Standardized Brainwave Image Creation for AI Training
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Solution Overview
Problem
Existing methods for analyzing brainwave signals face challenges in distinguishing minute differences and effectively training artificial intelligence models, as they often rely on topographic maps that are ambiguous at image edges and fail to consider the importance of each frequency band.
Innovation Solution
A method for creating standardized brainwave images by processing and combining signals from multiple frequency bands, using index values such as absolute power, relative power, and entropy, and arranging pixel values on a preset template to generate a single image that accounts for changes in frequency and position, allowing for improved analysis across all frequency bands and consideration of each band's importance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If topographic maps of brainwaves are used as training data for artificial intelligence models, then the data is appropriate for experts to estimate patient conditions, but the edge portions of the images are ambiguous causing poor model performance
Solution Approach 1:
The patent segments the brainwave image into multiple frequency bands (delta, theta, alpha, beta, gamma) and processes each band separately through filtering and power calculation. This segmentation allows the AI model to receive clear, distinct frequency information without edge ambiguity, improving both measurement precision and model reliability
Solution Approach 2:
The patent transforms the 2D topographic map into a multi-dimensional frequency spectrum representation by applying Fast Fourier Transform (FFT) and organizing power values across multiple frequency bands. This dimensional transformation converts ambiguous spatial edge information into structured spectral data that AI models can process reliably
2Productivity
If only a specific frequency band is considered in the image map, then the operation of identifying a specific brain disease is improved, but it becomes difficult to analyze brainwave signals in all frequency bands
Solution Approach 1:
The patent creates a universal brainwave processing system that simultaneously handles all frequency bands (delta, theta, alpha, beta, gamma) through a single standardized workflow. The same filtering, power calculation, and FFT processes are applied across all bands, making the system versatile for analyzing any frequency range while maintaining high efficiency for specific disease identification
Solution Approach 2:
The patent merges the analysis of all frequency bands into a single comprehensive image map where power values from delta, theta, alpha, beta, and gamma bands are organized together in a unified spectral representation. This combining allows simultaneous analysis of all frequency bands while preserving the ability to focus on specific bands for particular disease identification
3Device complexity
If brainwave signals are analyzed without considering the importance of each frequency band, then the processing is simpler, but the analysis accuracy decreases
Solution Approach 1:
The patent applies local quality by calculating power values and applying FFT processing specifically to each frequency band (delta, theta, alpha, beta, gamma) based on their individual characteristics. Each band receives tailored processing appropriate to its frequency range, improving analysis accuracy while maintaining manageable complexity through systematic organization
Solution Approach 2:
The patent changes parameters by calculating power values for each frequency band and organizing them in a structured spectral representation with distinct frequency ranges. This parameter organization allows the system to consider the importance of each frequency band through their respective power measurements without overwhelming complexity
Data Source
AI summary
Provided are a method, apparatus, and computer program for creating a standardized brainwave image for training an artificial intelligence model. The method of creating a standardized brainwave image for training an artificial intelligence model executed by a computing device according to various embodiments of the present invention includes collecting a plurality of brainwave signals of a user, processing the plurality of collected brainwave signals, and creating a brainwave image using the plurality of processed brainwave signals.