Neural oscillations in various distinct frequency bands and their interrelations yield high temporal resolution signatures of the human brain activity. This study demonstrates solutions to some of the common challenges in the analysis of neurophysiological data by means of subthalamic local field potentials (LFP) acquired form patients with Parkinson's Disease (PD) undergoing deep brain stimulation therapy. Multivariate empirical mode decomposition (MEMD), being a data-driven method suitable for multichannel data, is employed. This method allows identification of oscillatory bands without the requirement of fixed a priori basis functions. Our study focuses on two issues: (i) Determination of data specific frequency bands and revealing the weak inconspicuous high frequency components in the data and (ii) validation of the biological meaningfulness of the MEMD oscillatory components via phase-amplitude coupling as previously shown to be inherent in subcortical PD LFP data.
['Tolga Esat Özkurt', 'Markus Butz', 'Jan Hirschmann', 'Alfons Schnitzler']
Adaptive identification of oscillatory bands from subcortical neural data