Wavelet Transform In Signal Extraction Of The Eeg Feature

Electroencephalogram signal is bioelectric behavior from nerve cell in pallium. much message of health is consisted in the electroencephalogram signal. In clinic, the EGG signals not only offer the evidence of determination of disease, but also offer the effective measurement to cure the diseases. It is very important to get and identity the electroencephalogram signal for determination of disease and explanation of health.Because the electroencephalogram signal is so important, the research of electroencephalogram has been forward from the discovery of electroencephalogram signal in 20s last century. Meanwhile, Arithmetic of electroencephalogram signal process increasingly emerged with the development of technology of signal process, Fourier transformation and power frequency spectrum are applied in electroencephalogram signal process; these means promote the development of electroencephalogram signal process.However electroencephalogram signal is random, it is difficult to get the precious result by Fourier transformation. In 80s twenty century, a new signal process way emerged, which is the wavelet transformation. Little time resolution is adopted to observe fast change of signal for high frequency, and little frequency resolution is adopted to observe slow change of signal for low frequency. Wavelet is the character of watching both tree and forest. This character is advantage to analysis random signal.The content of wave of electroencephalogram signal firstly was introduced in this article and the connected way of getting signal and the difference frequency wave. The application of wavelet transformation in getting electroencephalogram signal were discussed in this article, revulsive visual signal and filter the noise were mainly studied. This produced a lot of message in time and frequency area for analysis electroencephalogram.For electroencephalogram signal, they contain much temporary signal.These signal are very important to detect the disease. We could get the significant result from the detected these signal by means of wavelet transformation. Multi-resolution analysis is adopted in decomposing of electroencephalogram signal, so character of difference frequency range of temporary signals is observed. Pulses of more temporary signals are mixed, and a new signal is consisted by the mixed signal. Then the new signal was detected and positioned. The noise was removed from signal by the means of detecting the extent. The result was obviously availability.Meanwhile, multi-resolution decomposed the signal in low frequency range, and wavelet package decompose the signal in both low and high frequency range; This is the advantage to detect whole frequency range. The electroencephalogram signal to difference frequency range was decomposed by the means of wavelet package. It is very important to research the high and low frequency electroencephalogram signal. It will play a significant role in detecting the disease.The revulsive electroencephalogram signal is important to analysis the disease and function of brain. In this article, we got the revulsive visual electroencephalogram signal hidden in the electroencephalogram, and do the emulator. We got the good result through compared the result.