EEG Classification of Mental Disorders Under Cognitive Stimuli
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Solution Overview
Problem
Current diagnostic methods for mental disorders, such as fMRI, are limited by high costs, spatial and temporal restrictions, and fail to consider altered cognitive processes, making accurate differentiation between disorders like PTSD and MDD challenging due to shared symptoms.
Innovation Solution
A method and device utilizing brain wave data and brain activity data, processed through a classification model, to classify mental disorders by analyzing brain wave signals and source activity in response to specific stimuli, providing a reliable diagnostic system.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If fMRI is used to diagnose mental disorders, then diagnostic information can be obtained, but the cost is expensive and there are spatial and temporal restrictions
Solution Approach 1:
The patent replaces expensive fMRI equipment with low-cost EEG headsets that can be easily deployed. The EEG system uses disposable or reusable electrodes and headbands that are much cheaper than fMRI machines, making the diagnostic process accessible and eliminating spatial restrictions.
Solution Approach 2:
The patent substitutes the complex mechanical and optical system of fMRI with an electrical measurement system (EEG). Instead of using magnetic fields and complex imaging equipment, the system uses electrical sensors to detect brain wave patterns, simplifying the overall diagnostic apparatus.
2Ease of operation
If traditional diagnostic methods are used for mental disorders, then diagnosis can be performed, but the accuracy is low due to similar symptoms between different disorders
Solution Approach 1:
The patent transforms the diagnostic approach by changing from symptom-based assessment to objective physiological parameter measurement. By analyzing EEG brain wave patterns, spectral power, and connectivity metrics, the system provides accurate differentiation between mental disorders without relying on subjective symptom evaluation.
Solution Approach 2:
The patent introduces EEG brain wave data as an intermediary objective measure between the patient's symptoms and the diagnosis. Instead of directly assessing symptoms which can be similar across disorders, the system uses brain electrical activity as a mediator to reveal underlying neurological differences.
3Reliability
If fMRI is used to diagnose mental disorders, then neural activity can be observed, but cognitive processes are not adequately considered leading to reliability issues
Solution Approach 1:
The patent extracts and specifically analyzes cognitive process-related features from EEG data, such as event-related potentials (ERPs), attention networks, and cognitive control patterns. By isolating these cognitive markers, the system captures information that traditional fMRI analyses often overlook.
Solution Approach 2:
The patent incorporates cognitive task paradigms and stimulus presentations before EEG recording to elicit specific cognitive responses. By preparing and presenting standardized cognitive tasks in advance, the system ensures that relevant cognitive processes are activated and captured during the recording session.
Data Source
AI summary
The present invention relates to a method for provision of information on a mental disorder and a device utilizing same, the method for provision of information on a mental disorder being implemented by a processor, and comprising: outputting a stimulus to a subject in order to generate brain waves; receiving brain wave data and brain activity data measured in the subject during outputting the stimulus; and determining whether a mental disorder is present in the subject by using a classification model configured to classify mental disorders on the basis of the brain wave data and the brain activity data.