EEG noise elimination method based on dual-density wavelet neighborhood related threshold processing

Abstract

The invention relates to an EEG noise elimination method based on dual-density wavelet neighborhood related threshold processing. At present, noise elimination is carried out on an EEG mostly by adopting classic discrete wavelet transform to be combined with a traditional threshold method, and defects exist in an existing noise elimination method with the combination of the classic wavelet transform and the traditional threshold method. The EEG noise elimination method comprises the steps: firstly collecting an EEG from a cerebral cortex, then using dual-density wavelet forward transform for conducting decomposition on the EEG to obtain multi-layer signal high-frequency coefficients, utilizing a neighborhood related threshold processing algorithm for contraction according to the partial statistics dependency of wavelet coefficients, and finally reconstructing the contacted wavelet coefficients to obtain signals with noise eliminated. According to the characteristics of the EEG and the characteristics of interference noise, the signal to noise ratio is used as an objective function, a grid optimum seeking method is adopted to seek the optimum in three adjustable parameters of the neighborhood related threshold processing algorithm, then the noise is effectively smoothed, and the detail features of the EEG are reserved.

Description

technical field [0001] The invention belongs to the field of biomedical signal denoising, and relates to a method for denoising brain electric signals based on double-density wavelet neighborhood correlation threshold processing. Background technique [0002] Electroencephalogram (Electroencephalogram, EEG) is the recruitment signal diffracted to the brain cortex by the bioelectric activity generated by the central nervous system. When people are actively thinking or receiving different sensory stimuli, the characteristics of EEG have obvious differences. By analyzing EEG, a large amount of physiological, psychological and pathological information can be obtained. EEG is a nonlinear and non-stationary signal, and it is very weak, its amplitude is only μV level, and it is very susceptible to noise interference, such as pulse interference, power frequency interference, respiratory interference, eye movement interference, ECG interference, and myoelectric interference , scalp ...

AI Technical Summary

Problems solved by technology

However, the traditional soft and hard thresholding algorithms only threshold each wavelet coefficient separately, without considering the local dependence of the wavelet coefficients, resulting in too large deviations in the estimated value of the coefficients, which in turn makes the traditional wavelet threshold denoising method less effective for EEG denoising performance. reduce

[0004] In summary, the existing EEG signal denoising methods based on classical wavelet transform and traditional soft thresholding method have deficiencies.

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