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Hi Benjy,
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<div class="">I would recommend against using the ‘rawtrial’ option. This in retrospect is a poorly implemented functionality in FieldTrip and does not work well (if at all) in concrete situations.</div>
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<div class="">I would recommend to use ft_virtualchannel to obtain your single trial time courses. I think that this has been discussed at other threads in this discussion forum, so it should be possible to find some information about this.</div>
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<div class="">Also, if your intention is to be able to classify zero from notzero trials, you will probably fool yourself if you NOT compute the covariance across all trials combined, and do a single call to ft_sourceanalysis to obtain a set of common spatial
filters. Currently, your pasted code computes the covariance and spatial filters per condition, which will already serve as a booster to make the 2 conditions more dissimilar.</div>
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<div class="">Good luck,</div>
<div class="">Jan-Mathijs</div>
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<blockquote type="cite" class="">
<div class="">On 25 Apr 2023, at 14:32, Barnett, Benjy via fieldtrip <<a href="mailto:fieldtrip@science.ru.nl" class="">fieldtrip@science.ru.nl</a>> wrote:</div>
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<div class=""><span style="caret-color: rgb(0, 0, 0); font-family: Helvetica; font-size: 14px; font-style: normal; font-variant-caps: normal; font-weight: normal; letter-spacing: normal; text-align: start; text-indent: 0px; text-transform: none; white-space: normal; word-spacing: 0px; -webkit-text-stroke-width: 0px; text-decoration: none; float: none; display: inline !important;" class="">Sorry
for multiple posting, please ignore - I have just noticed the cfg.rawtrials option in ft_sourceanalysis, which makes this much simpler.</span>
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Thank you<br class="">
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<blockquote type="cite" class="">
<div class="">On Apr 25, 2023, at 1:12 PM, Barnett, Benjy via fieldtrip <<a href="mailto:fieldtrip@science.ru.nl" class="">fieldtrip@science.ru.nl</a>> wrote:</div>
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⚠ Caution: External sender</p>
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<div class="">Hi all,
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<div class="">I’m trying to implement some MVPA decoding analyses in source space using MEG data collected on a CTF system. I have gone through different tutorials to perform the source reconstruction (namely <a href="https://urldefense.com/v3/__https://www.fieldtriptoolbox.org/workshop/aarhus/beamformingerf/__;!!HJOPV4FYYWzcc1jazlU!6dJY9xn9eTEqqGoXULa_OMBzAvS3Cl7jDWxpxhESR-a61x1hEJWt38h4sKsVfLMoG9DIkPTj6qamNi18fKgf9dtYDIaRLfHvTw$" originalsrc="https://urldefense.com/v3/__https://www.fieldtriptoolbox.org/workshop/aarhus/beamformingerf/__;!!HJOPV4FYYWzcc1jazlU!6dJY9xn9eTEqqGoXULa_OMBzAvS3Cl7jDWxpxhESR-a61x1hEJWt38h4sKsVfLMoG9DIkPTj6qamNi18fKgf9dtYDIaRLfHvTw$" shash="MWnbUqcyEpfb4w24mz60KcGzika8gzLbc572WrUlUGpIWZIftBXjI8vHfh/UT9VUKNDC1/BJwEkbZt/ASHJjmRFeuYXv++UF7K+nQTHDa0tVk3hGWukxV318Z2ZnmgH4nVWj2yrrT9GMTNi1RQI+AHp0OaXYo7uo53PW1pOjKfY=" class="">https://www.fieldtriptoolbox.org/workshop/aarhus/beamformingerf/</a>; <a href="https://urldefense.com/v3/__https://www.fieldtriptoolbox.org/workshop/paris2019/handson_sourceanalysis/__;!!HJOPV4FYYWzcc1jazlU!6dJY9xn9eTEqqGoXULa_OMBzAvS3Cl7jDWxpxhESR-a61x1hEJWt38h4sKsVfLMoG9DIkPTj6qamNi18fKgf9dtYDIZKqhJFug$" originalsrc="https://urldefense.com/v3/__https://www.fieldtriptoolbox.org/workshop/paris2019/handson_sourceanalysis/__;!!HJOPV4FYYWzcc1jazlU!6dJY9xn9eTEqqGoXULa_OMBzAvS3Cl7jDWxpxhESR-a61x1hEJWt38h4sKsVfLMoG9DIkPTj6qamNi18fKgf9dtYDIZKqhJFug$" shash="mmBfn/R2kyCqGi6D0mQIdxSAF1JpioWHFVb7s9XAqReNNLndhwQVB4yg983D962qgtYjPAoKHVPfPllVM6x1h76XLWXHkeI6YS10bRGq0TcDjy6jIG5NHu1ettLEXXCw44+37Ec3338hxQ/Xn5aPbTHSP8SmglVFzedmf1aMTiE=" class="">https://www.fieldtriptoolbox.org/workshop/paris2019/handson_sourceanalysis/</a>).</div>
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<div class="">To train my decoder, I will obviously need trial by trial activity estimates for each of the virtual channels. The issue is that these tutorials, and most other information I can find, only go into how to extract condition-level source estimates.
I have an idea of how to do this for individual trials, but it’s such a shift from the tutorials I wondered if I could check whether my plan will work conceptually and technically. </div>
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<div class="">My current code (to obtain condition level estimates) is as follows:</div>
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% Compute Covariance Matrix</div>
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cfg = [];</div>
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cfg.covariance =<span class="Apple-converted-space"> </span><span class="" style="color: rgb(170, 4, 249);">'yes'</span>;</div>
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avg = ft_timelockanalysis(cfg,meg_data); %meg_data = datazero + datanotzero;</div>
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cfg.keeptrials =<span class="Apple-converted-space"> </span><span class="" style="color: rgb(170, 4, 249);">'yes'</span>;</div>
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avgzero = ft_timelockanalysis(cfg,datazero); %condition 1 = ‘zero'</div>
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avgnotzero = ft_timelockanalysis(cfg,datanotzero); %condition 2 = ’not zero'</div>
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<span class=""> <span class="Apple-converted-space"> </span></span>%Calculate spatial filter for each voxel over all data</div>
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cfg = [];</div>
<div class="" style="margin: 0px; font-stretch: normal; font-size: 11px; line-height: normal; font-family: Courier;">
cfg.method =<span class="Apple-converted-space"> </span><span class="" style="color: rgb(170, 4, 249);">'lcmv'</span>;</div>
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cfg.sourcemodel = sourcemodel_new;</div>
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cfg.headmodel = headmodel_new;</div>
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cfg.lcmv.keepfilter =<span class="Apple-converted-space"> </span><span class="" style="color: rgb(170, 4, 249);">'yes'</span>;</div>
<div class="" style="margin: 0px; font-stretch: normal; font-size: 11px; line-height: normal; font-family: Courier;">
cfg.lcmv.lambda =<span class="Apple-converted-space"> </span><span class="" style="color: rgb(170, 4, 249);">'5%'</span>;</div>
<div class="" style="margin: 0px; font-stretch: normal; font-size: 11px; line-height: normal; font-family: Courier;">
cfg.lcmv.weightnorm =<span class="Apple-converted-space"> </span><span class="" style="color: rgb(170, 4, 249);">'unitnoisegain'</span>;</div>
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cfg.lcmv.fixedori =<span class="Apple-converted-space"> </span><span class="" style="color: rgb(170, 4, 249);">'yes'</span>;</div>
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cfg.channel = {<span class="" style="color: rgb(170, 4, 249);">'MEG'</span>};</div>
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cfg.senstype =<span class="Apple-converted-space"> </span><span class="" style="color: rgb(170, 4, 249);">'MEG'</span>;</div>
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sourceavg = ft_sourceanalysis(cfg, avg); %compute filters over all trials</div>
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<span class=""> <span class="Apple-converted-space"> </span></span>%Now apply this filter to zero and not-zero data separately</div>
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cfg = [];</div>
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cfg.method =<span class="Apple-converted-space"> </span><span class="" style="color: rgb(170, 4, 249);">'lcmv'</span>;</div>
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cfg.sourcemodel = sourcemodel_new;</div>
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cfg.sourcemodel.filter = sourceavg.avg.filter;</div>
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cfg.headmodel = headmodel_new;</div>
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sourcezero = ft_sourceanalysis(cfg, avgzero);</div>
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sourcenotzero = ft_sourceanalysis(cfg, avgnotzero);</div>
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<div class="">Would I be correct to simply alter this pipeline so that I perform the call to ft_timelockanalysis and estimate the covariance matrix for each trial individually, rather than each condition? Then I was planning to simply run the ft_sourceanalysis
function on each of these trials individually, keeping track of which trial belongs to which condition for later use in decoding. I can then go into the source output from the sourceanalysis and extract the .mom field to get the virtual channel data per trial,
which I plan to then enter into the decoder.</div>
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<div class="">Does this make sense conceptually? It seems a logical step from the beamforming of condition level signals, but I’ve come to learn it’s rarely that simple!</div>
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<div class="">I hope that all makes sense,</div>
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<div class="">Benjy</div>
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