Neural Cross-Frequency Coupling Analysis System for Low-Latency Stimulation
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Solution Overview
Problem
Conventional systems for neural cross-frequency coupling analysis are limited by processing latency, which hampers their responsiveness in applications such as closed-loop neural stimulation devices, particularly in pathological brain states like epilepsy and Parkinson's disease.
Innovation Solution
A system and method for neural cross-frequency coupling analysis that includes processors configured to extract phase frequency and amplitude frequency envelope signals, calculate measures like mean vector length modulation index (MVL-MI), cross-frequency phase locking value (CF-PLV), and heights ratio (HR), and perform surrogate analysis to determine statistical significance, optimizing for low-latency and high-accuracy processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If conventional systems are used for neural cross-frequency coupling analysis, then measurement precision can be maintained, but processing latency increases which reduces responsiveness
Solution Approach 1:
The system segments the neural signal processing into distinct functional modules: a modulation signal extractor module that separates phase and amplitude components, and a CFC measure calculator module that computes coupling metrics. This segmentation allows each module to operate independently with optimized processing, reducing overall latency while maintaining analytical precision.
Solution Approach 2:
The system performs preliminary signal preprocessing including Hilbert transform and envelope extraction before computing cross-frequency coupling measures. By preparing the signal components in advance through these preliminary actions, the subsequent CFC calculation can proceed more rapidly, reducing processing latency without sacrificing measurement accuracy.
2Measurement precision
If multiple CFC measures (MVL-MI, CF-PLV, HR) are computed to improve measurement precision, then analysis accuracy increases, but computational complexity and processing time increase
Solution Approach 1:
The system implements a universal CFC analysis platform that can compute multiple coupling measures (MVL-MI, CF-PLV, HR) using a unified architectural framework. The modulation signal extractor module and CFC measure calculator module serve multiple functions by accommodating different calculation algorithms, allowing the system to provide comprehensive CFC analysis without proportionally increasing overall system complexity.
Solution Approach 2:
The system enables selective computation of CFC measures based on specific application requirements. Rather than always computing all three measures (MVL-MI, CF-PLV, HR), the system can partially execute only the necessary measures, reducing computational complexity when full analysis is not required while maintaining sufficient precision for the given application.
3Measurement precision
If high-precision CFC analysis is performed with multiple measures and surrogate analysis, then statistical significance can be determined, but power consumption increases
Solution Approach 1:
The system dynamically adjusts the level of analysis based on operational context and power availability. The surrogate analysis module can be activated or deactivated, and the number of surrogate data points generated can be adjusted, allowing the system to maintain statistical significance determination when power is available while reducing power consumption during battery-constrained operations.
Solution Approach 2:
The system can change processing parameters such as the number of surrogate iterations, signal segment length, and calculation precision based on power constraints. By adjusting these parameters dynamically, the system maintains adequate statistical significance when possible while reducing power consumption during energy-constrained periods.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The system enables efficient and accurate detection of cross-frequency coupling, allowing for responsive neural stimulation with reduced latency and power consumption, suitable for implantable devices, and provides a tradeoff between precision and computational efficiency.
Implementation Method 1
extracting the amplitude frequency envelope signal by passing the neural signal through a Hilbert filter, creating a complex vector comprising real and imaginary components, and determining the magnitude of the complex vector
Data Source
AI summary
There is provided a system, system architecture, and method for neural cross-frequency coupling analysis. In an embodiment, the method includes: receiving neural signals; extracting a phase frequency signal and an amplitude frequency envelope signal from each of the neural signals; determining a first measure of cross-frequency coupling comprising a mean vector length modulation index (MVL-MI), determining the MVL-MI comprises determining a magnitude of an averaged complex-valued time series from a plurality of samples of the neural signals to extract a phase-amplitude coupling measure, each sample associated with a respective one of the amplitude frequency envelope signals and the phase frequency signals; and outputting at least one measure of the cross-frequency coupling.