Title :
The linear process mixture model
Author :
Palmer, Jason A. ; Kreutz-Delgado, Kenneth ; Makeig, Scott
Author_Institution :
Swartz Center for Comput. Neurosci., Univ. of California San Diego, La Jolla, CA, USA
Abstract :
We consider a likelihood framework for analyzing multivariate time series as mixtures of independent linear processes. We propose a flexible, Newton algorithm for estimating impulse response functions associated with independent linear processes and an EM-based finite mixture model to handle intermittent regimes. Simulations and application to EEG are also provided.
Keywords :
Newton method; electroencephalography; expectation-maximisation algorithm; medical signal processing; time series; EEG; EM-based finite mixture model; Newton algorithm; electroencephalography; expectation-maximization model; impulse response function estimation; independent linear process; linear process mixture model; maximum likelihood framework; multivariate time series analysis; Brain modeling; Hidden Markov models; Least squares approximations; Newton method; Signal processing algorithms; Technological innovation;
Conference_Titel :
Machine Learning for Signal Processing (MLSP), 2013 IEEE International Workshop on
Conference_Location :
Southampton
DOI :
10.1109/MLSP.2013.6661925
