Hi everyone!

I’m currently working on a project where I utilize SPoC to extract EEG spatial patterns from EMG data. I am having some trouble with figuring out the best way to do so and was hoping I would be able to find some clarity here.

My first approach is to do a log transformation on the EMG data to help with outliers and take the first principle component. Then I am between taking the variance per trial or the sum power per trial as my EMG feature for the SPoC decomposition. This is because I am working with Epoched data with dimensions *(648, 61, 1200)* for EEG *(with 100 sfreq)* and *(648, 2400)* for EMG *(with 200 sfreq)* which would give me the dimension *(648,)* for the EMG as the target.

Here is the documentation for the SPoC `fit(X, y)`

parameters:

**X** `ndarray`

, shape (n_epochs, n_channels, n_times)

**y** `array`

, shape (n_epochs,)

This seems to work for both the sum power and variance methods - however; I find it strange to only have one value per epoch to extract these patterns from. Thus I decided to try the `fit_transform(X, y, **fit_params)`

method which has parameters:

**X** `array`

, shape (n_samples, n_features)

**y** `array`

, shape (n_samples,)

giving a target value for each sample. So I downsampled my EMG data to match the EEG data with 100 sfreq, performed the log transformation, and took the first PC from the EMG and then flattened both *(n_epochs * n_times)* so the dimensions are now: *(777600, 61)* for EEG and *(777600,)* for EMG.

However, when I do the following:

```
spoc = SPoC(n_components=6, reg=0.001)
spoc_data = spoc.fit_transform(flattened_eeg, flattened_emg)
```

I get the following which seems to contradict the documentation:

`ValueError: X must have at least 3 dimensions.`

Has anyone else run into this issue? Do you have advice if I should stick with my first method (and if so with the sum power or rather the variance?) or continue along this avenue?

I am using:

- MNE version: mne==1.7.0
- operating system: macOS Sonoma 14.2.1

Any help is very much appreciated! Thank you