Neuro-informatics Repository System for Multi-Modal Data Integration
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional systems for managing neurological and neurophysiological data are inefficient and inaccurate due to semantic, syntactic, metaphorical, cultural, social, and interpretative errors and biases, limiting their practical use in storing and accessing data from neurological and neurophysiological measurements.
Innovation Solution
A neuro-informatics repository system that integrates multiple data models to efficiently store, manage, and query neurological and neurophysiological data using techniques such as EEG, EOG, GSR, and other measurement modalities, blending central nervous system, autonomic nervous system, and effector data to provide accurate assessments of stimulus material, with adaptive data cleansing and visualization tools.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If survey based evaluations or isolated neurophysiological measurements are used, then data collection is simple, but measurement precision and reliability are limited due to semantic, syntactic, and interpretative errors
Solution Approach 1:
The patent combines multiple data modalities including neurophysiological measurements (EEG, EOG, GSR), survey evaluations, and contextual metadata into a unified repository system. This integration allows cross-validation of data sources and reduction of errors associated with any single measurement method, thereby improving measurement precision while managing complexity through systematic organization.
Solution Approach 2:
The repository system is designed to handle diverse data types from multiple modalities through a unified architecture. The system provides multi-functional capabilities including data ingestion, storage, processing, and retrieval across different neurological measurement types, eliminating the need for separate systems for each modality and reducing overall system complexity.
2Reliability
If multiple data modalities are integrated, then measurement precision and reliability improve, but device complexity and data processing requirements increase
Solution Approach 1:
The repository system is organized into distinct functional modules including data ingestion components for different modalities, processing modules for specific data types, and retrieval systems. This segmentation allows each component to be optimized independently while maintaining overall system reliability through modular architecture, reducing the impact of complexity on system-wide reliability.
3Loss of information
If conventional systems store neurological data in isolation, then storage is efficient, but loss of information occurs due to inability to integrate contextual and multi-modal data
Solution Approach 1:
The patent introduces a centralized repository system that acts as an intermediary between diverse data sources and analysis tools. This intermediary consolidates multi-modal neurological data with contextual information, preventing information loss through integration while providing efficient access through unified query interfaces and standardized data structures.
4Measurement precision
If survey based evaluations are used alone, then ease of operation is maintained, but measurement precision deteriorates due to semantic and cultural biases
Solution Approach 1:
The system replaces reliance on subjective survey-based mechanical evaluation processes with automated neurophysiological measurement systems (EEG, EOG, GSR) that objectively capture neurological responses. This substitution eliminates semantic and cultural biases inherent in human interpretation while maintaining operational simplicity through automated data collection and processing pipelines.
Data Source
AI summary
A neuro-informatics repository system is provided to allow efficient generation, management, and access to central nervous system, autonomic nervous system, effector data, and behavioral data obtained from subjects exposed to stimulus material. Data collected using multiple modalities such as Electroencephalography (EEG), Electrooculography (EOG), Galvanic Skin Response (GSR), Event Related Potential (ERP), surveys, etc., is stored using a variety of data models to allow efficient querying, report generation, analysis and/or visualization.