Voluntary Action Intention Detection Using Respiratory-Neural Coupling
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Solution Overview
Problem
Existing brain-machine interfaces (BMIs) primarily rely on brain signals, disregarding respiratory and cardiac signals as physiological noise, despite evidence suggesting they impact voluntary actions and readiness potentials (RPs), lacking a clear association between respiration and voluntary action, which is crucial for improving BMI reliability and understanding unconscious-to-conscious signal processing.
Innovation Solution
A method and system that determine the intention of performing a voluntary action by analyzing the coupling between respiratory phases and neuroelectrical signals, specifically using respiratory signals to modulate Readiness Potential (RP) amplitude and Event-Related Desynchronization (ERD) power, establishing a time-based correlation to predict voluntary actions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If respiratory and cardiac signals are treated as physiological noise in BMIs, then device complexity is reduced, but measurement precision and reliability of voluntary action detection deteriorate
Solution Approach 1:
The patent converts the previously harmful respiratory and cardiac signals (treated as noise) into beneficial information sources. By analyzing the coupling between respiratory phases and neuroelectrical signals, the system transforms what was considered physiological interference into a reliable indicator for detecting voluntary action intention, thereby improving measurement precision without significantly increasing system complexity
Solution Approach 2:
The patent introduces respiratory signals as an intermediary element that mediates between the brain's voluntary action intention and the BMI system. By using respiratory phase information as a mediator to modulate and interpret neuroelectrical signals, the system achieves more accurate detection of voluntary actions while maintaining a relatively simple implementation architecture
2Ease of operation
If only brain signals are used in BMIs, then ease of operation is maintained, but reliability of action prediction deteriorates
Solution Approach 1:
The patent merges brain signals with respiratory and cardiac signals into a unified analysis framework. By combining multiple physiological signal sources that are naturally coupled during voluntary actions, the system improves prediction reliability while maintaining ease of operation through integrated signal processing that leverages the natural correlations between these physiological parameters
3Reliability
If respiratory signals are analyzed to determine voluntary action intention, then reliability and measurement precision improve, but device complexity increases
Solution Approach 1:
The patent applies preliminary action by pre-processing and analyzing respiratory signals to extract phase information before combining them with neuroelectrical signals. This preliminary extraction of respiratory phase characteristics simplifies the subsequent analysis and reduces the computational complexity of the overall system while maintaining improved reliability
Data Source
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AI summary
The invention relates to methods and systems for determining the intention of a subject to perform a voluntary action based on the analysis of the subject's respiratory phases and neuroelectrical signals.