Title :
Decomposing the alpha rhythms: comparative performance evaluation of parametric bispectral algorithms for EEG
Author :
Sherman, David Lee ; Zoltowski, Michael D.
Author_Institution :
Purdue Univ., West Lafayette, IN, USA
Abstract :
The alpha wave is often harmonically related or coupled to frequencies in other EEG bands. Several autoregressive and eigenstructure-based bispectral algorithms have proven to be effective in the detection and estimation of alpha coupling. The authors extend the triple Kronecker product method of Swindlehurst and Kailath (1989) to estimation of biphases through a generalized eigenvalue approach. As a major concentration of significant bispectral peaks lie in the alpha-alpha coupling region, low model order can be used for accurate results with these methods
Keywords :
eigenvalues and eigenfunctions; electroencephalography; medical signal processing; parameter estimation; signal detection; spectral analysis; EEG; alpha coupling; alpha rhythms; estimation of biphases; generalized eigenvalue approach; parametric bispectral algorithms; performance evaluation; triple Kronecker product method; Biomedical engineering; Biomedical signal processing; Brain modeling; Eigenvalues and eigenfunctions; Electroencephalography; Frequency estimation; Narrowband; Rhythm; Signal processing algorithms; Symmetric matrices;
Conference_Titel :
Statistical Signal and Array Processing, 1992. Conference Proceedings., IEEE Sixth SP Workshop on
Conference_Location :
Victoria, BC
Print_ISBN :
0-7803-0508-6
DOI :
10.1109/SSAP.1992.246898