EEG Brain Region Interaction Analysis for Psychiatric Cohort Identification
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Solution Overview
Problem
Current methods for diagnosing and treating psychiatric disorders are subjective and inefficient, relying heavily on trial-and-error approaches, which can lead to ineffective treatments and increased suffering due to the lack of objective physiological evidence, especially exacerbated by the COVID-19 pandemic, and existing EEG analysis techniques struggle to extract meaningful information from complex brain signals.
Innovation Solution
A computer-implemented method using scalp electroencephalograph (EEG) data to analyze brain region mutual interaction characteristics, performing multi-frequency band generalized eigenvalue decomposition to extract prominent features, and applying machine learning algorithms to identify cohorts of psychiatric disorder patients and predict treatment outcomes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If traditional subjective diagnosis methods using questionnaires are used, then ease of operation is improved, but measurement precision deteriorates
Solution Approach 1:
The patent replaces the mechanical/manual system of subjective questionnaire-based diagnosis with an automated electroencephalography (EEG) based diagnostic system. The EEG device objectively measures brain electrical activity, and the processing system automatically analyzes these signals to generate diagnostic results, eliminating reliance on patient self-reporting and clinician subjective judgment.
Solution Approach 2:
The diagnostic system enables patients to contribute to their own diagnosis through EEG data collection. The portable EEG device allows patients to perform self-monitoring of their brain activity, and the system processes this data to provide diagnostic insights, reducing the need for extensive clinician-patient interactions while maintaining or improving diagnostic accuracy.
2Adaptability or versatility
If trial-and-error treatment approaches are used, then adaptability is improved, but loss of time worsens
Solution Approach 1:
The patent performs preliminary diagnostic assessment using EEG analysis before initiating treatment. By objectively characterizing the patient's brain activity patterns and identifying specific neurological markers, the system determines the most appropriate treatment approach in advance, eliminating the need for trial-and-error testing of multiple treatments and significantly reducing the time required to achieve effective therapy.
3Measurement precision
If complex EEG signal analysis is performed, then measurement precision is improved, but device complexity worsens
Solution Approach 1:
The patent divides the complex EEG signal processing task into distinct functional modules: signal acquisition from multiple electrodes, preprocessing to remove artifacts, feature extraction to identify relevant patterns, and classification to generate diagnostic results. This modular segmentation allows each component to be optimized independently while maintaining overall system accuracy.
Solution Approach 2:
The patent introduces an intermediary processing layer that translates complex raw EEG signals into simplified, clinically interpretable features and metrics. This intermediary representation maintains the precision information from the original signals while presenting data in a form that is easier to analyze and interpret, effectively bridging the gap between complex measurement and simple interpretation.
Data Source
AI summary
A method and system for identifying cohorts of psychiatric disorder patients using electroencephalograph (EEG) are disclosed, which include analyzing a type of brain region mutual interaction characteristics using scalp EEG data from each of a group of patients and obtaining a set of brain region mutual interaction feature matrices, performing a feature enhancement process to the brain region mutual interaction feature matrices to extract prominent mutual interaction features, and applying a machine learning algorithm to identify cohorts of the group of psychiatric disorder patients. The method and system can also be used for patients diagnosed with COVID-19 and suffering from psychiatric disorder symptoms originated from COVID-19. The method and system can be used to identify responsive and non-responsive groups to a clinical treatment, different treatment outcome groups in response to a clinical treatment, or different risk groups to a clinical treatment.