[Eeglablist] Flat data in a continuous EEG & Linear Regression

Makoto Miyakoshi mmiyakoshi at ucsd.edu
Mon Aug 11 11:47:20 PDT 2014


Dear Srini,

Please try 'clean_rawdata()'. It has a flat channel removal function.
http://sccn.ucsd.edu/wiki/Plugin_list_process

Makoto


On Wed, Jul 30, 2014 at 11:02 AM, Jerry Zhu <jerryzhu at siu.edu> wrote:

> Hi Srinivas,
>
> Flat lines have limited variable data. So you can try:
>
>     devStd = std(EEG.data(:,:), [], 2);
>     if any(devStd == 0)
>         flatchan = find(devStd == 0)';
>    end
>
> If you need complete codes, feel free to email me.
>
> Good luck with your study!
> Jerry
>
> --
> Jian Zhu, M.A.
> Brain and Cognitive Sciences
> Department of Psychology
> Southern Illinois University Carbondale
> Web: http://zhupsy.com
>
> We have two halves in the brain: left and right. Nothing is right in the
> left. Nothing is left in the right.
>
>
> On Wed, Jul 30, 2014 at 10:46 AM, Srinivas Kota <svkota at gmail.com> wrote:
>
>> Hello
>>
>> I have a few portions of flat data in a continuous EEG. Is there any
>> better way over visual inspection to reject those flat lines from
>> continuous EEG?
>>
>>
>> Gwin et. al 2010, (movement artifacts during walking/running) mentioned
>> "linearly regress artifact template from the data". What is the built-in
>> routine in EEGLAB to perform this?
>>
>> Best Regards,
>> Srini.
>>
>>
>> --
>>
>> ***********************************************************
>> Srinivas Kota, Ph.D
>> Research Scientist
>> Movement and Neurosciences Center
>> Institute for Rehabilitation Science and Engineering
>> Madonna Rehabilitation Hospital
>> 5401 South St, Lincoln, NE 68506
>> www.madonna.org/research_institute/index.html
>> <https://owa.madonna.org/owa/redir.aspx?C=RNnR5lrQvEmYU7lVtl6kLHIjwe3PiNAI8ouI90lD2Vnn4niuQ5QMBdLYDKmByFhfb525xS5XXT8.&URL=http%3a%2f%2fwww.madonna.org%2fresearch_institute%2findex.html>
>> ***********************************************************
>>
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-- 
Makoto Miyakoshi
Swartz Center for Computational Neuroscience
Institute for Neural Computation, University of California San Diego
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