epoching on epoched data

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Hi,

Is it possible to make fixed length epochs on existing epoched file ? I currently have an EEG file that has epochs of length 30s each. I need to preprocess this file and run autoreject. To do so, I need to make 1s length epochs and then run autoreject. Is there a way I can create more epochs on the existing file ?

Thanks,
Apoorva.

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do you want to use autoreject local or global?

A

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global! I usually identify the global rejection threshold on the epoched EEG data and then run ICA and then compute channel level autorejections.

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Hi Apoorva,

You can do something like this:

for epochs_data in epochs:
    raw_epoch = mne.io.RawArray(epochs_data, epochs.info)
    epochs_autoreject = mne.make_fixed_length_epochs(raw_epoch, duration=1, preload=True)

where epochs is your long 30 s epochs and epochs_autoreject are the shorter epochs. Does this work for you?

Mainak

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ooh, that’s even better than what I came up with. @apoorva6262 and I discussed this during office hours today and I suggested

data = epochs.get_data()
pseudo_raw_data = np.hstack(data)
pseudo_raw = mne.io.RawArray(pseudo_raw_data, epochs.info)
new_epochs = mne.make_fixed_length_epochs(pseudo_raw, duration=1.)
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Thanks @drammock and @mainakjas .

For both suggestions, I get this error : numpy.ndarray’ object has no attribute ‘info’

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Are you sure you are working with epochs and not epochs.get_data()? The second is a numpy array and no attribute info.

Mainak

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Sorry, yes I did use epochs. Then it gave me an error that it is 3d and data must be 2d. So I tried using epochs.transpose.reshape and then it gave me an error that ‘EpochsFIF’ object has no attribute ‘transpose’

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please give us the exact lines of code that you ran as well as the error traceback. Also, some general advice:

  1. different kinds of data objects have different methods. NumPy arrays allow things like my_array.transpose() but MNE-Python Epochs objects do not have a .transpose() method. If you type the name of your variable in an iPython console and push . and then the tab key, you can get a pop-up of all its available methods and attributes. That can help you avoid trying things that won’t work (like my_epochs.transpose()… because “transpose” won’t be one of the available options that pops up). Here’s an example:

Screenshot_2021-09-07_12-12-48
Screenshot_2021-09-07_12-12-481915×627 81.7 KB

  1. in MNE-Python, the actual data (the numbers representing voltage X in channel Y at time Z) are stored in a NumPy array that is part of a larger object that also contains other data (like channel positions, etc). From any MNE-Python object you can always do my_object.get_data() or my_object.data if you need direct access to the NumPy array, but do not confuse the (Raw, Epochs, Evoked) object for the data array that it contains.
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Thank you . That was helpful.
This worked for me :

sfreq=epochs.info['sfreq']
ch_names=epochs.info['ch_names']
ch_types = ['eeg'] * 22 
info = mne.create_info(ch_names=ch_names, ch_types=ch_types, sfreq=sfreq)
epoch=epochs.get_data()
n_epochs, n_channels, n_times = epoch.shape
data=epoch.transpose([1, 0, 2]).reshape((n_channels, n_epochs * n_times))
raw_epoch = mne.io.RawArray(data, info)
new_epochs = mne.make_fixed_length_epochs(raw_epoch, duration=1, preload=True)
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glad you got it working! One more tiny suggestion: instead of doing epoch=epochs.get_data(), use a more descriptive variable name like epoch_data or data_array or similar. Having 2 variables called “epoch” and “epochs” implies that they are both the same type of object, differing in how many trials they include. In your code, however, one is a mne.Epochs object and the other is a numpy.ndarray object, and both of them contain all trials… so the variable names are misleading and make it easier to get confused.

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