Individualized binaural beat music generation method based on electroencephalogram frequency band quantitative analysis

By analyzing multi-format EEG data and standardizing preprocessing, combined with quantitative assessment and intelligent audio synthesis, the problems of insufficient physiological drive, data compatibility and cumbersome operation of existing binaural beat music generation tools have been solved, and the stability and effectiveness of personalized neurofeedback have been achieved.

CN122272046APending Publication Date: 2026-06-26GUILIN UNIV OF ELECTRONIC TECH
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Patent Information

Application Number
CN202610631289.X
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2026-05-09
Publication Date
2026-06-26

AI Technical Summary

Technical Problem

Existing binaural beat music generation tools lack objective physiological driving mechanisms, have poor data compatibility, fragmented functions, and lack effect verification, resulting in unstable neural feedback effects and cumbersome operation.

Method used

It employs automatic parsing of multi-format EEG data, standardized preprocessing, quantitative EEG state assessment, intelligent frequency recommendation, and AM modulation audio synthesis to integrate the entire process of neural music generation, thereby achieving personalized neural feedback.

Benefits of technology

It enables the generation of personalized neural music that is accurately recommended based on the individual's EEG state, improving the stability and ease of operation of neural feedback, and has a quantitative verification mechanism to ensure the effectiveness of neural feedback.

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Abstract

The invention discloses a personalized binaural beat music generation method based on electroencephalogram frequency band quantitative analysis, and the method comprises the steps: automatically analyzing eight mainstream electroencephalogram data formats, and completing data standardization through adaptive preprocessing; a Welch periodogram method is adopted to calculate a five-frequency-band power spectrum, key clinical indexes are extracted, and quantitative state scores are output; a target beat frequency is intelligently recommended or manually selected based on an electroencephalogram state, and binaural beats are embedded into a user-selected background audio through an AM double-sideband modulation technology; a modulation effect quantitative verification link is introduced to ensure the effectiveness of neural feedback. According to the method, whole-process integration of electroencephalogram analysis, personalized recommendation, audio synthesis and playing control is realized, and a scientific, convenient and quantifiable technical scheme is provided for emotion regulation, concentration improvement and sleep improvement.
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