Excited to share a paper we've been stewing for a while looking into ambiguity in defining phase for brain rhythms and how one can use metrics of uncertainty to identify moments when phase is less ambiguous.
https://doi.org/10.1101/2023.01.05.522914
#neuroscience #brain #tootprint #preprint
We show how we can use confidence limits on the phase coming from a Hilbert transform and credible intervals from the state space model to define moments of time ("high confidence") when the phase will be correlated across these different methods. Here's an example for AR(2) sim data.
The big message we wanted to convey is that depending on what is intended for the phase to track (do you want it to act like a clock or do you want it to tell you when the peak/trough is reached?) you might want to consider alternative methods and use uncertainty metrics.
I focused in this #tootprint on one situation when the phase can become quite ambiguous - amplitude modulation, but in the paper we consider other cases as well including non-sinusoidal oscillations which lead to other considerations - do check it out! - https://doi.org/10.1101/2023.01.05.522914