[Eeglablist] Mu-rythm & Alpha Activity
Kyle Elliott Mathewson
kylemath at gmail.com
Wed Oct 24 20:42:48 PDT 2012
Hello Habib,
I believe the answer to your question is that imaging a particular
movement will in general activate the same brain areas as actually
performing that movement. So a right hand movement will lead increase
in 8-13 Hz oscillations over the left motor cortex (roughly C3
electrode in the 10/10 system). Imagining a right hand movement will
do the same, although probably less.
This Mu rhythm can be functionally distinguished from other 8-13 Hz
rhythms in the brain. When the eyes are closed, or focus is taken off
of external stimuli, a large increase in 8-13 Hz oscillations are
observed over the back of the head. These are usually maximal around
Poz or Pz.
Also, topographic changes in this posterior alpha rhythm can be
observed based on the focus of spatial visual attention. Attending to
the left side of space with lead to a decrease in the 8-13 Hz power
over the right visual areas, and usually an increase in the power of
these oscillations over left visual areas.
Kyle Mathewson, PhD
University of Illinois
---------- Forwarded message ----------
From: Habib Paracha <ra_lums at hotmail.co.uk>
To: EEGLAB Lisst <eeglablist at sccn.ucsd.edu>
Cc:
Date: Wed, 24 Oct 2012 12:21:42 +0500
Subject: [Eeglablist] Mu-rythm & Alpha Activity
Hi,
I want to distinguish the Mu-rythms with the alpha activity of the
brain. The question that I have in mind is that while detecting
Mu-rhytms does the alpha activity of the whole brain changes when we
imagine a particular movement or ii changes on just certain specific
locations.
Another Question is When I actually move my hands together with
imagining the movement at which particular location(Electrode
positions) will I be able to detect this.
Regards,
Habib
On Wed, Oct 24, 2012 at 12:47 PM, <eeglablist-request at sccn.ucsd.edu> wrote:
> Send eeglablist mailing list submissions to
> eeglablist at sccn.ucsd.edu
>
> To subscribe or unsubscribe via the World Wide Web, visit
> http://sccn.ucsd.edu/mailman/listinfo/eeglablist
> or, via email, send a message with subject or body 'help' to
> eeglablist-request at sccn.ucsd.edu
>
> You can reach the person managing the list at
> eeglablist-owner at sccn.ucsd.edu
>
> When replying, please edit your Subject line so it is more specific
> than "Re: Contents of eeglablist digest..."
>
> Today's Topics:
>
> 1. Re: Grand Average (Alexander J. Shackman)
> 2. ERSP on components in STUDY (Pieranna Arrighi)
> 3. A question about inverse calculation (mengtongxiao at sina.com)
> 4. EEGLAB, ARMA & VAR Models (Yaseen Gerhold)
> 5. Mu-rythm & Alpha Activity (Habib Paracha)
> 6. Re: Baseline removal in dead channels (Gorka Fraga Gonzalez)
>
>
> ---------- Forwarded message ----------
> From: "Alexander J. Shackman" <shackman at wisc.edu>
> To: Steve Luck <sjluck at ucdavis.edu>
> Cc: eeglablist at sccn.ucsd.edu, vilanova5 at hotmail.com
> Date: Tue, 23 Oct 2012 15:39:48 -0500
> Subject: Re: [Eeglablist] Grand Average
> I would agree with Steve and Steve with this.
>
> However, I wanted to underscore that "Giving some subjects more weight than others" has a legitimate place. Robust regression, which is increasingly used in neuroimaging, effectively weights subjects based on the residuals as a means of down-weighting outlying cases.
>
> See e.g., http://wagerlab.colorado.edu/files/papers/Wager_2005_Neuroimage_2.pdf
>
> Take care,
> Alex
>
>
> On Tue, Oct 23, 2012 at 1:24 PM, Steve Luck <sjluck at ucdavis.edu> wrote:
>>
>> I agree with Steve Politzer-Ahles about this. Giving some subjects more weight than others could lead to bizarre results, whereas differences in number of trials (and hence differences in measurement error) are likely to simply reduce statistical power (and only modestly in typical situations).
>>
>> More generally, the idea of using a sample of subjects to estimate the parameters of a larger population would be greatly distorted by giving some subjects greater weight than others.
>>
>> Steve Luck
>>
>> From: Stephen Politzer-Ahles <politzerahless at gmail.com>
>> Subject: Re: [Eeglablist] Grand Average
>> Date: October 22, 2012 5:18:30 PM PDT
>> To: Alberto Gonzalez V <vilanova5 at hotmail.com>
>> Cc: <eeglablist at sccn.ucsd.edu>
>>
>>
>> Hello Alberto,
>>
>> There may be discussion of this issue in Luck (2005) and/or Handy (2004); if there is, you can ignore what I say and check those instead.
>>
>> My assumption, though, is that the reason we typically average them the way we do, instead of using a weighted average, is that more epochs does not necessarily mean better data. It's true that an insufficient number of epochs (and/or subjects) will make the ERP noisy. But once you reach a certain point, adding more epochs does not make the data a lot better (see Luck's (2005) discussion of the signal-to-noise ratio). Each subject is meant to be one datapoint, so once a given subject reaches the threshhold after which she has "enough" trials to make a good ERP, then it's fair to make that subject a datapoint.
>>
>> Also, of course, the characteristics of the ERP components you are interested in are likely to differ across subjects; some people may have a bigger P300 or N400 or whatnot overall. There is not necessarily a straightforward relationship between this and how clean their data are (i.e., it's not necessarily the case that someone who has a bigger/smaller P300 also happens to blink more/less during the experiment). Thus, by weighting subjects differently because of how many clean epochs they happened to have, you may be inadvertently biasing your grand averages towards certain individuals. At least when you treat all subjects equally, you are neutral as far as that is concerned.
>>
>> Those are just my impressions; I don't know if there is published literature discussing this topic, and if there is then it of course is a better reference than my impressions!
>>
>> Best,
>> Steve
>>
>> On Mon, Oct 22, 2012 at 7:51 AM, Alberto Gonzalez V <vilanova5 at hotmail.com> wrote:
>>>
>>> Hi to all,
>>>
>>> I have a question about ERP methodology. Consider that we record the EEG during and task in 3 subjects, then we do the averages ( considering that the task has 60 epochs):
>>> Subject 1 did a perfect task, so we did the average with 60 epochs.
>>> Subject 2 had some problems during the recording, and the average was done with 40 epochs.
>>> Subject 3 had only 20 epochs, but we think that it´s enough and did the average.
>>>
>>> So the Subj 1 has all the epochs =1, Subj 2 has = 2/3 of the epochs, and Subj 3 has only =1/3. But in the grand averages we treat them as they had all the epochs (=1). Isn't better to give each subject a proportional value (considering it's number of epochs) in the grand average(something like: ([Subj1*1]+[Subj2*2/3]+[Subj3*1/3])/2)?.
>>>
>>> Thanks for your time!!!
>>
>>
>>
>>
>>
>>
>>
>>
>>
>> _______________________________________________
>> Eeglablist page: http://sccn.ucsd.edu/eeglab/eeglabmail.html
>> To unsubscribe, send an empty email to eeglablist-unsubscribe at sccn.ucsd.edu
>> For digest mode, send an email with the subject "set digest mime" to eeglablist-request at sccn.ucsd.edu
>>
>
>
>
> --
> Alexander J. Shackman, Ph.D.
> HealthEmotions Research Institute | Lane Neuroimaging Laboratory
> Wisconsin Psychiatric Institute & Clinics
> University of Wisconsin-Madison
> 6001 Research Park Boulevard
> Madison, Wisconsin 53719
>
> Telephone: +1 (608) 358-5025
> Fax: +1 (608) 265-2875
> Email: shackman at wisc.edu
> http://psyphz.psych.wisc.edu/~shackman
>
>
> ---------- Forwarded message ----------
> From: "Pieranna Arrighi" <parrighi at dfb.unipi.it>
> To: eeglablist at sccn.ucsd.edu
> Cc:
> Date: Tue, 23 Oct 2012 22:49:57 +0200 (CEST)
> Subject: [Eeglablist] ERSP on components in STUDY
> Dear Arno,
>
> I referred to the following bug:
>
> in the 10_2_4_4a version, std_ersp.m, fix important bug for computing ERSP
> of components in STUDY (SVN 9355 - Arno).
>
> Indeed, I have loaded the above reported version as well as other
> following versions (included the latest, 2012) and I found that all seem
> OK (ersps on components are as expected on the basis of hypothesis,
> channel results, etc), while versions before the 10_2_4_4a give really
> different results.
>
> best regards
> Pieranna
>
>
>
>
>
>
>
>
>
>
>
> ---------- Forwarded message ----------
> From: <mengtongxiao at sina.com>
> To: "eeglablist" <eeglablist at sccn.ucsd.edu>
> Cc:
> Date: Wed, 24 Oct 2012 11:14:25 +0800
> Subject: [Eeglablist] A question about inverse calculation
> Dear all,
>
> i want use eeg project to Cortical in order to get the data obout cortical .
> what shoude i do ?
>
> Thanks a lot !
>
> tongxiao
>
>
>
> ---------- Forwarded message ----------
> From: Yaseen Gerhold <sunshineafterdusk at gmail.com>
> To: eeglablist at sccn.ucsd.edu
> Cc:
> Date: Wed, 24 Oct 2012 08:36:09 +0200
> Subject: [Eeglablist] EEGLAB, ARMA & VAR Models
> Hi
>
> Can anyone recommend a good resource that can interface with EEGLAB to assist in the processing of EEG time-series data. In particular, I wanted to know if there are any resources related to multivariate time-series analysis: autoregressive moving average models and vector autoregression i.e., tutorials and toolboxes that researchers are using and are finding effective an efficient. Is there any framework/good tutorials that deal with this in the context of EEG? Any suggestions?
>
> Yaseen
>
>
> ---------- Forwarded message ----------
> From: Habib Paracha <ra_lums at hotmail.co.uk>
> To: EEGLAB Lisst <eeglablist at sccn.ucsd.edu>
> Cc:
> Date: Wed, 24 Oct 2012 12:21:42 +0500
> Subject: [Eeglablist] Mu-rythm & Alpha Activity
> Hi,
>
> I want to distinguish the Mu-rythms with the alpha activity of the brain. The question that I have in mind is that while detecting Mu-rhytms does the alpha activity of the whole brain changes when we imagine a particular movement or ii changes on just certain specific locations.
>
> Another Question is When I actually move my hands together with imagining the movement at which particular location(Electrode positions) will I be able to detect this.
>
> Regards,
>
> Habib
>
>
> ---------- Forwarded message ----------
> From: Gorka Fraga Gonzalez <gorkafraga at gmail.com>
> To: Stephen Politzer-Ahles <politzerahless at gmail.com>, eeglablist at sccn.ucsd.edu
> Cc:
> Date: Wed, 24 Oct 2012 11:03:05 +0200
> Subject: Re: [Eeglablist] Baseline removal in dead channels
> Hi Steve,
>
> Thanks for your replay.
>
> I meant the average in that dead channel. When scrolling data before baseline correction I was not able to display the signal in that channel unless using a very large scale (above 2000) when of course all lines were flat . However after baseline correction it displayed signal similar to the rest of the channels. This channel also showed a flat line in the Biosemi actiview software when recording.
>
> greets
> Gorka
>
>
> On 24 October 2012 04:28, Stephen Politzer-Ahles <politzerahless at gmail.com> wrote:
>>
>> Hi Gorka,
>>
>> Do you mean you found a normal ERP average in other channels? That would not be surprising, because baseline-correcting one channel doesn't affect other channels. On the other hand, if you mean that the channel had no data before baseline correction but then suddenly had a normal ERP after baseline correction, then I'm not sure why that would happen either...
>>
>> Best,
>> Steve Politzer-Ahles
>>
>> On Tue, Oct 23, 2012 at 9:47 AM, Gorka Fraga Gonzalez <gorkafraga at gmail.com> wrote:
>>>
>>> Dear EEGlab experts,
>>>
>>>
>>>
>>> I am analyzing data collected with a Biosemi 64 channels system. After a large sample tested one electrode did not get any signal in the last subjects so it was manually checked and apparently is dead.
>>>
>>>
>>> However after preprocessing data without excluding this channel I found a normal ERP average. The data was analysed in EEglab as it follows:
>>>
>>>
>>>
>>> Import ref average of mastoids// bandpass filter (1-70)// downsample to 256hz// Epoched// Remove baseline (…)
>>>
>>>
>>>
>>> I found that it was only after the “remove baseline “step that I could see signal in that channel when manually scrolling the data.
>>>
>>>
>>>
>>> It would be great if any of you could give me an explanation on this, since after reading about what the “baseline removal” procedure does it is still not clear to me why this could happen
>>>
>>>
>>>
>>> Many thanks in advance
>>>
>>> Gorka
>>>
>>>
>>> _______________________________________________
>>> Eeglablist page: http://sccn.ucsd.edu/eeglab/eeglabmail.html
>>> To unsubscribe, send an empty email to eeglablist-unsubscribe at sccn.ucsd.edu
>>> For digest mode, send an email with the subject "set digest mime" to eeglablist-request at sccn.ucsd.edu
>>
>>
>>
>>
>> --
>> Stephen Politzer-Ahles
>> University of Kansas
>> Linguistics Department
>> http://people.ku.edu/~sjpa/
>
>
>
> _______________________________________________
> eeglablist mailing list eeglablist at sccn.ucsd.edu
> Eeglablist page: http://www.sccn.ucsd.edu/eeglab/eeglabmail.html
> To unsubscribe, send an empty email to eeglablist-unsub at sccn.ucsd.edu
> To switch to non-digest mode, send an empty email to eeglablist-nodigest at sccn.ucsd.edu
More information about the eeglablist
mailing list