Bayesian approaches to the analysis of task related coherent activity in the basal ganglia

Author

Cassidy, M.J. ; Brown, P.

Author_Institution

Sobell Dept. of Motor Neurosci. & Movement Disorders, Inst. of Neurology, London, UK

fYear

2003

fDate

20-22 March 2003

Firstpage

477

Lastpage

478

Abstract

In this paper, we outline a suitable methodology for the analysis of nonstationary electrophysiological signals. The methodology is founded on a Bayesian approach to spectral estimation, which offers definite advantages in objectivity as compared to other approaches. The analysis of such signals is important in experimental paradigms where one is interested in tracking changes in spectral power or coherence. We describe how this methodology has been successfully applied to scalp EEG and deep brain local field potentials recorded from Parkinsonian patients, and used to deduce task related changes in power and coherence that are relevant to the understanding of the neural organisation of voluntary movement.

Keywords

Bayes methods; Kalman filters; autoregressive processes; bioelectric potentials; electroencephalography; hidden Markov models; medical signal processing; neurophysiology; synchronisation; Bayesian approach; Kalman filter; Parkinsonian patients; autoregressive approach; basal ganglia; deep brain local field potentials; frequency-specific changes; hidden Markov model; model parameters; neural organisation; nonstationary electrophysiological signals; prior distributions; scalp EEG; spectral estimation; subthalamic nucleus; synchronisation; task related coherent activity; voluntary movement; Basal ganglia; Bayesian methods; Electroencephalography; Hidden Markov models; Nervous system; Neuroscience; Parkinson´s disease; Scalp; Signal analysis; Spectral analysis;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Engineering, 2003. Conference Proceedings. First International IEEE EMBS Conference on

Print_ISBN

0-7803-7579-3

Type

conf

DOI

10.1109/CNE.2003.1196865

Filename

1196865