Brain Signal Noise Reduction via Spatial Oversampling
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Solution Overview
Problem
Current methods for brain signal measurements, such as EEG and MEG, often fail to accurately capture signals due to insufficient sensor density, leading to noise from ambient and channel-specific sources, which spatial oversampling can mitigate but is not commonly used in commercial practices due to increased costs and perceived lack of essential accuracy.
Innovation Solution
Implementing a method that uses an array of sensors spaced at least half the theoretical minimum Nyquist sampling rate, with spatial oversampling of at least 25% to 100%, and a geodesic sensor net configuration with clusters of sensors to reduce noise by identifying and subtracting unique variance from measurements, utilizing principles like principal factors analysis to isolate and remove channel-specific noise.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If spatial oversampling is implemented by increasing sensor density beyond the minimum Nyquist rate, then measurement precision is improved, but device complexity and cost increase
Solution Approach 1:
The patent implements spatial oversampling by placing sensors at intervals smaller than the theoretical minimum Nyquist spacing (using a spacing factor of 0.4-0.6 instead of 1.0). This excessive sampling beyond the minimum requirement captures spatial frequencies more accurately, allowing for better noise characterization and removal, thereby improving measurement precision while the computational methods handle the increased data load
Solution Approach 2:
The patent introduces an intermediary computational process that analyzes variance patterns across multiple sensors to identify and remove noise. By using principal factors analysis and variance decomposition, the system mediates between the raw oversampled data and the final cleaned signal, extracting useful information while eliminating noise without requiring proportional increases in hardware complexity
2Measurement precision
If spatial oversampling is implemented by increasing sensor density, then measurement precision is improved, but manufacturing cost increases
Solution Approach 1:
The patent changes the sampling interval parameter to be smaller than the minimum Nyquist spacing (using spacing factors of 0.4-0.6). This parameter modification enables the system to capture more spatial frequency information and better characterize noise patterns, improving measurement precision and noise reduction capability while maintaining a practical sensor array configuration
Solution Approach 2:
The patent uses multiple sensors spaced at oversampled intervals to capture redundant information about the same underlying signal and noise patterns. By analyzing correlations and common variance across these replicated measurements, the system can identify and remove noise more effectively, improving precision without requiring each individual sensor to be more expensive
3Device complexity
If sensors are spaced at minimum Nyquist rate, then device complexity is reduced, but measurement precision deteriorates due to insufficient noise characterization
Solution Approach 1:
The patent applies spatial oversampling by using sensor spacing intervals that are 40-60% of the minimum Nyquist spacing. This excessive sampling provides redundant measurements that enable better noise characterization through variance analysis, improving signal fidelity while keeping the sensor array configuration relatively simple and manageable
Data Source
AI summary
A method and apparatus for reducing noise in brain signal measurements. The method provides an array of sensors providing for spatial oversampling, and multiple samplings over time to produce measurement data. The measurement data have a variance common to the sensors, and a remaining variance that can be safely assumed to be sensor or channel specific noise and that is accounted for by a suitable modification of the measurement data. The apparatus provides clusters of the sensors positioned in correspondence to the vertices of substantially equilateral triangles that are defined by tensile elements connecting the clusters.