<div dir="ltr"><span style="font-family:arial,sans-serif;font-size:13.333333969116211px">Dear Tyler,</span><div style="font-family:arial,sans-serif;font-size:13.333333969116211px"><br></div><div style="font-family:arial,sans-serif;font-size:13.333333969116211px">
Beamforming is one type of inverse method and it requires the sensor data covariance matrix. It also assumes that underlying sources are uncorrelated. If you try to input data from only 1 IC, all the sensors will covary together and any underlying sources that contribute to this 1 IC will be 100% temporally correlated. </div>
<div style="font-family:arial,sans-serif;font-size:13.333333969116211px"><br></div><div style="font-family:arial,sans-serif;font-size:13.333333969116211px">You can apply beamforming to a mixture of ICs, mixed back from the 'component space' back to the 'sensor space' (see ft_rejectcomponent). This is often done for example by excluding a few components which are artefactual and keeping many remaining (presumably brain) components. Normally the 'rank' of your covariance matrix is equal to the number of sensors (rank is the number of independent rows or columns, i.e. contributions), and so by removing a few ICs from the data, you have reduced the rank. If your rank is reduced relative to number of sensors, then you should use the regularisation option (cfg.lambda). This is true for either LCMV or DICS. However, this is usually only done when the rank is still well above 1.</div>
<div style="font-family:arial,sans-serif;font-size:13.333333969116211px"><br></div><div style="font-family:arial,sans-serif;font-size:13.333333969116211px">However, if you wish to localise the underlying sources of just 1 (or a small number of mixed) ICs, then better to use a min-norm method which does not depend on the sensor covariance. In this case, you still must re-project the IC back to sensor space in FieldTrip for the labels of the data to match the labels of the leadfield.</div>
<div style="font-family:arial,sans-serif;font-size:13.333333969116211px"><br></div><div style="font-family:arial,sans-serif;font-size:13.333333969116211px">See also: </div><div style="font-family:arial,sans-serif;font-size:13.333333969116211px">
<a href="http://fieldtrip.fcdonders.nl/example/use_independent_component_analysis_ica_to_remove_eog_artifacts" target="_blank">http://fieldtrip.fcdonders.nl/example/use_independent_component_analysis_ica_to_remove_eog_artifacts</a></div>
<div style="font-family:arial,sans-serif;font-size:13.333333969116211px"><a href="http://fieldtrip.fcdonders.nl/tutorial/minimumnormestimate" target="_blank">http://fieldtrip.fcdonders.nl/tutorial/minimumnormestimate</a><br>
</div><div style="font-family:arial,sans-serif;font-size:13.333333969116211px"><a href="http://fieldtrip.fcdonders.nl/tutorial/shared/virtual_sensors" target="_blank">http://fieldtrip.fcdonders.nl/tutorial/shared/virtual_sensors</a> (<- LCMV)<br>
</div><div style="font-family:arial,sans-serif;font-size:13.333333969116211px"><br></div><div style="font-family:arial,sans-serif;font-size:13.333333969116211px">Hope this helps; please ask again if you need further clarification, or please give a specific error message.</div>
<div style="font-family:arial,sans-serif;font-size:13.333333969116211px"><br></div><div style="font-family:arial,sans-serif;font-size:13.333333969116211px">Cheers,</div><div style="font-family:arial,sans-serif;font-size:13.333333969116211px">
Johanna</div><div><br></div><div class="gmail_extra"><br><br><div class="gmail_quote">2014-03-17 0:21 GMT+00:00 Tyler Grummett <span dir="ltr"><<a href="mailto:tyler.grummett@flinders.edu.au" target="_blank">tyler.grummett@flinders.edu.au</a>></span>:<br>
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<p>Hello fieldtrip,<br>
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<p>I am new to the fieltrip toolbox and Ive come from eeglab to do some beamformer and connectivity processing.<br>
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<p>Ive used the function eeglab2fieldtrip, with one of the arguments being 'componentanalysis'. I tried using the data in the beamformer tutorial and everything worked fine <br>
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<p>until I got to the ft_sourceanalysis script where it used the channel labels and positions from the original non-ICAed data. So the code crashed because the ICA labels<br>
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<p>and channels labels didnt match.<br>
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<p>I was wondering whether beamformer can be used with ICs from eeglab, and if so, where I am going wrong.<br>
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<p>We also want to do functional connectivity as I mentioned, which requires a beamformer that operates in the time domain as opposed to the frequency domain.<br>
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<p>However, there arent any tutorials on the <span style="line-height:22.5px;text-align:justify;font-size:15px;font-family:calibri,geneva,arial,verdana,sans-serif">
Constrained Minimum Variance (LCMV)</span> beamformer (which I think works in the time domain), so I am trying to make do with the one used in the tutorial.<br>
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<p>Any help will be greatly appreciated.<br>
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<p>Tyler.<br>
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<div><font face="Arial"><i>Tyler Grummett ( BBSc, BSc(Hons I))</i></font></div>
<div><font face="Arial"><i>PhD Candidate</i></font></div>
<div><font face="Arial"><i>Brain Signals Laboratory</i></font></div>
<div><font face="Arial"><i>Flinders University</i></font></div>
<div><font face="Arial"><i>Rm 5A301</i></font></div>
<div><font face="Arial"><i>Ext 66124</i></font></div>
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