Implantation regulation electroencephalogram signal strengthening method based on artificial intelligence and computer system

By employing AI-based feature representation and perturbation suppression methods, the problem of signal continuity between implanted EEG signals and their internal and external components was solved, thereby improving signal quality and diagnostic accuracy.

CN120030320BActive Publication Date: 2026-05-29BEIJING JISHUITAN HOSPITAL
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
BEIJING JISHUITAN HOSPITAL
Filing Date
2024-12-23
Publication Date
2026-05-29

AI Technical Summary

Technical Problem

Existing EEG signal processing methods struggle to simultaneously ensure both continuity and stability within and between signals. In particular, implanted EEG signals are affected by a variety of complex physiological factors, resulting in inconsistent signal quality and impacting diagnostic accuracy.

Method used

An artificial intelligence-based method is used to represent the features of implanted EEG signals. Perturbation suppression is achieved by using a potential difference smoothing matrix and a smoothing matrix factor. By combining the feature information of adjacent signal segments, smoothness and stability between signal segments are realized.

Benefits of technology

It improves the smoothness and stability between signal segments of EEG signals, enhances the accuracy and stability of signal analysis, and improves signal quality.

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Abstract

The application provides an implantation regulation and control electroencephalogram signal strengthening method and computer system based on artificial intelligence, relates to the field of data processing, and based on the application, after obtaining a signal segment disturbance suppression result, the disturbance suppression result of a previous signal segment is used to perform disturbance suppression on the current signal segment, the electroencephalogram signals in the implantable electroencephalogram signal set can be subjected to disturbance suppression within the signals and between the signals (i.e. time domain and frequency domain), so that the signal segments have better smoothness and stability on the basis of accurate disturbance suppression of the electroencephalogram signals.
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