[Mne_analysis] EEG redundancy data reduction based on N-dimensional PCA

Teon Brooks teon.brooks at gmail.com
Thu Mar 1 02:09:49 EST 2018
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Hi there,

Just quickly looking over the code, it looks as though you are overwriting
`pca`. Within MNE, we have the `UnsupervisedSpatialFilter` that takes an
estimator `PCA` as an argument. It can then fit/fit_transform your data
`epochs.get_data()` which has the dimensions (n_epochs, channels, time).

It seems you did create this with the first `pca` step. However, it seems
you then included an erroneous second step, which overwrites `pca` with
sklearn PCA object, which expects the data in the shape (n_samples,
n_features).

Try removing that second PCA step and see if it works.

HTH,

On Wed, Feb 28, 2018 at 9:52 PM, MD KHORSHED ALAM <
khorshed.alam at live.iium.edu.my> wrote:

> Dear MNE Group,
>
>
> I am currently working on MNE for Neuro-feedback application. I would like
> to use PCA for dimensional EEG data reduction.
>
>
> I followed the workflow explain in the example:
>
>
> epochs = mne.Epochs(eeg_channels, events=events, event_id=event_id,
> tmin=-0.2, tmax=0.5, proj=True, baseline=(None, 0), preload=True)
>
>
> X=epochs.get_data()
>
>
> pca = UnsupervisedSpatialFilter(PCA(19), average=False)
>
> pca = PCA(n_components=None, whiten=False)
>
> pca_data = pca.fit_transform(X)
>
> pca_reduced=pca.transform(X)
>
> Error:
>
>
> ValueError: Found array with dim 3. Estimator expected <= 2.
>
>
> Any help would be highly appreciated.
>
>
>
>
>
>
>
>
>
>
> * Thanks With Warm Regards, MD. KHORSHED ALAM (Shishir) Graduate Research
> Assistant (GRA) Center of Intelligent Signal and Imaging Research (CISIR)
> Department of Electrical and Electronic Engineering Universiti Teknologi
> PETRONAS Bandar Seri Iskandar 32610 Tronoh Perak Darul Ridzuan Malaysia *
>
>
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-- 
teon <http://www.twitter.com/teon_io>, phd
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