[FieldTrip] Appending data.grad, after using D.ftraw(0)
smoratti at psi.ucm.es
smoratti at psi.ucm.es
Mon Sep 10 15:55:26 CEST 2012
Dear George,
Your approach was right, and it would have worked with older versions of field trip. Now in field trip the grad field contents have changed a little bit.
Now it contains
balance: [1x1 struct]
chanori: [306x3 double]
chanpos: [306x3 double]
chantype: {306x1 cell}
chanunit: {306x1 cell}
coilori: [510x3 double]
coilpos: [510x3 double]
label: {306x1 cell}
tra: [306x510 double]
unit: 'cm'
so the field put would be chanpos, ori would be chanori (or coilpos/ coilori depending on your MEG system).
So check this out and you can easily adapt the structure. I think SPM does not go with the últimate field trip version.
I hope this helps,
best,
Stephan
________________________________________________________
Stephan Moratti, PhD
see also: http://web.me.com/smoratti/
Universidad Complutense de Madrid
Facultad de Psicología
Departamento de Psicología Básica I
Campus de Somosaguas
28223 Pozuelo de Alarcón (Madrid)
Spain
and
Center for Biomedical Technology
Laboratory for Cognitive and Computational Neuroscience
Parque Científico y Tecnológico de la Universidad Politecnica de Madrid
Campus Montegancedo
28223 Pozuelo de Alarcón (Madrid)
Spain
email: smoratti at psi.ucm.es
Tel.: +34 679219982
El 10/09/2012, a las 14:22, George Wallis escribió:
> Dear fieldtrippers,
>
> if anyone can help me with this I'd be very grateful!
>
> I have a bunch of preprocessed and epoched data files in SPM format, and I'm converting them to fieldtrip format using the following:
>
> D = spm_eeg_load(fn);
> data = D.ftraw(0);
>
> ...which results in...
>
> data =
>
> fsample: 250
> label: {350x1 cell}
> trial: {1x360 cell}
> time: {1x360 cell}
>
> Comparing this to data structures generated using fieldtrip alone, field like data.grad are missing.
>
> I can create something like a grad field by doing:
>
> D.sensors('MEG')
>
> ans =
>
> pnt: [510x3 double]
> ori: [510x3 double]
> tra: [306x510 double]
> unit: 'mm'
> label: {306x1 cell}
>
> If I then append this to the data:
>
> data.grad = D.sensors('MEG');
>
> I get an error:
>
> ---
>
> Error using ft_chantype (line 56)
> the input that was provided to this function cannot be deciphered
>
> Error in ft_datatype_sens (line 128)
> sens.chantype = ft_chantype(sens);
>
> Error in ft_datatype_raw (line 99)
> data.grad = ft_datatype_sens(data.grad);
>
> Error in ft_checkdata (line 177)
> data = ft_datatype_raw(data, 'hassampleinfo', hassampleinfo);
>
> Error in ft_timelockanalysis (line 134)
> data = ft_checkdata(data, 'datatype', {'raw', 'comp'}, 'feedback', 'yes', 'hassampleinfo',
> 'yes');
>
> Error in ft_SENS_ERF_grRep (line 82)
> avdat = ft_timelockanalysis(cfg,raw);
>
> ---
>
> ...when I try to do timelockanalysis on this data. If I leave the grad field off (i.e. not append it to the output of ftraw(0)) there's no problem, and the analysis runs fine. However, because I want to do spatial neighbour cluster analysis later, I think I need the .grad field, so I might as well get it incorporated at this stage.
>
> Going a bit further into the error messages above, the function ft_datatype_sens appears to convert the old format .ori, .pnt into the newer .coilori, .coilpos ... etc. But the next function in the chain, ft_chantype, looks for .pnt and .ori when doing:
>
> isgrad = isa(input, 'struct') && isfield(input, 'pnt') && isfield(input, 'ori');
>
> I'm wondering if this could be the problem?
>
> Sorry for the long message! I'd be very grateful if anyone can help out.
>
> Best wishes,
> George
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