Generalized sidelobe canceller for magnetoencephalography arrays

In the last decade, large arrays of sensors for magnetoencephalography (MEG) (and electroencephalography (EEG)) have become more common place, allowing new opportunities for the application of beamforming techniques to the joint problems of signal estimation and noise reduction. We introduce a new a...

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Bibliographic Details
Published in:2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro 2009-08, Vol.2009, p.149-152
Main Authors: Mosher, J.C., Hamalainen, M.S., Pantazis, D., Hui, H.B., Burgess, R.C., Leahy, R.M.
Format: Article
Language:English
Subjects:
adaptive arrays
Array signal processing
Electroencephalography
Estimation
Humans
Magnetic sensors
Magnetoencephalography
Noise cancellation
Noise reduction
Sensor arrays
spatial filters
Working environment noise
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Summary:In the last decade, large arrays of sensors for magnetoencephalography (MEG) (and electroencephalography (EEG)) have become more common place, allowing new opportunities for the application of beamforming techniques to the joint problems of signal estimation and noise reduction. We introduce a new approach to noise cancellation, the generalized sidelobe canceller (GSC), itself an alternative to the linearly constrained minimum variance (LCMV) algorithm. The GSC framework naturally fits within the other noise reduction techniques that employ real or virtual reference arrays. Using expository human subject data with strong environmental and biological artifacts, we demonstrate a straightforward sequence of steps for practical noise filtering, applicable to any large array sensor design.
ISSN:1945-7928
1945-8452
DOI:10.1109/ISBI.2009.5193005