A FAST SOLUTION TO ROBUST MINIMUM VARIANCE BEAMFORMER AND APPLICATION TO SIMULTANEOUS MEG AND LOCAL FIELD POTENTIAL

Biosignal Estimation and Classification

Přednášející: Penny Probert Smith, Autoři: Hamid Mohseni, Morten Kringelbach, Mark Woolrich, Penny Probert Smith, Tipu Aziz, University of Oxford, United Kingdom

In this study, a robust minimum variance beamformer (RMVB) is employed for source reconstruction in simultaneous MEG and local field potential (LFP) measurements. RMVB selects the electrical activity only from a specified volume of the brain while suppressing the rest. To improve imaging we added two more terms to the RMVB: the first term minimises the mean squared error (i.e. maximises the correlation) between the recorded LFP and reconstructed time courses from MEG data, the second term nulls the large inference induced by deep brain stimulation (DBS) device. A solution of this problem, relevant both with and without extra terms, is presented using the Lagrange multiplier method. This solution---if we ignore the new terms---has a simpler secular equation compared to its original solution and therefore is faster. The method is validated using both simulated and real data to show its potential use in practical applications.

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