The decomposition and reconstruction formula of orthogonal wavelet is determined by the scale equation coefficient of scale function. Multi resolution analysis is the core theory of wavelet analysis, and Mallat algorithm is a common algorithm for signal wavelet decomposition and reconstruction.
#Dsp builder quartus verification#
DSP Builder combines the algorithm development, simulation and verification functions of MATLAB / Simulink design simulation tool with HDL synthesis, simulation and verification functions of Quartus II software to provide a good platform for FPGA of wavelet transform. Then, wavelet transform based on FPGA emerges as the times require in EEG digital processing, which has good real-time performance.
#Dsp builder quartus Pc#
In the past, the digital processing of EEG signal was realized by general PC or single chip microcomputer, but the real-time performance was poor. If wavelet packet method is introduced into EEG analysis, it can not only overcome the shortcomings of traditional EEG analysis, but also improve Mallat algorithm to analyze the actual EEG.
Therefore, wavelet packet decomposition has better filtering characteristics. It not only decomposes the low frequency part, but also decomposes the high frequency part twice, which can adjust the frequency resolution to be consistent with the characteristics of EEG rhythm. Wavelet packet decomposition is a more detailed signal decomposition and reconstruction method extended from wavelet decomposition. But wavelet decomposition only decomposes the low frequency part of the last decomposition, not the high frequency part, so the resolution of high frequency band is poor. Only by combining time and frequency can better effect be achieved. Many lesions in EEG are expressed in the form of transient. This is just what is needed to analyze EEG.
It is very suitable for analyzing the transient characteristics and time-varying characteristics of non-stationary signals. Because the filters used in traditional filtering denoising methods generally have low-pass characteristics, the classical filtering method is used to denoise non-stationary signals, reduce noise, broaden waveform, and smooth the components of abrupt peaks in signals, However, the important information carried by these mutation points may be lost, and Fourier spectrum analysis is only a pure frequency analysis method, which is invalid for time-varying non-stationary EEG signals.Ĭompared with the traditional Fourier transform, wavelet transform is a multi-scale signal analysis method, which has good time-frequency localization characteristics. Since Berger discovered EEG in 1929, many kinds of digital signal processing techniques have been used to process and analyze EEG signals. Due to the nonstationary and random characteristics of EEG, it is very difficult to filter real-time signals. EEG (electroencephalography) is a basic physiological signal of human body, which has important clinical diagnosis and medical value.