An algorithm for detecting oscillatory behavior in discretized data: the damped-oscillator oscillator detector

TL;DR

This paper introduces a straightforward algorithm that detects oscillatory patterns in discrete data by simulating damped oscillators and monitoring their energy, offering a potentially more effective alternative to traditional spectral analysis methods.

Contribution

The paper presents a novel, simple algorithm for oscillation detection that outperforms or matches Fourier transform techniques in various test scenarios.

Findings

Effective detection of oscillations in real brain data at 20 and 70 Hz

Comparable or superior performance to FFT in tests

Circular statistics performed poorly in comparison

Abstract

We present a simple algorithm for detecting oscillatory behavior in discrete data. The data is used as an input driving force acting on a set of simulated damped oscillators. By monitoring the energy of the simulated oscillators, we can detect oscillatory behavior in data. In application to in vivo deep brain basal ganglia recordings, we found sharp peaks in the spectrum at 20 and 70 Hz. The algorithm is also compared to the conventional fast Fourier transform and circular statistics techniques using computer generated model data, and is found to be comparable to or better than fast Fourier transform in test cases. Circular statistics performed poorly in our tests.