Compute real-time power spectrum density with FieldTrip client¶

# Author: Mainak Jas <mainak@neuro.hut.fi>
#
# License: BSD (3-clause)

import numpy as np
import matplotlib.pyplot as plt

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
    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()