NDLI: Optimal spatial filtering for brain oscillatory activity using the Relevance Vector Machine

Content Provider	Springer Nature Link
Author	Belardinelli, P. Jalava, A. Gross, J. Kujala, J. Salmelin, R.
Copyright Year	2013
Abstract	Over the past decade, various techniques have been proposed for localization of cerebral sources of oscillatory activity on the basis of magnetoencephalography (MEG) or electroencephalography recordings. Beamformers in the frequency domain, in particular, have proved useful in this endeavor. However, the localization accuracy and efficacy of such spatial filters can be markedly limited by bias from correlation between cerebral sources and short duration of source activity, both essential issues in the localization of brain data. Here, we evaluate a method for frequency-domain localization of oscillatory neural activity based on the relevance vector machine (RVM). RVM is a Bayesian algorithm for learning sparse models from possibly overcomplete data sets. The performance of our frequency-domain RVM method (fdRVM) was compared with that of dynamic imaging of coherent sources (DICS), a frequency-domain spatial filter that employs a minimum variance adaptive beamformer (MVAB) approach. The methods were tested both on simulated and real data. Two types of simulated MEG data sets were generated, one with continuous source activity and the other with transiently active sources. The real data sets were from slow finger movements and resting state. Results from simulations show comparable performance for DICS and fdRVM at high signal-to-noise ratios and low correlation. At low SNR or in conditions of high correlation between sources, fdRVM performs markedly better. fdRVM was successful on real data as well, indicating salient focal activations in the sensorimotor area. The resulting high spatial resolution of fdRVM and its sensitivity to low-SNR transient signals could be particularly beneficial when mapping event-related changes of oscillatory activity.
Starting Page	357
Ending Page	369
Page Count	13
File Format	PDF
ISSN	16124782
Journal	Cognitive Processing
Volume Number	14
Issue Number	4
e-ISSN	16124790
Language	English
Publisher	Springer Berlin Heidelberg
Publisher Date	2013-06-01
Publisher Place	Berlin, Heidelberg
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	RVM Beamformer Magnetoencephalography MEG inverse problem Cortical rhythms Source localization Bayesian approaches Neurosciences Behavioural Sciences Artificial Intelligence (incl. Robotics)
Content Type	Text
Resource Type	Article
Subject	Experimental and Cognitive Psychology Artificial Intelligence Cognitive Neuroscience Medicine

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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