update: artifact detection

georges Otte georges.otte at PANDORA.BE
Sun Feb 5 15:22:41 CET 2006

Thanks Robert !

Georges Otte

Neurologpsychiatrie EEG-EMG
Otte Georges
georges.otte at pandora.be
Bijlokehof 24
9000 Gent
tel: 09 329 06 62
fax: 09 282 26 72
mobile: 0478 205 202

> -----Original Message-----
> From: FieldTrip discussion list
> [mailto:FIELDTRIP at NIC.SURFNET.NL] On Behalf Of Robert Oostenveld
> Sent: zondag 5 februari 2006 13:30
> Subject: Re: [FIELDTRIP] update: artifact detection
> Dear Georges,
> On 3-feb-2006, at 9:41, georges Otte wrote:
> > I saw some paper from a group in Leuven eliminating EMG
> artefact based
> > on the canonical component analysis (another Blind Source method).
> > Google it with CCA- EEG. It worked very fast. I think that ANT
> > compagny
> > is looking into porting this into their standard software. The
> > method is
> > based on the difference in aotocorellation between EEG and EMG
> > signals.
> We have implemented that algorithm in Fieldtrip, in the
> componentanalysis function (cfg.method=cca). I am not sure
> whether it
> is compleetly in the ftp release version of fieldtrip.
> We did try it out on our MEG data. It performed rather poor on our
> MEG data, the obvious EMG artifacts in the lower temporal channels
> were not very well separates into seperate components to be removed
> from the data. I suspect the reason for it performing rather poor on
> the MEG data is that it is based on 1-sample lag
> autocorrelation. The
> seperation (linear unmixing) using autocorrelation seperates the
> signal into high frequency components and low frequency components.
> But our MEG data has a much higher sampling rate (usually 1200Hz),
> and contains noise components at a much higher frequency than
> the EMG
> artifacts.
> It may work for clinical EEG data for which the Leuven group
> develloped the algorithm, since that is sampled at typically 250 Hz,
> which means that the EMG does correspond to the high frequency part
> of the data. It may be possible to tweak the algorithm (e.g. use N-
> lag autocorrelation) and to use differently preprocessed (filtered
> and downsampled) data to improve the performance, but we have not
> pursued that. Furthermore, we have not tested it on EEG data, so I
> cannot judge how it performs there.
> best
> Robert

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