Systems Seminar

Beamspace Signal Processing for Magnetoencephalography and Electroencephalography

Prof. Barry Van Veen
ECE Department

Abstract

Magnetoencephalographic and electroencephalographic data are typically characterized by low signal to noise ratio (SNR), spatially and temporally colored noise with unknown statistics, and relatively few observations of the phenomena of interest. These constraints create very challenging problems for signal processing algorithms that image or localize brain electrical activity. In this talk we show how performing signal processing in beamspace results in significantly improved performance at low SNR and with small numbers of data observations. The term "beamspace" refers to mapping the measured data at the sensors into a lower dimensional space with a linear transformation prior to applying the signal processing algorithm of interest. Reducing the dimension significantly improves the reliability of the estimated noise statistics in algorithms that implicitly or explicitly estimate the spatial covariance matrix of the data. We show that relatively small beamspace dimensions are sufficient to accurately represent anatomically meaningful regions of the cortex. The effectiveness of beamspace processing is demonstrated by performing source localization on somatosensory data with three different algorithms. While the three algorithms involve different tradeoffs between SNR and the number of data observations, we show that beamspace processing provides dramatic performance improvement in every case when the number of observations is small.

Time and Place: Wed., Oct. 19, at 3:30 pm in 4610 Engr. Hall.

SYSTEMS SEMINAR WEB PAGE: http://homepages.cae.wisc.edu/~gubner/seminar/

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