[FieldTrip] regressconfound and frequency domain

[Raghavan Gopalakrishnan]

Thanks Arjen,
Should I use ft_freqdescriptives to compute t descriptives for individual subjects, and then take that to group level instead of mean? If not, what are the other alternatives?
Thanks,
Raghavan

Hi Raghavan, ft_regressconfound run on timelock data seems to return output with avg field. However, ft_regressconfound run on frequency data, does not return average. I see the avg field being removed. Is there a reason? >> Not intentionally, but not an issue either. You could still use ft_freqdescriptives to compute the average for you, but see my comment below. Question - Since ft_regressconfound outputs power spectrum of individual trials - 4D matrix (instead of average), can I simply re-average the power spectrum over trials to see the average power for that subject. Also, I need to run grand average (over subjects) before running statistics. I hope these steps does not distort the data. Please advise. >> Remember that the mean over trials is not affected by your clean-up of trial-by-trial variance due to head movement. Taking each subject's mean (unaffected) to the group level is an approach that will not benefit from your clean-up. In order to benefit from reduced trial-by-trial variance, you'll need a measure that depends on it, e.g. t-descriptive, neural activity-behavior correlation (for taking to the group level). Hope this helps, Arjen ----- Oorspronkelijk bericht -----
> Van: "Raghavan Gopalakrishnan" <gopalar.ccf at gmail.com>
> Aan: fieldtrip at science.ru.nl
> Verzonden: Donderdag 20 februari 2014 22:12:28
> Onderwerp: Re: [FieldTrip] regressconfound and frequency domain
> Arjen,
> Thanks, I reduced down the time resolution so computation can go
> faster. Now, m y matrix looks like this
> hpicomptimefreq =
> label: {204x1 cell}
> dimord: 'rpt_chan_freq_time'
> freq: [1x56 double]
> time: [1x375 double]
> powspctrm: [4-D double]
> cumtapcnt: [59x56 double]
> cfg: [1x1 struct]
> trialinfo: [59x1 double]
> beta: [4-D double]
> ft_regressconfound run on timelock data seems to return output with
> avg field. However, ft_regressconfound run on frequency data, does not
> return average. I see the avg field being removed. Is there a reason?
> Question - Since ft_regressconfound outputs power spectrum of
> individual trials - 4D matrix (instead of average), can I simply
> re-average the power spectrum over trials to see the average power for
> that subject. Also, I need to run grand average (over subjects) before
> running statistics. I hope these steps does not distort the data.
> Please advise.
> Thanks,
> Raghavan
> Date: Wed, 19 Feb 2014 22:58:38 +0100 (CET)
> From: "Stolk, A. (Arjen)" < a.stolk at fcdonders.ru.nl >
> To: FieldTrip discussion list < fieldtrip at science.ru.nl >
> Subject: Re: [FieldTrip] regressconfound and frequency domain
> Message-ID:
> < 2108167665.5423215.1392847118322.JavaMail.root at sculptor.zimbra.ru.nl
> >
> Content-Type: text/plain; charset="utf-8"
> Dear Raghavan, Good to hear it's working out for you. A short answer
> would be 'no'. Reducing the size of your data matrix is likely going
> to speed up computations. Your time resolution seems pretty high (1500
> frequency estimations per single trial); do you need that many? Yours,
> Arjen ----- Oorspronkelijk bericht -----
> > Van: "Raghavan Gopalakrishnan" < gopalar.ccf at gmail.com >
> > Aan: fieldtrip at science.ru.nl
> > Verzonden: Woensdag 19 februari 2014 22:01:00
> > Onderwerp: [FieldTrip] regressconfound and frequency domain
> > Arjen,
> > Thanks for answering all my previous questions. I was successfully
> > able to incorporate head movements to my erf data. As I understand I
> > have to do this separately for the time frequency data after keeping
> > individual trials. I am interested in both beta and gamma bands
> > [15:1:70]. my time frequency looks like this using wavelets,
> > timefreq =
> > label: {204x1 cell}
> > dimord: 'rpt_chan_freq_time'
> > freq: [1x56 double]
> > time: [1x1500 double]
> > powspctrm: [4-D double]
> > cumtapcnt: [55x56 double]
> > grad: [1x1 struct]
> > elec: [1x1 struct]
> > cfg: [1x1 struct]
> > trialinfo: [55x1 double]
> > After regressconfound
> > hpicomptimefreq =
> > label: {204x1 cell}
> > dimord: 'rpt_chan_freq_time'
> > freq: [1x56 double]
> > time: [1x1500 double]
> > powspctrm: [4-D double]
> > cumtapcnt: [55x56 double]
> > cfg: [1x1 struct]
> > trialinfo: [55x1 double]
> > beta: [4-D double]
> > Regressconfound took about 1 hr and 30 mins, since its a huge matrix
> > [55x204x56x1500]. I have 25 such blocks of data for 20 subjects. It
> > will take an enoumous amount of time to process the data through
> > regressconfound. Is there a workaround to make the processing faster
> > or am I missing something. Any help would be of great help.
> > Thanks,
> > Raghavan

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