Brain Activity Deduction via Hierarchical Bayesian Estimation
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Solution Overview
Problem
Conventional methods for deducing brain activity signals require extensive time-series data acquisition using MRI or EEG signals, necessitating prolonged subject constraint and case-by-case measurement due to individual variations in brain activity responses.
Innovation Solution
A brain activity measuring apparatus and method that computes individual conversion information using hierarchical Bayesian estimation to correlate brain activity signals between subjects, allowing for the deduction of brain activity signals without direct measurement, enabling efficient data processing and reduced measurement time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional methods use extensive time-series data acquisition (MRI or EEG) to configure cerebral nerve models, then measurement precision is improved, but measurement time and device complexity increase significantly
Solution Approach 1:
The patent creates a virtual copy of the cerebral nerve model for each subject by extracting feature vectors from actual brain activity signals. Instead of directly measuring every detail of each subject's brain activity, the system copies the essential characteristics from measured signals and uses these copies to infer complete brain activity patterns, significantly reducing measurement time while maintaining precision
Solution Approach 2:
The system performs preliminary extraction of feature vectors and configuration of cerebral nerve models during a first measurement phase. These pre-processed models are then stored and reused during subsequent measurement phases, eliminating the need to perform time-consuming model configuration for each new subject and enabling rapid brain activity inference
2Measurement precision
If conventional methods conduct case-by-case measurements for each subject, then measurement precision is maintained, but productivity decreases due to individual variations requiring separate measurements
Solution Approach 1:
The patent develops a universal cerebral nerve model framework that can be applied across multiple subjects. By extracting subject-specific feature vectors and configuring models within this universal framework, the system maintains subject-specific precision while enabling efficient batch processing of multiple subjects through standardized procedures
Solution Approach 2:
Instead of performing completely separate measurements for each subject, the system extracts feature vectors from each subject's brain activity signals and creates corresponding virtual copies of cerebral nerve models. These copies can then be used to infer brain activity patterns for multiple subjects efficiently, maintaining individual specificity while improving throughput
3Measurement precision
If conventional methods require prolonged subject constraint under large-scale measurement devices, then measurement precision is improved, but ease of operation deteriorates
Solution Approach 1:
The patent extracts essential feature vectors from brain activity signals and uses these extracted features to configure simplified virtual cerebral nerve models. This extraction approach maintains the essential information needed for precise brain activity inference while eliminating the need for prolonged subject constraint and complex large-scale measurement procedures
Solution Approach 2:
The system creates virtual copies of cerebral nerve models based on extracted feature vectors rather than requiring direct prolonged observation of actual brain activity. These virtual models preserve the essential characteristics needed for precise measurement while dramatically reducing the operational complexity and subject constraint requirements
Data Source
AI summary
What is provides is a brain activity deducing apparatus, a brain activity deducing method, a brain activity measuring apparatus, a brain activity measuring method, and a brain-machine interface device, capable of deducing a brain activity signal of a source subject. The information presentation device presents perceptible information to the first subject. The brain activity measurement device acquires a brain activity signal representing a brain activity of the first subject. The individual conversion device deduces a brain activity signal of the second subject from the acquired brain activity signal based on the individual conversion information, which correlates the brain activity signal of the first subject and the brain activity signal of the second subject.