Nan
ALCITIN Mamat
ZHOU Yi
Abstract：EEG signals can be extracted from EEG signals, which can better understand the characteristics of EEG signals.However, due to the aliasing of various types of external signals, the signal exhibits nonlinear and nonstationarity.Therefore, for EEG signals, extraction is a problem.In this paper, an empirical mode decomposition (EMD) algorithm, which is superior to wavelet decomposition, was proposed to decompose the EEG signal and extract the eigenvalues of the main IMF components.Then, the cost-sensitive support vector machine (CSVM) was used to classify the parameters excellent.In the study of EEG signals of epilepsy patients, the accuracy of classification is more than 90%, which verifies the feasibility of this method.
Keywords：ElectroencephalogramEpilepsyEmpirical mode decompositionCost-sensitive SVMParameter optimization
Publication Date：2017-01-01
Online Publishing Date：2026-09-11(First online date of this platform, not the publication date of the document)
Pages：5( 33-37 )