Hi there,
I’m new to MNE (and electrophysiological data in general) so I apologize in advance if I’ve missed something obvious here. I’m just wondering why my data looks the way it does, that is to say, bad. I’ve brought in sEEG data from .edf files, and attempted to filter them based on power line noise and a high-pass filter. Below is a screenshot of the data I am getting:
Here is the code I am using:
raw = mne.io.read_raw_edf(file_name, preload=True)
raw.apply_function(lambda x: x*1e-6, channel_wise=False) # Adjusts the scaling by a factor of 7
start, stop = pull_start_stop(sub, mov1_trig) # Time of onset/end for current subject; see sub_channel_lists for timings/events
raw.crop(tmin=start, tmax=stop) # Crop raw file to event timings
# -- Raw Object Parameters -- #
ch_names = raw.ch_names # returns a list of all channel names
n_chan = len(ch_names) # Returns an integer denoting number of channels in the raw data
sfreq = int(raw.info['sfreq']) # Returns an integer for the sampling frequency
pl_freqs = (60, 120, 180, 240)
raw_filtered = raw.copy().notch_filter(freqs=pl_freqs, method='spectrum_fit', filter_length='5s')
raw_filtered = raw_filtered.copy().filter(l_freq=1.2, h_freq=None)
events = events = mne.make_fixed_length_events(raw, duration=.500)
event_id, tmin, tmax = 1, -0.5, 2.0 # need a long enough epoch for 5 cycles
picks = mne.pick_channels(raw.info['ch_names'], nets_of_interest)
epochs = mne.Epochs(raw_filtered, events, event_id, tmin, tmax, picks=picks,
baseline=(None, 0))
epochs.plot()
I have also tried using the “scalings=auto” function but it is essentially the same result. I can’t imagine that all my channels for all my participants are bad, and they all look like this.
Does anyone have any ideas about what might be going wrong here? Any input you might have is much appreciated. Thanks again!