mne_epochs_to_polars
mne.mne2dataframe.mne_epochs_to_polars(epo: mne.BaseEpochs)Convert MNE Epochs object to Polars DataFrame.
Converts epoched EEG/MEG data from MNE format to a long-format Polars DataFrame. Data is automatically scaled from Volts to microvolts (µV). If the epochs object contains metadata, it is automatically joined to each epoch’s data.
Parameters
| Name | Type | Description | Default |
|---|---|---|---|
| epo | mne.BaseEpochs | MNE Epochs object (e.g., mne.Epochs, mne.EpochsArray) containing epoched data. | required |
Returns
| Name | Type | Description |
|---|---|---|
| pl.DataFrame | Polars DataFrame with columns: - Channel columns: One column per channel with data in µV - time: Time in seconds relative to epoch onset - epoch_nr: Index identifying each epoch (0 to n_epochs-1) - sample_idx: Global continuous sample index across all epochs - Metadata columns: Any columns from epo.metadata (if present) |
Examples
>>> import mne
>>> from mdu.mne.mne2dataframe import mne_epochs_to_polars
>>> # Create sample epochs
>>> info = mne.create_info(['Ch1', 'Ch2'], sfreq=100, ch_types='eeg')
>>> data = np.random.randn(5, 2, 50) * 1e-6 # 5 epochs, 2 channels, 50 times
>>> epochs = mne.EpochsArray(data, info)
>>> df = mne_epochs_to_polars(epochs)
>>> df.shape
(250, 5) # 5 epochs * 50 timepoints, with Ch1, Ch2, time, epoch_nr, sample_idxSee Also
mne_raw_to_polars : Convert continuous Raw data to DataFrame