I designed my data collection in the way that participants didn’t need to do any task, therefore, my data doesn’t have any event (I have about 4 minutes continuous data collected in each file). My aim is to measure power for each EEG frequency band during this 4 minutes . I have several questions:
Since I don’t have any events and I want the power for the frequency bands for the all 4 minutes, I think I don’t need to epoch the data. Please correct me if I’m wrong.
I tried to do baseline correction before running ICA using mne.baseline.rescale() but I received the following error
AttributeError Traceback (most recent call last)
----> 1 mne.baseline.rescale(notched_filt_raw,350 , (None,None), mode=‘mean’, copy=True)
in rescale(data, times, baseline, mode, copy, picks, verbose)
~\Anaconda3\envs\mne\lib\site-packages\mne\baseline.py in rescale(data, times, baseline, mode, copy, picks, verbose)
65 msg = _log_rescale(baseline, mode)
—> 67 if baseline is None or data.shape[-1] == 0:
68 return data
AttributeError: ‘RawEDF’ object has no attribute ‘shape’
Can you please help me how to solve this error?
- I went through the tutorial and found some functions to calculate power for each frequency band and found some functions such as psd_multitaper() and psd_welch(). But all the functions that I’ve found are for epoched data, can you please let me know how can I do that for continuous data.