Identifying Stable Brain States via RMSE Peaks in EEG Snapshots

Solution Overview

Problem

Current neuroimaging technologies face challenges in accurately identifying and characterizing the temporal dynamics of brain states during cognitive and social processes, particularly in specifying when and how specific brain areas are activated, due to limitations in temporal resolution and data analysis methods.

Innovation Solution

A computer-readable medium and method for identifying stable states in a time-ordered sequence of data with a temporal component, using root mean square error (RMSE) and global field power (GFP) analysis to detect peaks and valleys, which helps in determining stable and transition states in EEG/ERP data, enabling more precise identification of brain microstates.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If high-density EEG recordings are used to capture detailed brain activity, then measurement precision is improved, but device complexity increases

Engineering Contradiction:

Improvetemporal resolutionVSAvoiddata analysis complexity

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The patent segments continuous EEG data into discrete snapshots at specific time points, allowing detailed temporal analysis while simplifying processing through discrete time-point comparison rather than continuous analysis

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The patent introduces RMSE and GFP as intermediary metrics that mediate between raw EEG data and brain state identification, transforming complex multichannel data into comparable scalar values that indicate stable versus transition states

Inventive Principle:

Principle #24Intermediary (Mediator)

2Measurement precision

If detailed temporal information is captured in EEG data, then measurement precision is improved, but difficulty of detecting and measuring increases

Engineering Contradiction:

Improvetemporal resolutionVSAvoidbrain state identification

Core Design Contradiction:

Measurement precisionVSDifficulty of detecting and measuring

Solution Approach 1:

The patent introduces RMSE and GFP as intermediary metrics that mediate between raw EEG data and brain state identification, transforming complex multichannel data into comparable scalar values that indicate stable versus transition states

Inventive Principle:

Principle #24Intermediary (Mediator)

Solution Approach 2:

The patent replaces complex visual or manual inspection of EEG waveforms with automated computational algorithms that calculate RMSE and GFP, objectively identifying stable and transition states through mathematical operations rather than subjective interpretation

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

3Measurement precision

If more snapshots are analyzed to improve state identification accuracy, then measurement precision is improved, but loss of time increases

Engineering Contradiction:

Improvestate identification accuracyVSAvoiddata processing time

Core Design Contradiction:

Measurement precisionVSLoss of time

Solution Approach 1:

The patent segments continuous EEG data into discrete snapshots at specific time points, allowing detailed temporal analysis while simplifying processing through discrete time-point comparison rather than continuous analysis

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The patent analyzes data at selected time points (snapshots) rather than continuously processing all data, providing sufficient information for state identification while reducing overall processing time through selective sampling

Inventive Principle:

Principle #16Partial or excessive action

Data Source

PatentUS10085684B2State identification in data with a temporal dimension

Publication Date: 2018.10.02 UNIVERSITY OF CHICAGO

AI summary

States are identified in a time ordered sequence of data that have a temporal component. Data that includes a plurality of snapshots is received. Each snapshot of the plurality of snapshots includes a plurality of sensor measurements captured from distinct sensors at a common time point. The plurality of snapshots are time ordered. Root mean square error (RMSE) values are computed between successive pairs of the plurality of snapshots in time order. A peak is identified in the computed RMSE values. A valley is identified in the computed RMSE values. A stable state is determined as occurring from the identified peak to the identified valley. The determined stable state is output.