[Eeglablist] Using ICA for labelling artifacts

[Cedric Cannard]

Hello,

My 2 cents are:
1) ICA does not perform well with few channels (I don't remember the minimum) and lowpass-filtered data (keeping high-frequency helps the algorithm identify high-frequency bursts).

2) I would include other artifactual components if the goal is to discriminate between "Clean" and "Artifactual".

> From our preliminary analysis, more than 50% of windows are labeled for Artifact, hence the concern.

Are you saying you are running ICA on each 4 s epoch?? No wonder it doesn't work. The algorithm needs data to learn and separate components.

Cedric Cannard



------- Original Message -------
On Tuesday, July 19th, 2022 at 5:45 AM, Velu Prabhakar Kumaravel via eeglablist <eeglablist at sccn.ucsd.edu> wrote:


> Hello,
>
> Does anyone have some thoughts to share on this?
>
> Thanks,
>
> Velu
>
> On Thu, 14 Jul 2022 at 12:00, Velu Prabhakar Kumaravel <
> velu.kumaravel at unitn.it> wrote:
>
> > Dear EEGLABers,
> >
> > As we know, it is rare to find data labeled for artifacts which makes it
> > hard to develop or validate artifacts detection algorithms. My colleagues
> > and I wonder if we could use ICA followed by ICLabel to label the segments
> > of data as "Artifact" or "Clean". The steps are as follows:
> >
> > 0) To facilitate reliable ICA decomposition for short windows of data,
> > only 4 channels are kept (known apriori)
> > 1) Bandpass filter (1-45 Hz)
> > 2) Segment data into 4s non-overlapping windows (sampling rate = 256 Hz,
> > 1024 samples)
> > 3) Perform ICA
> > 4) Classify components using ICLabel
> > 5) If there exists at least 1 eye or muscle Component with probability >
> > 0.95, the given window of data is "Artifact". Otherwise, "Clean".
> >
> > Do we see any potential problems in the approach? From our preliminary
> > analysis, more than 50% of windows are labeled for Artifact, hence the
> > concern.
> >
> > I appreciate your feedback.
> >
> > Thanks and regards,
> >
> > Velu Prabhakar Kumaravel, Ph.D. Student
> > Center for Mind/Brain Sciences,
> > University of Trento, Italy
>
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