In order to more effectively extract the characteristic value of the EEG are three ways to compare the characteristic value extraction: ar model power spectral density estimation based on wavelet packet decomposition coefficients and sub-band energy feature extraction, the common spatial pattern alg

In order to extract the brain more efficient telecommunications, the characteristic values, this is a method to compare 3 to feature Value Extraction: AR model power spectral density estimate, based on wavelet packet decomposition coefficients and with energy, and the feature extraction algorithms c

For the more effective extraction brain electrical signal characteristic value, this article used three methods to contrast carries on the characteristic value extraction: The AR model power spectral density estimated, based on small wave packet resolution ratio and innertube energy characteristic e

For a more efficient extraction of EEG features value, this article uses a comparison of three methods for feature extraction: AR model power spectrum density estimation based on wavelet packet energy feature extraction and subband decomposition coefficient, common spatial pattern and its algorithm

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