[Eeglablist] Gratton ocular correction issue

[Kyle Elliott Mathewson]

Hi All,
Just thought I would chime in a little on the Gratton, Coles, &
Donchin Eye Movement Correction Procedure (EMCP).

References:
Gratton, G., Coles, M. G. H., & Donchin, E. (1983). A new method for
off-line removal of ocular artifact. Electroencephalography and
Clinical Neurophysiology, 55, 468-484.
Miller, G. A., Gratton, G., & Yee, C. M. (1988). Generalized
implementation of an eye movement correction procedure.
Psychophysiology, 25, 241-243.

This method is not designed for continuous data, in theory a
regression technique is very much applicable to continuous data but a
specific feature of the EMCP procedure precludes this use.



The EMCP procedure first subtracts the stimulus locked average from
all individual trials in order to remove any stimulus locked brain
activity from influencing the regression coefficients:

	-other regression techniques asked them to blink, estimate
propagation factors from this
	-but these initiated blinks differ from unintentional blinks (Records, 1979)
-blinks and eye movements have different factors, consider separately
(Verleger, 1982 doesn’t)
	-estimated after ERP average for EEG and EOG is subtracted from data
		-because if something happened locked to the event at both EOG and O1
		-the regression factor would be spuriously high!!!
-separately for horizontal and vertical EOG
-Average EEG at each channel and EOG
-subtract average from each trial to get non-event related
-Test by comparing the deviation from the true ERP (no blink trials)
-and by reduction in variance of trial s and time points
-factors are stable across sessions and subjects, saccades larger than blinks
-blink assumed to have occurred when ever at time point t, the
integral of V t-10 to Vt+10 exceeds some criteria.

ICA seems like the best option for continuous data as it will separate
this stimulus locked brain data as well,
Kyle Mathewson, PhD
Cognitive Neuroimaging Lab
University of Illinois




On Wed, Oct 17, 2012 at 1:22 PM,  <eeglablist-request at sccn.ucsd.edu> wrote:
> ---------- Forwarded message ----------
> From: Scott Makeig <smakeig at gmail.com>
> To: Ricardo Moura <ricardoojm at gmail.com>
> Cc: eeglablist at sccn.ucsd.edu, Thang Le <Thang.Le at park.edu>
> Date: Tue, 16 Oct 2012 15:09:07 -0700
> Subject: Re: [Eeglablist] Gratton ocular correction issue
> We routinely and quite successfully use ICA decomposition for this. Both CORRMAP and a new function by Nima Bigdely-Shamlo are quite good, we believe, at indicating which ICs (independent components) account for eye movements...
>
> Scott Makeig
>
> On Tue, Oct 16, 2012 at 10:09 AM, Ricardo Moura <ricardoojm at gmail.com> wrote:
>>
>> By the way, does anyone know a plugin for ocular correction which can be aplyed to continuous data?
>> I've checked and this plugin reported in the first email only works for segmented data.
>>
>> Best,
>> Ricardo
>>
>>
>> On 11 October 2012 22:36, Arnaud Delorme <arno at ucsd.edu> wrote:
>>>
>>> Dear Le,
>>>
>>> the correction method you mention is an EEGLAB plugin so you might want to contact the authors of the plugin directly.
>>> The problem might be that the plugin only works on continuous data not on segmented data.
>>> Hope this helps,
>>>
>>> Arno
>>>
>>> On 2 Oct 2012, at 08:17, Thang Le wrote:
>>>
>>> Hi everyone,
>>>
>>> I am running into a problem with the Gratton's ocular correction method in EEGLAB and I was wondering whether you could help me troubleshoot.
>>>
>>> I have been attempting to apply the Gratton's ocular correction method to a 64-channel data set. The two EOG channels are 65 and 66. So far I have downsampled the data to 256Hz, rereferenced and segmented. The values I used for Gratton's were:
>>>
>>> Number of EOG channel for regression: [65 66]
>>> Channels: [1 64]
>>> Window for Blink-detection: 24
>>> Voltage for Blink-detection: 200
>>>
>>> However, EEGLAB would give me an error message that says "To RESHAPE the number of elements must not change". I wasn't sure what to use for blink window but it does not look like 24 is the right value.
>>>
>>> Thank you so much for your help.
>>>
>>> Le
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>>
>>
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>
>
>
> --
> Scott Makeig, Research Scientist and Director, Swartz Center for Computational Neuroscience, Institute for Neural Computation, University of California San Diego, La Jolla CA 92093-0559, http://sccn.ucsd.edu/~scott
>