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

[MD KHORSHED ALAM]

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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