Version 1
: Received: 18 October 2016 / Approved: 19 October 2016 / Online: 19 October 2016 (10:09:19 CEST)
How to cite:
Qin, X.; Deng, J.; Wang, M.; Zhang, Y.; Wang, P. EEG Signal Recognition Based on Wavelet Transform and ACCLN Network. Preprints2016, 2016100075. https://doi.org/10.20944/preprints201610.0075.v1
Qin, X.; Deng, J.; Wang, M.; Zhang, Y.; Wang, P. EEG Signal Recognition Based on Wavelet Transform and ACCLN Network. Preprints 2016, 2016100075. https://doi.org/10.20944/preprints201610.0075.v1
Qin, X.; Deng, J.; Wang, M.; Zhang, Y.; Wang, P. EEG Signal Recognition Based on Wavelet Transform and ACCLN Network. Preprints2016, 2016100075. https://doi.org/10.20944/preprints201610.0075.v1
APA Style
Qin, X., Deng, J., Wang, M., Zhang, Y., & Wang, P. (2016). EEG Signal Recognition Based on Wavelet Transform and ACCLN Network. Preprints. https://doi.org/10.20944/preprints201610.0075.v1
Chicago/Turabian Style
Qin, X., Yizhe Zhang and Pai Wang. 2016 "EEG Signal Recognition Based on Wavelet Transform and ACCLN Network" Preprints. https://doi.org/10.20944/preprints201610.0075.v1
Abstract
The electroencephalogram (EEG) is a record of brain activity. Brain Computer Interface (BCI) technology formed by the EEG signal has become one of the hotspots at present. How to extract the feature signal of EEG is the most basic research of BCI technology. In this paper, A new method of recognizing fatigue, conscious, concentrated state of human brain is proposed by the combination of discrete wavelet transform and the neural network based on EEG signal. First of all, the law signal is preprocessed by the wavelet denoising method because the law EEG signal contains a large number of high frequency noise, which is decomposed into multi-layer high frequency signal and low frequency signal. thus, δ wave, θ wave, α wave, β wave are obtained by the wavelet transform. And then, frequency band energy of the different wave is regards as the feature signal of EEG. In the experiment, the feature signal is classified by radial basic function (RBF) and annealed chaotic competitive learning network (ACCLN). RBF and ACCLN networks are trained with 500 sets of sample data and are tested by 100 sets of samples in different mental states. The experimental results show that the average accuracy of RBF network under three conditions are 88.75%, 88.25%, 88.5%, respectively, and the correct rate of ACCLN network is 97%, 98%, 98%, respectively.
Computer Science and Mathematics, Signal Processing
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
