EEG Correlation Analysis for ADHD Diagnostic Scoring
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Solution Overview
Problem
Current methods fail to effectively generate a score indicative of cognitive conditions like ADHD using EEG/MEG data, as they lack proper treatment of top-down attention signals and struggle to differentiate between stimulus-bounded surprise and uncertainty, leading to inefficiencies in diagnosing and assessing cognitive disorders.
Innovation Solution
An apparatus and method that calculate correlation values between specific time series from EEG/MEG data, focusing on source positions in the frontal lobe, ventral and dorsal attention networks, and ventral visual pathways to generate a score indicative of ADHD, leveraging the Shannonian brain model to reconcile surprise and uncertainty.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional EEG/MEG analysis methods are used to assess cognitive conditions, then the measurement process is simple, but the diagnostic accuracy and ability to distinguish ADHD from healthy subjects is insufficient
Solution Approach 1:
The patent segments the brain into specific functional regions (frontal lobe, ventral attention network, dorsal attention network, ventral visual pathway) and analyzes time series from each region separately. This segmentation allows targeted calculation of correlation values between specific brain networks, improving diagnostic accuracy for ADHD by focusing on relevant neural pathways rather than analyzing all brain regions uniformly.
Solution Approach 2:
The patent transforms raw EEG/MEG signals into multiple derived parameters including time series, correlation values, and a composite score. By changing the parameter representation from raw signals to processed metrics that specifically capture attention network dynamics, the system achieves better differentiation between ADHD and healthy subjects while maintaining manageable analysis complexity.
2Loss of information
If correlation values between all time series are calculated, then comprehensive brain network analysis is achieved, but the computational complexity and processing time increase significantly
Solution Approach 1:
The patent extracts and focuses only on correlation values between time series from specific brain regions relevant to attention processing (frontal lobe, ventral and dorsal attention networks, ventral visual pathway). By extracting only the most diagnostically relevant correlations rather than calculating all possible pairwise correlations, the system maintains comprehensive analysis of attention networks while significantly reducing computational burden and processing time.
3Adaptability or versatility
If prior art modeling approaches are used, then bottom-up visual attention is modeled, but top-down attention signals are not properly treated
Solution Approach 1:
The patent merges the analysis of both bottom-up and top-down attention mechanisms by simultaneously examining correlation values between time series from ventral visual pathways (bottom-up) and frontal lobe/attention network regions (top-down). This integration allows the model to capture the full spectrum of attention processing, improving both the coverage and reliability of cognitive condition assessment.
Data Source
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AI summary
The present disclosure relates to the generation of a score that is indicative for a subject having a cognitive condition, such as a cognitive disorder like attention deficit hyperactivity disorder, ADHD. In particular, the score may be generated based on electro- or magnetoencephalography (EEG/MEG) data by calculating one or more correlation values between pairs of time series that are determined based on the EEG/MEG data. An apparatus, system, computer-readable medium and computer-implemented method described herein receive EEG/MEG data of a subject. Then a first plurality of time series is determined based on the EEG/MEG data, wherein each of the first plurality of times series corresponds to a respective source position located inside a cranial cavity of the subject. Then, a first correlation value may be calculated for a first pair of time series, the first pair of time series being included in the determined first plurality of time series. A score is generated based on the first correlation value, the score being indicative for the subject having a cognitive condition, such as a cognitive disorder like attention deficit hyperactivity disorder, ADHD. The score is outputted, e.g., to a physician who may diagnose the subject.