[Eeglablist] artifact detection in continuous data

[Christian Kothe]

Dear James,

I also have a few functions here that operate on continuous data.
Their parameters are not fine-tuned, but with appropriate settings
they work pretty well when artifacts show up only sporadically (as in
a typical well-controlled experimental setting).

One is for EOG artifacts (assumes that you have EOG channels). The
others are for bad channels, sporadic muscle artifacts, and the like.
The function clean_peaks is special in that it is not safe for
scientific use, as it leaves behind zeroes where there used to be
artifacts (this produces data that doesn't look like artifacts, but is
statistically coupled to them).

Best,
Christian

2011/6/9 Mahesh Casiraghi <mahesh.casiraghi at gmail.com>:
> Dear James,
>
> completely agree that this is an issue: I also find the possibility to apply
> the same artifact detection techniques available for segmented data  to
> continuous data quite necessary, and needed to write some custom workarounds
> whenever in the condition to do so, both in EEGLAB and ERPLAB. I am not
> aware of common ways to systematically do that and would be very happy - and
> have the feeling I am not alone in that - to see such a technique you are
> proposing implemented in a shareable tool or in a simple bunch of reliable
> functions!
>
> Perhaps Arnaud is already aware (as he often is) of an already implemented
> way or workaround for easily doing that.
>
> Cheers,
>
> Mahesh
>
>
>
> Mahesh M. Casiraghi
> PhD candidate - Cognitive Sciences
> Roberto Dell'Acqua Lab, University of Padova
> Pierre Jolicoeur Lab, Univesité de Montréal
> mahesh.casiraghi at umontreal.ca
> I have the conviction that when Physiology will be far enough advanced, the
> poet, the philosopher, and the physiologist will all understand each other.
> Claude Bernard
>
>
>
> On Thu, Jun 9, 2011 at 10:53 AM, James Desjardins <jdesjardins at brocku.ca>
> wrote:
>>
>> Dear list members,
>>
>> For my purposes it is beneficial to reject artifacts in the continuous
>> data rather than following segmentation. I find the artifact detection
>> tools in EEGLAB extremely helpful when working with segmented data.
>>
>> I am considering working on a plugin that windows continuous data,
>> takes advantage of some of the currently available artifact detection
>> tools for segmented data, then translates the windowed rejection
>> information back to time intervals in the continuous data.
>>
>> Are there already methods for doing this... or is this something that
>> someone else is already working on?
>>
>>
>> James Desjardins
>> Technician, MA Student
>> Department of Psychology, Behavioural Neuroscience
>> Cognitive and Affective Neuroscience Lab
>> Brock University
>> 500 Glenridge Ave.
>> St. Catharines, ON, Canada
>> L2S 3A1
>> 905-688-5550 x4676
>>
>>
>>
>>
>>
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