A geometric method for spatiotemporal coherent structure analysis

TL;DR

This paper introduces a geometric approach to analyze complex, non-stationary wave patterns in neural systems by examining Fourier spectrum fluctuations to quantify coherence and complexity.

Contribution

It presents a novel geometric method that quantifies spatiotemporal wave coherence in neural data using Fourier spectrum analysis and peak counting.

Findings

Number of peaks correlates with coherent clusters

Method effectively distinguishes different wave patterns

Provides a complexity measure for neural wave analysis

Abstract

We describe a geometric method to quantify wave patterns observed in the nervous system, which are non-stationary and with a mixture of spiral, target, plane and irregular waves. The method analyzes fluctuations of the energy angular distribution in two-dimensional Fourier spectrum of wave patterns, which reflects changes of the orientation distribution of wavefronts. We show that the number of the genuine peaks in generalized phase spectrum is close to the number of the coherent space-time clusters arising in wave patterns, and propose to use the number as a complexity measure.