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- W2348170483 abstract "In order to improve accuracy of mental task classification,we propose a new method of EEG classification with feature extraction.First,the raw signals are decomposed by wavelet packet decomposition(WPD).Then,using wavelet packet entropy reflecting the distribution of signal energy in time and frequency domains,the best basis of wavelet packets is selected from a wavelet packet library according to the wavelet packet entropy.Afterwards the statistical features are used to represent the best basis wavelet coefficients.Moreover,the eigenvector is obtained by calculating the asymmetry ratio of the hemispheric brainwave at each electrode in different mental tasks.Finally,the performance of the eigenvector is evaluated via a support vector machines classifier.A publicly available EEG database was used to validate this study.Compared to the conventional WPD,wavelet packet best basis decomposition and existing autoregressive feature extraction methods,the average accuracy for the proposed method ranged from 95.41% to 99.65% for ten different combinations of five mental tasks." @default.
- W2348170483 created "2016-06-24" @default.
- W2348170483 creator A5021580006 @default.
- W2348170483 date "2013-01-01" @default.
- W2348170483 modified "2023-09-24" @default.
- W2348170483 title "A New Method of EEG Classification with Feature Extraction Based on Wavelet Packet Decomposition" @default.
- W2348170483 hasPublicationYear "2013" @default.
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