[FieldTrip] regressconfound and frequency domain

[Stolk, A. (Arjen)]

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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-- Donders Institute for Brain, Cognition and Behaviour Centre for Cognitive Neuroimaging Radboud University Nijmegen Email: a.stolk at donders.ru.nl Phone: +31(0)243 68294 Web: www.arjenstolk.nl 
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