Abstract
EEG signals play an important role in both the diagnosis of neurological diseases and understanding the psychophysiological processes. Classification of EEG signals includes feature extraction and feature classification. This paper uses approximate entropy and sample entropy based on wavelet package decomposition as the feature exaction methods and employs support vector machine and extreme learning machine as the classifiers. Experiments are performed in epileptic EEG data and five mental tasks, respectively. Experimental results show that the combination strategy of sample entropy and extreme learning machine has shown great performance, which obtains good classification accuracy and low training time.
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Acknowledgments
This work is partly supported by National Natural Science Foundation of China (No. 61373127), the China Postdoctoral Science Foundation (No. 20110491530), and the University Scientific Research Project of Liaoning Education Department of China (No. 2011186).
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Zhang, Y., Zhang, Y., Wang, J. et al. Comparison of classification methods on EEG signals based on wavelet packet decomposition. Neural Comput & Applic 26, 1217–1225 (2015). https://doi.org/10.1007/s00521-014-1786-7
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DOI: https://doi.org/10.1007/s00521-014-1786-7