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Updating dynamic noise models with moving magnetoencephalographic (MEG) systems

Lopez, J. D.; Tierney, T. M.; Sucerquia, A.; Valencia, F.; Holmes, N.; Mellor, S.; Roberts, G.; Hill, R.; Bowtell, R.; Brookes, M. J.; Barnes, G. R.

Updating dynamic noise models with moving magnetoencephalographic (MEG) systems Thumbnail


J. D. Lopez

T. M. Tierney

A. Sucerquia

F. Valencia

N. Holmes

S. Mellor

G. Roberts

R. Hill

G. R. Barnes


Optically pumped magnetometers have opened many possibilities for the study of human brain function using wearable moveable technology. In order to fully exploit this capability, a stable low-field environment at the sensors is required. One way to achieve this is to predict (and compensate for) changes in the ambient magnetic field as the subject moves through the room. The ultimate aim is to account for dynamically changing noise environments by updating a model based on measurements from a moving sensor array. We begin by demonstrating how an appropriate environmental spatial noise model can be developed through Free-energy based model selection. We then develop a Kalman-filter based strategy to account for dynamically changing interference. We demonstrate how such a method could not only provide realistic estimates of interfering signals when the sensors are moving, but also provide powerful predictive performance (at a fixed point within the room) when both sensors and sources of interference are in motion.

Journal Article Type Article
Acceptance Date Jan 1, 2019
Online Publication Date Jan 7, 2019
Publication Date Jan 7, 2019
Deposit Date Feb 21, 2019
Publicly Available Date Feb 21, 2019
Journal IEEE Access
Electronic ISSN 2169-3536
Publisher Institute of Electrical and Electronics Engineers
Peer Reviewed Peer Reviewed
Volume 7
Issue 1
Pages 10093-10102
Keywords General Engineering; General Materials Science; General Computer Science
Public URL


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