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"""
==============================================================
Compute real-time power spectrum density with FieldTrip client
==============================================================
Please refer to `ftclient_rt_average.py` for instructions on
how to get the FieldTrip connector working in MNE-Python.
This example demonstrates how to use it for continuous
computation of power spectra in real-time using the
get_data_as_epoch function.
"""
# Author: Mainak Jas <mainak@neuro.hut.fi>
#
# License: BSD (3-clause)
import numpy as np
import matplotlib.pyplot as plt
import time
import mne
from mne.realtime import FieldTripClient
from mne.time_frequency import psd_welch
print(__doc__)
# user must provide list of bad channels because
# FieldTrip header object does not provide that
bads = ['MEG 2443', 'EEG 053']
fig, ax = plt.subplots(1)
with FieldTripClient(host='localhost', port=1972,
tmax=150, wait_max=10) as rt_client:
# get measurement info guessed by MNE-Python
raw_info = rt_client.get_measurement_info()
# select gradiometers
picks = mne.pick_types(raw_info, meg='grad', eeg=False, eog=True,
stim=False, include=[], exclude=bads)
n_fft = 256 # the FFT size. Ideally a power of 2
n_samples = 2048 # time window on which to compute FFT
# make sure at least one epoch is available
time.sleep(n_samples / raw_info['sfreq'])
for ii in range(20):
epoch = rt_client.get_data_as_epoch(n_samples=n_samples, picks=picks)
psd, freqs = psd_welch(epoch, fmin=2, fmax=200, n_fft=n_fft)
cmap = 'RdBu_r'
freq_mask = freqs < 150
freqs = freqs[freq_mask]
log_psd = 10 * np.log10(psd[0])
tmin = epoch.events[0][0] / raw_info['sfreq']
tmax = (epoch.events[0][0] + n_samples) / raw_info['sfreq']
if ii == 0:
im = ax.imshow(log_psd[:, freq_mask].T, aspect='auto',
origin='lower', cmap=cmap)
ax.set_yticks(np.arange(0, len(freqs), 10))
ax.set_yticklabels(freqs[::10].round(1))
ax.set_xlabel('Frequency (Hz)')
ax.set_xticks(np.arange(0, len(picks), 30))
ax.set_xticklabels(picks[::30])
ax.set_xlabel('MEG channel index')
im.set_clim()
else:
im.set_data(log_psd[:, freq_mask].T)
plt.title('continuous power spectrum (t = %0.2f sec to %0.2f sec)'
% (tmin, tmax), fontsize=10)
plt.pause(0.5)
plt.close()
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