A complex cross-spectral distribution model using Normal Variance Mean Mixtures

Author

Palmer, J.A. ; Makeig, S. ; Kreutz-Delgado, K.

Author_Institution

Swartz Center for Comput. Neurosci., Univ. of California, La Jolla, CA

fYear

2009

fDate

19-24 April 2009

Firstpage

3569

Lastpage

3572

Abstract

We propose a model for the density of cross-spectral coefficients using normal variance mean mixtures. We show that this model density generalizes the corresponding marginal density of the complex Wishart distribution for the cross-spectral density. The maximum likelihood estimate of parameters in the distribution is derived, and examples are given from alpha brain wave sources in separated EEG data.

Keywords

electroencephalography; maximum likelihood estimation; medical signal processing; EEG data; alpha brain wave sources; complex Wishart distribution; complex cross-spectral distribution model; cross-spectral coefficients; marginal density; maximum likelihood estimation; normal variance mean mixtures; Biomedical signal processing; Brain modeling; Coherence; Electroencephalography; Frequency estimation; Independent component analysis; Phase estimation; Random processes; Rhythm; Scalp; coherence; cross-spectrum; estimation; phase;

fLanguage

English

Publisher

ieee

Conference_Titel

Acoustics, Speech and Signal Processing, 2009. ICASSP 2009. IEEE International Conference on

Conference_Location

Taipei

ISSN

1520-6149

Print_ISBN

978-1-4244-2353-8

Electronic_ISBN

1520-6149

Type

conf

DOI

10.1109/ICASSP.2009.4960397

Filename

4960397