[Eeglablist] [Fwd: Eye Movement Artifacts and Filtering]
Arnaud Delorme
arno at ucsd.edu
Sun Apr 20 14:13:52 PDT 2008
Dear Justin,
> I am planning to use ICA in EEGLAB to remove eye movement artifacts. I
> know that ICA does not use EOG channels locate the component associated
> with eye movements. However, my advisor will want me to show ERP averages
> of the EOG channels as evidence that the eye movements have been removed
> using ICA. So, I will be including the EOG channels (bipolar, but
> represented as individual difference waves as one channel, for HEO and
> VEO).
>
It is OK to include bipolar channels for that purpose. ICA should be
able to detect the difference in reference (although you might have to
search for the component that accounts for that). Then you may compute
ERP for your EOG channels. A better idea though would be to do that for
frontal channels such as FPz.
> Finally, I need to run an acausal FIR high pass filter at 1 Hz. [which
> will not change the temporal components of the EEG] on the data before
> running ICA, as there is an extreme trend in the data that made automated
> and even manual eye movement removal inadequate using the raw data outside
> of EEGLAB. Also, the trend needs to be removed anyway. However, removing
> the linear trend will not adequately alter the tend in the continuous data
> as the FIR filter will. So, I wish to use such a filter. [I may remove
> the linear trend once the data is epoched per condition, but not on the
> continuous data.] What sort of filters does EEGLAB have built in? Is this
> filter already available?
>
Yes, there are 2 types of filters available in EEGLAB by default. I
would advise to use the non-linear IIR filter which has better design
than the linear FIR filter (and does not introduce phase distortion
because it is applied in both directions). There is also an additional
plugin that allows more advanced filtering.
http://www.uni-leipzig.de/~biocog/content/widmann/eeglab-plugins/#firfilt
Sorry for the late answer. Answer do come in time if you are patient.
Best regards,
Arno
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