The most common way to identify the artifacts in Fp1-Fp2 ,f7-F8 is to register the EOG. The signals in frontal elctrodes usually sinchronous with vertical eyes movements(same polarity), f7-f8- with horizontal movements(opposite polarity). Using EOG helps you to identify these artifacts and usually you can eliminate them with any EEG software. Also you can indicate the limit of amplitude for non-artifact signals. Some software use ICA algorithm for artifacts. Anyway I dont like absolutely automatic artifacts deletion - you must "know the enemy by sight".

If you're interested in time-frequency approaches you should see this paper by Keren et al., 2010: http://www.ncbi.nlm.nih.gov/pubmed/19874901

See also:

http://www.ncbi.nlm.nih.gov/pubmed/21437227

http://www.ncbi.nlm.nih.gov/pubmed/17088100

http://www.ncbi.nlm.nih.gov/pubmed/10494051 (most methods outlined here are not restricted to sleep EEG)

Ocular artifacts on frontopolar electrodes are usually easy to identify: you can perform a correlation between an EOG channel and FP1 or FP2 channel and automatically reject the EEG epochs around the artifact whenever the correlation exceeds a fixed threshold.

You can also use a spectral analysis approach and reject EEG epochs when the delta band power on FP electrodes is high too much.

The better way you can use, in my opinion, is independent component analysis (ICA): using this approach you can identify the ocular component in the EEG and subtract it, without rejecting any epochs. With ICA you 'correct' the artifact, without losing EEG signal.

Hi, I suggest you to read this article: http://pe.org.pl/articles/2012/11a/50.pdf

May i know what is the procedure to use for the attached paper

I have developed an open source matlab toolbox for the automatic EEG artifact rejection, as published in: http://www.mdpi.com/1099-4300/16/12/6553

If you are interested, please visit: http://nadiamam.wix.com/eawica

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