Hi there,

Iâ€™m trying to compute PCA with 95% variance on an epochs object.

Iâ€™ve written the below function to do so:

```
def run_PCA(epochs):
m = epochs.metadata
pca = UnsupervisedSpatialFilter(PCA(0.95), average=False)
X = epochs.get_data()
pca_data = pca.fit_transform(X)
events = epochs.events
pca_components = pca_data.shape[1]
info = mne.create_info(pca_components, epochs.info['sfreq'], ch_types='eeg')
epochs_pca = mne.EpochsArray(pca_data, info, events)
epochs_pca.metadata = m
return epochs_pca
```

This function has then be saved in a separate .py file and Iâ€™m calling the function from an .ipynb notebook. When initially tested in a notebook the code worked fine, but once called in a separate notebook it will only return an epochs object with a single channel. Does anyone know why this is? Iâ€™ve repeatedly had the same problem where computing PCA using the UnsupervisedSpatialFilter function, where n_components is a percentage of variance instead of a selected number of components.