Attention recognition based on multi-feature fusion of electroencephalogram signals
XU Bin
GAN Liangzhi
LUAN Shengyang
LI Wei
Abstract：In order to make full use of electroencephalogram(EEG)signals features and overcome the limitation that a single feature extraction method can't fully represent EEG information,we proposed a multi-feature fusion algorithm based on permutation entropy,fuzzy entropy and average energy.Firstly,the eye and myoelectric artifacts in EEG were removed by empirical mode decomposition.Secondly,the signals were extracted with multiple features,and the features were fused into the support vector machine(SVM)for classification 40 sets of collected EEG data were experimented and the results showed that the recognition accuracy of attention and re-laxation signals reached 90.53％.The result indicates that the multi-feature fusion algorithm can enhance the feature expression ability and improve the accuracy of EEG recognition.
Keywords：ElectroencephalogramAttention recognitionPermutation entropyMean energySupport vector machineFeature fusion
Publication Date：2024-12-28
Online Publishing Date：2026-09-11(First online date of this platform, not the publication date of the document)
Pages：6( 462-467 )