Neuroheadset Reference Averaging for Low-Noise Bioelectrical Sensing
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Solution Overview
Problem
Existing bioelectrical signal collection technologies face challenges in simultaneously measuring multiple signals while preventing cross-talk and effectively eliminating noise, which affects the accuracy of psychological and physiological monitoring.
Innovation Solution
A biomonitoring neuroheadset system with multiple bioelectrical and reference sensors that utilize averaging and noise reduction techniques to generate accurate bioelectrical signal datasets, reducing common mode noise and improving signal fidelity by averaging reference signals from multiple locations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If multiple bioelectrical sensors are used to simultaneously measure multiple signals, then the quantity of measurable signals increases, but cross-talk between sensors increases and measurement precision deteriorates
Solution Approach 1:
The patent divides the sensing system into multiple independent sensor channels, each equipped with its own preamplifier and signal processing circuitry. This segmentation isolates each measurement channel from others, preventing cross-talk while maintaining the ability to simultaneously measure multiple bioelectrical signals such as EEG, ECG, and EMG.
Solution Approach 2:
The patent introduces reference electrodes as intermediary elements that provide a common reference potential for multiple measurement channels. These reference electrodes act as mediators that enable simultaneous measurements without direct interference between channels, as each channel measures differential voltage relative to the shared reference rather than directly interacting with other measurement channels.
2Object-affected harmful factors
If traditional noise reduction techniques are used, then some noise is reduced, but common mode noise from multiple sensors accumulates and measurement precision deteriorates
Solution Approach 1:
The patent implements active noise reduction using feedback mechanisms where the system continuously monitors the electrical potential at multiple sensor locations and actively adjusts reference potentials to cancel out common mode noise. The processor analyzes signals from multiple channels and generates corrected reference signals that feed back into the measurement system, dynamically reducing common mode interference that would otherwise accumulate from multiple sensors.
Solution Approach 2:
The patent introduces computationally-generated reference signals as intermediary elements that mediate between the multiple physical sensors and the final measurement output. Instead of directly combining raw sensor signals, the system uses processed reference signals that have been optimized to reject common mode noise, acting as an intermediary layer that preserves signal fidelity while eliminating accumulated noise.
3Measurement precision
If reference signals from multiple locations are used, then common mode noise reduction improves, but device complexity increases
Solution Approach 1:
The patent designs the reference electrode system to serve multiple functions simultaneously: providing a common reference potential for all measurement channels, enabling active noise reduction across all sensors, and serving as additional measurement points for computational algorithms. This multi-functionality reduces the need for separate dedicated reference systems for each sensor, thereby limiting the increase in device complexity while achieving improved noise reduction through multiple reference locations.
Data Source
AI summary
A method and system for detecting bioelectrical signals from a user, including establishing bioelectrical contact between a user and one or more sensors of a biomonitoring neuroheadset; collecting one or more reference signal datasets; collecting, at the one or more sensors, one or more bioelectrical signal datasets referenced to a combined reference signal dataset; and extracting one or more bioparameters from the one or more bioelectrical signal datasets.