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Open AccessArticle

EEG Signals Feature Extraction Based on DWT and EMD Combined with Approximate Entropy

by 1, 1, 1 and 1,2,*
1
College of Electronic and Optical Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210023, China
2
Nation-Local Joint Project Engineering Lab of RF Integration & Micropackage, Nanjing University of Posts and Telecommunications, Nanjing 210023, China
*
Author to whom correspondence should be addressed.
Brain Sci. 2019, 9(8), 201; https://doi.org/10.3390/brainsci9080201
Received: 26 July 2019 / Revised: 6 August 2019 / Accepted: 12 August 2019 / Published: 14 August 2019
(This article belongs to the Collection Collection on Theoretical and Computational Neuroscience)
The classification recognition rate of motor imagery is a key factor to improve the performance of brain–computer interface (BCI). Thus, we propose a feature extraction method based on discrete wavelet transform (DWT), empirical mode decomposition (EMD), and approximate entropy. Firstly, the electroencephalogram (EEG) signal is decomposed into a series of narrow band signals with DWT, then the sub-band signal is decomposed with EMD to get a set of stationary time series, which are called intrinsic mode functions (IMFs). Secondly, the appropriate IMFs for signal reconstruction are selected. Thus, the approximate entropy of the reconstructed signal can be obtained as the corresponding feature vector. Finally, support vector machine (SVM) is used to perform the classification. The proposed method solves the problem of wide frequency band coverage during EMD and further improves the classification accuracy of EEG signal motion imaging. View Full-Text
Keywords: classification recognition rate; discrete wavelet transform; empirical mode decomposition; intrinsic mode functions; approximate entropy classification recognition rate; discrete wavelet transform; empirical mode decomposition; intrinsic mode functions; approximate entropy
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Ji, N.; Ma, L.; Dong, H.; Zhang, X. EEG Signals Feature Extraction Based on DWT and EMD Combined with Approximate Entropy. Brain Sci. 2019, 9, 201.

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