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.

Abstract

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.

Citation

Lopez, J. D., Tierney, T. M., Sucerquia, A., Valencia, F., Holmes, N., Mellor, S., …Barnes, G. R. (2019). Updating dynamic noise models with moving magnetoencephalographic (MEG) systems. IEEE Access, 7(1), 10093-10102. https://doi.org/10.1109/access.2019.2891162

• 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
• DOI: https://doi.org/10.1109/access.2019.2891162
• Keywords: General Engineering; General Materials Science; General Computer Science
• Public URL: https://nottingham-repository.worktribe.com/output/1574683