[Eeglablist] eeglablist Digest, Vol 129, Issue 7

Fri Jul 10 15:56:12 PDT 2015

Can you provide more information what you are trying to do?

The usage is pretty clear from the documentation: the function creates a
structure from a set of key-value pairs you provide. For example:

>> cfg = keyval2cfg('dataset', 'mydataset.fif', 'continuous', 'yes')

cfg =

       dataset: 'mydataset.fif'
    continuous: 'yes'

Thus it is basically equivalent to running

>> cfg = [];
>> cfg.dataset = 'mydataset.fif';
>> cfg.continuous = 'yes'

cfg =

       dataset: 'mydataset.fif'
    continuous: 'yes'

If you have further questions about the Fieldtrip toolbox, it is best to
ask on the Fieldtrip mailing list rather than here.

Best,
Steve

Stephen Politzer-Ahles
New York University, Abu Dhabi
Neuroscience of Language Lab
http://www.nyu.edu/projects/politzer-ahles/

On Thu, Jul 9, 2015 at 3:26 PM, Rajyalakshmi Matta <
rajyalakshmiphd at gmail.com> wrote:

> Dear experts,
>
>     I am trying to analyze fieldtrip tool box. In keyval2cfg(varargin)
> what is the input to be supplied. Can anybody please let me know?
>
>
> Your help is more delighted
>
> regards
> M. Rajyalakshmi
>
>
> M. Rajya Lakshmi
> Ph D Scholar
> KL University
> Guntur.
>
>
>
> On Thu, Jul 9, 2015 at 12:30 AM, <eeglablist-request at sccn.ucsd.edu> wrote:
>
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>> Today's Topics:
>>
>>    1. Re: EEG AC and DC record problem (Makoto Miyakoshi)
>>    2. Re: fmrib plugin gradient correction question (Makoto Miyakoshi)
>>
>>
>> ---------- Forwarded message ----------
>> From: Makoto Miyakoshi <mmiyakoshi at ucsd.edu>
>> To: bjj2909102003 <bjj2909102003 at 163.com>
>> Cc: eeglablist <eeglablist at sccn.ucsd.edu>
>> Date: Tue, 7 Jul 2015 15:15:27 -0700
>> Subject: Re: [Eeglablist] EEG AC and DC record problem
>> Dear Booer,
>>
>> If data are recorded in what you call 'AC mode', you can't recover 'DC
>> mode' data. It's like this:
>>
>> DC mode data = AC mode data + infraslow signal + DC difference
>>
>> I believe when you record data with 'AC mode' the recorder should use
>> high-pass filter that removes infraslow signal and DC.
>>
>> Makoto
>>
>> On Mon, Jul 6, 2015 at 4:18 AM, bjj2909102003 <bjj2909102003 at 163.com>
>> wrote:
>>
>>> Hi, everyone.
>>> Recently, I recorded my EEG data in AC mode. And I made the model based
>>> on AC mode data (range centred around zero). Now I want to analyze
>>> on-line EEG data by my previous model. But the online data is acquired in
>>> DC mode (value is big and not centred around zero) by access SDK. I
>>> don't know how to convert DC mode data into AC mode data so that I can
>>> apply the previous model into the data?
>>>
>>> Thank you.
>>> Booer
>>>
>>> ------------------------------
>>> bjj2909102003
>>>
>>> _______________________________________________
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>>
>>
>>
>> --
>> Makoto Miyakoshi
>> Swartz Center for Computational Neuroscience
>> Institute for Neural Computation, University of California San Diego
>>
>>
>> ---------- Forwarded message ----------
>> From: Makoto Miyakoshi <mmiyakoshi at ucsd.edu>
>> To: basile pinsard <basile.pinsard at gmail.com>
>> Cc: EEGLAB List <eeglablist at sccn.ucsd.edu>
>> Date: Tue, 7 Jul 2015 15:31:11 -0700
>> Subject: Re: [Eeglablist] fmrib plugin gradient correction question
>> Dear Basile,
>>
>> > - having volume markers, is there any interest in switching to slice
>> markers (that I could add myself, the scanner being regular)? Does the
>> gradient template is then computed on slices and windowed on let say 30
>> slices instead of 30 volumes? Or does this slice splitting is already
>> performed in the software?
>>
>> It depends on whether you have identical artifact timecourse for every
>> slice or not. I believe it is not the case. At least in my case, the last
>> slice had visibly different artifact pattern from other slices... probably
>> the first slice is different too. I believe using volume template (i.e.
>> 30-slice long template).
>>
>> > - I removed the 4 ECG channels recorded from the residual artifact
>> correction as suggested, however, there remains high-frequency content in
>> the signal (see screenshot
>> <http://s3.postimg.org/uyzdb2zgj/gradient_correction.png> with 1 ECG
>> channel and EEG channels) only for these channels. Is that because the
>> correction didn't work that well for these channels ? Is that something
>> that I can fix? Does low-pass filter is compulsory in this step? Why EEG
>> channels are not affected?
>>
>> First of all, the primary purpose of having ECG (or EKG if you like a
>> German flavor) is to build artifact template, and what you need is just
>> reliable peak detection. Your data, after cleaning, seems ok for this
>> criterion. Low-pass filtering the ECG/EKG after cleaning is fine (I think
>> fMRIb apply 20Hz low-pass or something when detecting ECG/EKG peaks...sorry
>> if I'm wrong.) The reason why EEG channels are fine is probably that
>> ECG/EKG channels have longer wires and they are less physically stable than
>> those of EEG channels.
>>
>> Makoto
>>
>> On Sat, Jul 4, 2015 at 12:35 PM, basile pinsard <basile.pinsard at gmail.com
>> > wrote:
>>
>>> Hi EEGlab users,
>>>
>>> I am quite new to EEGlab and trying to use the fMRIb plugin to correct
>>> from MRI artifacts.
>>> My questions are:
>>>
>>> - having volume markers, is there any interest in switching to slice
>>> markers (that I could add myself, the scanner being regular)? Does the
>>> gradient template is then computed on slices and windowed on let say 30
>>> slices instead of 30 volumes? Or does this slice splitting is already
>>> performed in the software?
>>>
>>> - I removed the 4 ECG channels recorded from the residual artifact
>>> correction as suggested, however, there remains high-frequency content in
>>> the signal (see screenshot
>>> <http://s3.postimg.org/uyzdb2zgj/gradient_correction.png> with 1 ECG
>>> channel and EEG channels) only for these channels. Is that because the
>>> correction didn't work that well for these channels ? Is that something
>>> that I can fix? Does low-pass filter is compulsory in this step? Why EEG
>>> channels are not affected?
>>>
>>> Thank you very much for your help and ideas on this.
>>>
>>> basile
>>>
>>>
>>> _______________________________________________
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>>>
>>
>>
>>
>> --
>> Makoto Miyakoshi
>> Swartz Center for Computational Neuroscience
>> Institute for Neural Computation, University of California San Diego
>>
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>
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