  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.
