Weighted EEG Brain Network Analysis for Stroke Hand Movement
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Solution Overview
Problem
Conventional EEG-based connectivity studies focus on resting states and binary network analysis, failing to adequately analyze information processing in brain network structures during motor performance in stroke patients, particularly between ipsilesional and contralesional hemispheres, and lack standard methods to determine significant interactions.
Innovation Solution
Employing weighted network analysis to measure and compare brain network indices, such as strength, clustering coefficient, and small-worldness, during hand movements in stroke patients, using EEG to provide motor impairment-related information for prognosis prediction and rehabilitation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If binary network analysis is used to analyze brain network properties in stroke patients, then the analysis can be simplified and processed faster, but important information is lost due to transformation of continuous functional connectivity patterns into binary values without standard threshold determination
Solution Approach 1:
The patent changes the parameter representation from binary (0/1) to continuous weighted values, allowing functional connectivity patterns to be represented with their original magnitude information. This enables both efficient processing and preservation of important connectivity strength information through standardized threshold-free weighting.
2Ease of operation
If conventional EEG-based connectivity studies focus on resting state rather than motor performance state, then the analysis can be performed with simpler task requirements, but the ability to analyze information processing during motor performance is limited
Solution Approach 1:
The patent transitions from static resting-state analysis to dynamic motor-performance state analysis. By implementing motor tasks with standardized protocols and using real-time EEG acquisition during movement, the system captures dynamic brain network changes that occur during motor performance, providing reliable insights into information processing during actual motor function.
3Device complexity
If fragmentary PSDs from individual brain regions are used to explain abnormal brain mechanisms, then the analysis can be performed with simpler data processing, but the complex communication between different brain regions cannot be fully explained
Solution Approach 1:
The patent merges fragmentary PSD data from individual brain regions into a comprehensive functional connectivity analysis. By combining regional spectral power density measurements with connectivity analysis across multiple brain regions, the system reconstructs the complex communication patterns between regions, providing complete explanations of abnormal brain mechanisms during motor performance.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The method provides accurate motor impairment-related information for prognosis prediction and rehabilitation by quantifying functional connectivity changes in brain networks, enhancing the understanding of motor impairment mechanisms and aiding in treatment selection.
Implementation Method 1
measuring a brain network index according to movement performance of an affected side part of a subject
Data Source
AI summary
One aspect of the present disclosure relates to a method for providing motor impairment-related information, comprising the steps of: (a) measuring a brain network index according to movement performance of an affected side part of a subject; and (b) comparing the brain network index measured in step (a) with a brain network index according to movement performance of an unaffected side part of the subject. In addition, another aspect of the present disclosure relates to a device for providing a motor impairment-related information that provides a brain network index obtained according to movement performance of a subject. The method or device for providing motor impairment-related information of the present invention according to the present disclosure has an excellent effect of providing information on prognosis for motor impairment, motor impairment-related diagnosis, or motor impairment-related rehabilitation.