2014/01/15 by William Marshall, Marshall, William, Paul Marriott +1
Biochemistry, Genetics and Molecular Biology · Computer Science · Mathematics · Neuroscience · Physics and Astronomy · #Applications (stat.AP) #Chaos control and synchronization #FOS: Biological sciences #FOS: Computer and information sciences #Neural Networks and Applications #Neural dynamics and brain function #Neurons and Cognition (q-bio.NC) #q-bio.NC #stat.AP
paper · pdf · doi:10.48550/arxiv.1401.3790
20 pages, 8 figures, 2 tables
arxiv created 2014/01/15 · openalex publication_date 2014/01/15 · arxiv updated 2014/01/17 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
We consider the problem of change-point estimation of the instantaneous phase of an observed time series. Such change points, or phase shifts, can be markers of information transfer in complex systems; their analysis occurring in geology, biology and physics, but most notably in neuroscience. We develop two non-parametric approaches to this problem: the cumulative summation (CUSUM) and phase derivative (PD) estimators. In general the CUSUM estimator has higher power for identifying single shift events, while the PD estimator has better temporal resolution for multiple ones. A system of weakly coupled Rossler attractors provides an application in which there are high levels of systematic and time-dependent noise. Shift identification is also performed on beta-band activity from electroencephalogram recordings of a visual attention task, an unsupervised application which requires high temporal resolution.