This technology introduces a methodology that computes the phase-locking value (PLV) of two narrowband signals by directly relating it to their signal-to-noise ratios (SNRs). It involves filtering signals to the target frequency and estimating SNR at each time point across multiple trials. By approximating the instantaneous phases as Gaussian-distributed, the method derives a simple formula, PLV(t) = exp[–(1/SNR₁ + 1/SNR₂)], which quantifies phase synchrony almost instantaneously. This streamlined procedure eliminates reliance on time-bin approaches like Gabor wavelet convolution, thereby enabling continuous dynamic measurements.
Description
The approach stands out because it addresses the limitations of conventional methods that compromise temporal resolution. Instead of segmenting data into discrete bins to extract phases, it offers a real-time evaluation of functional connectivity. This direct link between SNR and phase consistency not only enhances computational efficiency but also provides deeper insights into neuronal signal interactions. Its mathematical rigor and intuitive grounding allow for faster and more accurate assessments, making it a significant improvement over traditional techniques commonly used in brain connectivity studies.
Applications
- Brain connectivity diagnostics
- MEG analysis software
- Neurofeedback optimization
- Advanced BCI interfaces
Advantages
- Enables instantaneous measurement of phase synchrony with high temporal resolution
- Eliminates the temporal limitations of time-bin methods used in traditional Gabor wavelet convolution
- Provides a rigorous, mathematically derived approach based on SNR for accurate functional connectivity analysis
- Facilitates faster computation and easy implementation in dynamic neural signal studies
IP Status
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