Electroencephalogram distortion detection and recovery method and equipment based on wavelet domain energy modulation

Through the wavelet domain energy modulation method, the existing EEG signal denoising method lacks biological interpretation ability and the adequacy of using prior domain knowledge is solved, and intelligent adaptive detection and recovery of various EEG distortion conditions is achieved, and the accuracy and robustness of signal processing are improved.

CN119719723AActive Publication Date: 2025-03-28XIAOZHOU TECH CO LTD
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Patent Information

Application Number
CN202510242390.1
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-03-03
Publication Date
2025-03-28
Estimated Expiration
2045-03-03

AI Technical Summary

Technical Problem

The existing EEG signal denoising methods lack biological interpretation ability and the adequacy of using prior domain knowledge, resulting in limitations in the denoising effect.

Method used

The EEG distortion detection and recovery method based on wavelet domain energy modulation is adopted, and the energy spectrum is obtained through time-frequency transformation, modulation is performed based on the preset intensity threshold, wavelet coefficients are extracted, and similarity calculation and weight assignment are performed according to the characteristics of the EEG type, and finally wavelet coefficient adjustment control command is generated for signal reconstruction.

Benefits of technology

It realizes intelligent adaptive detection and recovery of various EEG distortion conditions, makes full use of the time-frequency characteristics of EEG signals, improves the accuracy and robustness of distortion detection and recovery, and improves the system's adaptability and self-regulation capabilities.

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Abstract

The invention discloses an electroencephalogram distortion detection and recovery method and equipment based on wavelet domain energy modulation. The method comprises the following steps: acquiring a modulated wavelet coefficient; extracting wavelet coefficient data corresponding to each electroencephalogram type from the wavelet coefficients; extracting wavelet feature data of each electroencephalogram type; obtaining a preset normal feature corresponding to the electroencephalogram data, performing similarity calculation on each piece of wavelet feature data and the preset normal feature, and determining a target similarity calculation result and a distortion mode feature corresponding to the target similarity calculation result; obtaining a current operation stage corresponding to the electroencephalogram data, determining a weight value corresponding to each distortion mode feature according to the current operation stage, obtaining feature vectors of the distortion mode features, and generating a comprehensive distortion feature description according to a plurality of feature vectors and the corresponding weight values; and obtaining a wavelet coefficient adjustment control instruction corresponding to the comprehensive distortion feature, reconstructing the original electroencephalogram signal, and obtaining a recovered electroencephalogram signal.
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