    [1]:

    import numpy as np
    from matplotlib import pyplot as plt

    import mne
    from mne import find_events, Epochs, compute_covariance, make_ad_hoc_cov
    from mne.datasets import sample
    from mne.simulation import simulate_sparse_stc, simulate_raw, add_noise, add_ecg, add_eog

    [41]:

    data_path = sample.data_path()
    raw_fname = data_path + '/MEG/sample/sample_audvis_raw.fif'
    fwd_fname = data_path + '/MEG/sample/sample_audvis-meg-eeg-oct-6-fwd.fif'

    # Load real data as the template
    raw = mne.io.read_raw_fif(raw_fname)
    raw.set_eeg_reference(projection=True)
    raw.pick_types(eeg=True)
    raw.pick_channels(raw.info.ch_names[:16])

    Opening raw data file /home/yeison/mne_data/MNE-sample-data/MEG/sample/sample_audvis_raw.fif...
        Read a total of 3 projection items:
            PCA-v1 (1 x 102)  idle
            PCA-v2 (1 x 102)  idle
            PCA-v3 (1 x 102)  idle
        Range : 25800 ... 192599 =     42.956 ...   320.670 secs
    Ready.
    Adding average EEG reference projection.
    1 projection items deactivated
    Removing projector <Projection | PCA-v1, active : False, n_channels : 102>
    Removing projector <Projection | PCA-v2, active : False, n_channels : 102>
    Removing projector <Projection | PCA-v3, active : False, n_channels : 102>

    [41]:

Measurement date | December 03, 2002 19:01:10 GMT
Experimenter | MEG
| Participant | Unknown
Digitized points | 146 points
Good channels | 0 magnetometer, 0 gradiometer, and 16 EEG channels
Bad channels |
EOG channels | Not available
ECG channels | Not available
Sampling frequency | 600.61 Hz
Highpass | 0.10 Hz
Lowpass | 172.18 Hz
Filenames | sample_audvis_raw.fif
Duration | 00:04:37 (HH:MM:SS)

    [46]:

    n_dipoles = 4  # number of dipoles to create
    epoch_duration = 2.  # duration of each epoch/event
    n = 0  # harmonic number
    rng = np.random.RandomState(0)  # random state (make reproducible)

    def data_fun(times):
        """Generate time-staggered sinusoids at harmonics of 10Hz"""
        global n
        n_samp = len(times)
        window = np.zeros(n_samp)
        start, stop = [int(ii * float(n_samp) / (2 * n_dipoles))
                       for ii in (2 * n, 2 * n + 1)]
        window[start:stop] = 1.
        n += 1
        data = 25e-9 * np.sin(2. * np.pi * 10. * n * times)
        data *= window
        return data

    times = raw.times[:int(raw.info['sfreq'] * epoch_duration)]
    fwd = mne.read_forward_solution(fwd_fname)
    src = fwd['src']
    stc = simulate_sparse_stc(src, n_dipoles=n_dipoles, times=times,
                              data_fun=data_fun, random_state=rng)
    # look at our source data
    fig, ax = plt.subplots(1)
    ax.plot(times, 1e9 * stc.data.T)
    ax.set(ylabel='Amplitude (nAm)', xlabel='Time (sec)')
    mne.viz.utils.plt_show()

    Reading forward solution from /home/yeison/mne_data/MNE-sample-data/MEG/sample/sample_audvis-meg-eeg-oct-6-fwd.fif...
        Reading a source space...
        Computing patch statistics...
        Patch information added...
        Distance information added...
        [done]
        Reading a source space...
        Computing patch statistics...
        Patch information added...
        Distance information added...
        [done]
        2 source spaces read
        Desired named matrix (kind = 3523) not available
        Read MEG forward solution (7498 sources, 306 channels, free orientations)
        Desired named matrix (kind = 3523) not available
        Read EEG forward solution (7498 sources, 60 channels, free orientations)
        MEG and EEG forward solutions combined
        Source spaces transformed to the forward solution coordinate frame

    [47]:

    raw_sim = simulate_raw(raw.info, [stc] * 10, forward=fwd, verbose=False)
    cov = make_ad_hoc_cov(raw_sim.info)
    add_noise(raw_sim, cov, iir_filter=[0.2, -0.2, 0.04], random_state=rng)
    # add_ecg(raw_sim, random_state=rng)
    # add_eog(raw_sim, random_state=rng)
    raw_sim.plot();

    Adding noise to 16/16 channels (16 channels in cov)

    [72]:

    x = np.linspace(0, 20, raw_sim._data.shape[1])
    xx = np.linspace(0, 20, 20*1000)
    y = []

    for channel in raw_sim._data:
        y.append(np.interp(xx, x, channel))

    y = np.array(y)
    y.shape

    [72]:

    (16, 20000)

    [73]:

    with open('fake_eeg-20s.npy', 'wb') as f:
        np.save(f, y)

    [77]:

    import rawutil

    def eeg_(values): return [rawutil.pack('>u', v // 24) for v in values.tolist()]

    [99]:

    eeg_data = np.array([rawutil.unpack('>u', bytes(ch))[0] for ch in eeg.reshape(-1, 3)]).reshape(-1, 8) * 24

    ---------------------------------------------------------------------------
    AttributeError                            Traceback (most recent call last)
    <ipython-input-99-b3380eea58a2> in <module>
    ----> 1 eeg_data = np.array([rawutil.unpack('>u', bytes(ch))[0] for ch in eeg.reshape(-1, 3)]).reshape(-1, 8) * 24

    AttributeError: 'list' object has no attribute 'reshape'

    [98]:

    eeg = list(b''.join(eeg_(np.array([400, 5, 6]))))
    eeg

    [98]:

    [0, 0, 16, 0, 0, 0, 0, 0, 0]