[FieldTrip] power suppression during DICS beamforming ?

[Maité Crespo García]

Dear Frédéri,
maybe you have the solution already.
In case not, could you explain how did you get the last two plots? Are spectra computed after averaging across all virtual channel spectra or you are representing the spectrum of the source with maximal power?
Best,Maite



    El Jueves 26 de noviembre de 2015 13:55, Frédéric Roux <f.roux at bcbl.eu> escribió:
 

 Dear all,

I am observing a counter-intuitive result after applying the spatial filters obtained from DICS beamforming to my MEG data.

The plot in the attachment summarizes the issue: The raw data shows a clear peak of activity at 10 Hz, however, after applying the spatial filter to the MEG data the spectrum of the virtual channels shows that power in this frequency band is reduced. The same also happens when I apply a spatial filter in the gamma range for power at 70 Hz to the raw MEG signals.

Is this something that falls out of the beamformer math due to the fact how the algorithm suppresses power at all locations by minimizing the noise level, or is there something wrong with my code? I (maybe naively) assumed that the results should come out the opposite. On the other hand, when plotting the noise normalized maps (NAI) of both the alpha and gamma beamformers the sources look quite accurate. Should I normalize with the level of noise first before plotting the spectrum to see the expected results (ie an upregulation of power instead of a downregulation of power)?
 
The code that I use to generate the figures is:

% 1) compute CSD matrix
cfg = [];
cfg.method = 'mtmfft';
cfg.output = 'powandcsd';
cfg.foilim = [10 10];
cfg.tapsmofrq = 2;
cfg.pad = 'maxperlen';
cfg.taper = 'dpss';

[csd] = ft_freqanalysis(cfg,meg_data);


% 2) compute DICS filter
cfg = [];
cfg.method = 'dics';
cfg.grad = meg_data.grad;
cfg.headmodel = vol;
cfg.frequency = csd.freq;

cfg.dics.realfiter = 'yes';
cfg.dics.fixedori = 'yes';

[alpha_filter]= ft_sourceanalysis(cfg,csd);

% 3) compute the virtual channel data

VC = meg_data;
VC.trial = cell(1,length(meg_data.trial));
VC.label = cell(1,length(alpha_filter.avg.pow));

for i = 1:length(VC.trial)
  for j = 1:length(VC.label)

      VC.trial{i}(j,:) = alpha_filter{j}*meg_data.trial{i};
      VC.label(j) = {['virtual_channel',num2str(j)]};
  end;
end;

% 4) compute the spectrum of the virtual channel data
cfg = [];
cfg.method = 'mtmfft';
cfg.pad = 'maxperlen';
cfg.taper = 'dpss';
cfg.tapsmofrq = 1;
cfg.foi = 0.1:100;

[pow] = ft_freqanalysis(cfg,VC);


Any help or suggestions would be greatly appreciated.

Fred

--
Frédéric Roux
Postdoctoral Scientist, Marie-Curie fellow
BCBL. Basque Center on Cognition, Brain & Language.

f.roux at bcbl.eu
Tel: +34 943 309 300 Ext 211
Fax: +34 943 309 052

Legal disclaimer/Aviso legal/Lege-oharra: www.bcbl.eu/legal-disclaimer
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