Non-contact electromagnetic brain wave physiotherapy method and system

By using non-contact EEG electromagnetic therapy, pulse phase time axis and reinforcement learning technology are used to adaptively adjust the pulse electromagnetic field parameters, solving the comfort and stability problems of contact EEG stimulation devices and achieving efficient EEG activity regulation and sleep induction.

CN122141126APending Publication Date: 2026-06-05NANCHANG YAOGUANG TECHNOLOGY CO LTD
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Patent Information

Authority / Receiving Office
CN ยท China
Patent Type
Applications(China)
Current Assignee / Owner
NANCHANG YAOGUANG TECHNOLOGY CO LTD
Filing Date
2026-05-06
Publication Date
2026-06-05

AI Technical Summary

Technical Problem

Among existing insomnia intervention techniques, contact EEG devices are uncomfortable, easily affected by changes in hair, skin condition, and body position, and have adverse effects with long-term use.

Method used

The non-contact EEG electromagnetic therapy method maps EEG signal data to a pulse phase time axis, corrects the EEG signal data sequence by combining the repetition component constraint of the pulse phase label, and uses reinforcement learning to construct an electromagnetic field building unit driving parameter set to adaptively adjust the parameters of the pulse electromagnetic field to regulate EEG activity.

Benefits of technology

It achieves non-contact, highly comfortable, and individualized EEG activity modulation, reduces interference from pulsed electromagnetic fields on EEG acquisition, and improves sleep induction effect and stability.

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Abstract

The application relates to the technical field of physiotherapy, in particular to a non-contact electromagnetic physiotherapy method and system for brain waves. The non-contact electromagnetic physiotherapy system for brain waves comprises an electroencephalogram signal acquisition module, an electroencephalogram signal feature data construction module and a pulse electromagnetic field physiotherapy module. The electroencephalogram signal data is mapped to a pulse phase time axis, and the electroencephalogram signal data sequence is corrected in combination with the repetition component constraint of the pulse phase label, so that the periodic interference component introduced in the pulse electromagnetic field action process can be effectively inhibited, the influence on the electroencephalogram acquisition result and the frequency band energy calculation is reduced, and the accuracy and stability of the electroencephalogram signal feature data extraction are improved. Based on the corrected electroencephalogram signal feature data, a reinforcement learning method is used to construct a driving parameter set of an electromagnetic field construction unit, and a reward value is calculated in combination with a standard curve of brain wave sleep to update a driving parameter construction network, so that the sleep induction effect of the pulse electromagnetic field regulation is improved.
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