HF-SEF dataset

This example looks at high-frequency SEF responses.

# Author: Jussi Nurminen (jnu@iki.fi)
#
# License: BSD-3-Clause

import mne
import os
from mne.datasets import hf_sef

fname_evoked = os.path.join(hf_sef.data_path(),
                            'MEG/subject_b/hf_sef_15min-ave.fif')

print(__doc__)

Read evoked data

evoked = mne.Evoked(fname_evoked)

    Found the data of interest:
        t =     -50.00 ...     250.00 ms (SEF)
        0 CTF compensation matrices available
        nave = 2790 - aspect type = 100
No projector specified for this dataset. Please consider the method self.add_proj.

Create a highpass filtered version

evoked_hp = evoked.copy()
evoked_hp.filter(l_freq=300, h_freq=None)

Setting up high-pass filter at 3e+02 Hz

FIR filter parameters
---------------------
Designing a one-pass, zero-phase, non-causal highpass filter:
- Windowed time-domain design (firwin) method
- Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation
- Lower passband edge: 300.00
- Lower transition bandwidth: 75.00 Hz (-6 dB cutoff frequency: 262.50 Hz)
- Filter length: 133 samples (0.044 sec)

[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=1)]: Done   2 out of   2 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=1)]: Done   3 out of   3 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=1)]: Done   4 out of   4 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=1)]: Done 306 out of 306 | elapsed:    0.2s finished

Condition	SEF
Data kind	average
Timepoints	901 samples
Channels	306 channels
Number of averaged epochs	2790
Time range (secs)	-0.05 – 0.25
Baseline (secs)	off

Compare high-pass filtered and unfiltered data on a single channel

ch = 'MEG0443'
pick = evoked.ch_names.index(ch)
edi = {'HF': evoked_hp, 'Regular': evoked}
mne.viz.plot_compare_evokeds(edi, picks=pick)

Estimated memory usage: 9 MB