Application of Wavelet Transform in The Construction of Short-term Memory EEG Information Transmission Model

Abstract

Wavelet transform is an analysis method that combines time, frequency (or scale) domains. It has: (1) Multi-resolution; (2) The relative bandwidth is constant; (3) Proper selection of basic wavelet can make wavelet have the ability to represent the local characteristics of signals in both time and frequency domains, and it is known as "the microscope for analyzing signals". An analysis method of EEG signal based on autoregressive model (ARM) and wavelet transform is proposed, and it is used to eliminate noise interference in EEG signal. Wavelet transform is a multi-resolution time scale analysis method, which can divide the signal into sub-band signals of different frequency bands. According to this characteristic of wavelet transform, the EEG signals obtained by sampling are decomposed and denoised at various scales, and the results of decomposition and denoising at various scales are given. Wavelet transform can effectively remove noise interference from EEG signals. Wavelet transform is a multi resolution time scale analysis method that can divide signals into subband signals of different frequency bands. According to this characteristic of wavelet transform, the sampled EEG signal is decomposed and denoised at various scales, and the decomposition and denoising results at each scale are given. Wavelet transform can effectively remove noise interference from EEG signals.