EEG Brain Activity Characterization via ODE Mathematical Models

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Solution Overview

Problem

Existing methods for measuring brain activity, such as fMRI and EEG, face challenges with high noise levels in EEG signals and the need for improved data processing techniques to gain insights into human brain function.

Innovation Solution

A method and system that utilize EEG output to determine a mathematical model comprising ordinary differential equations (ODEs) to characterize brain activity, allowing for the isolation of linear and non-linear patterns, and identify unique features for subject identification and cognitive state analysis.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Ease of operation

If EEG signals are used to measure brain activity, then the measurement process is non-invasive and relatively simple, but the noise level in the signals is high

Engineering Contradiction:

Improveease of measurementVSAvoidsignal noise level

Core Design Contradiction:

Ease of operationVSMeasurement precision

Solution Approach 1:

The patent introduces mathematical models as an intermediary between the raw EEG signals and the brain activity characterization. These models process and filter the noisy signals, extracting meaningful patterns while eliminating noise, thus resolving the contradiction between easy measurement and precise measurement.

Inventive Principle:

Principle #24Intermediary (Mediator)

Solution Approach 2:

The patent replaces direct physical measurement interpretation with mathematical modeling and computational analysis. Instead of relying solely on physical signal processing, the system uses ordinary differential equations and mathematical transformations to extract brain activity information from noisy EEG signals.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Loss of information

If complex data processing techniques are applied to EEG data, then better insights into brain function can be obtained, but the processing complexity increases

Engineering Contradiction:

Improveinformation extraction qualityVSAvoiddata processing complexity

Core Design Contradiction:

Loss of informationVSDevice complexity

Solution Approach 1:

The patent transforms the EEG data through mathematical parameter changes, converting raw signals into modeled representations that reveal brain activity patterns. By changing the mathematical parameters and using differential equations, the system extracts meaningful information without requiring excessively complex processing architectures.

Inventive Principle:

Principle #35Parameter changes

Solution Approach 2:

The patent creates mathematical copies or models of brain activity based on EEG signals. Instead of directly analyzing the complex raw signals, the system generates simplified mathematical representations that capture the essential brain activity patterns, reducing processing complexity while maintaining information quality.

Inventive Principle:

Principle #26Copying

Data Source

PatentUS20240374217A1Systems and methods for characterizing brain activity

Publication Date: 2024.11.14 TEXAS A&M UNIVERSITY

AI summary

A method of characterizing brain activity includes receiving an electroencephalogram (EEG) output. In addition, the method includes determining a mathematical model of a brain using the EEG output, wherein the mathematical model comprises a plurality of ordinary differential equations (ODEs) that are determined based on the EEG output. Further, the method includes characterizing brain activity of a subject using the mathematical model.