[FieldTrip] regressconfound and frequency domain

[Raghavan Gopalakrishnan]

Arjen,
Thanks, I reduced down the time resolution so computation can go faster.
Now, my 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
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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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