Python mne.create_info方法代码示例

# 需要导入模块: import mne [as 别名]
# 或者: from mne import create_info [as 别名]
def main():
    BoardShim.enable_dev_board_logger ()
    # use synthetic board for demo
    params = BrainFlowInputParams ()
    board = BoardShim (BoardIds.SYNTHETIC_BOARD.value, params)
    board.prepare_session ()
    board.start_stream ()
    time.sleep (10)
    data = board.get_board_data ()
    board.stop_stream ()
    board.release_session ()

    eeg_channels = BoardShim.get_eeg_channels (BoardIds.SYNTHETIC_BOARD.value)
    eeg_data = data[eeg_channels, :]
    eeg_data = eeg_data / 1000000 # BrainFlow returns uV, convert to V for MNE

    # Creating MNE objects from brainflow data arrays
    ch_types = ['eeg', 'eeg', 'eeg', 'eeg', 'eeg', 'eeg', 'eeg', 'eeg']
    ch_names = ['T7', 'CP5', 'FC5', 'C3', 'C4', 'FC6', 'CP6', 'T8']
    sfreq = BoardShim.get_sampling_rate (BoardIds.SYNTHETIC_BOARD.value)
    info = mne.create_info (ch_names = ch_names, sfreq = sfreq, ch_types = ch_types)
    raw = mne.io.RawArray (eeg_data, info)
    # its time to plot something!
    raw.plot_psd (average = True)
    plt.savefig ('psd.png') 

# 需要导入模块: import mne [as 别名]
# 或者: from mne import create_info [as 别名]
def make_epochs(z_hat, info, t_lim, n_times_atom=1):
    """Make Epochs on the activations of atoms.
    n_splits, n_atoms, n_times_valid = z_hat.shape
    n_trials, n_atoms, n_times_epoch = z_hat_epoch.shape
    """
    n_splits, n_atoms, n_times_valid = z_hat.shape
    n_times = n_times_valid + n_times_atom - 1
    # pad with zeros
    padding = np.zeros((n_splits, n_atoms, n_times_atom - 1))
    z_hat = np.concatenate([z_hat, padding], axis=2)
    # reshape into an unique time-serie per atom
    z_hat = np.reshape(z_hat.swapaxes(0, 1), (n_atoms, n_splits * n_times))

    # create trials around the events, using mne
    new_info = mne.create_info(ch_names=n_atoms, sfreq=info['sfreq'])
    rawarray = mne.io.RawArray(data=z_hat, info=new_info, verbose=False)
    t_min, t_max = t_lim
    epochs = mne.Epochs(rawarray, info['events'], info['event_id'], t_min,
                        t_max, verbose=False)
    z_hat_epoched = epochs.get_data()
    return z_hat_epoched 

# 需要导入模块: import mne [as 别名]
# 或者: from mne import create_info [as 别名]
def test_handle_kind():
    """Test the automatic extraction of kind from the data."""
    # Create a dummy raw
    n_channels = 1
    sampling_rate = 100
    data = random((n_channels, sampling_rate))
    channel_types = ['grad', 'eeg', 'ecog']
    expected_kinds = ['meg', 'eeg', 'ieeg']
    # do it once for each type ... and once for "no type"
    for chtype, kind in zip(channel_types, expected_kinds):
        info = mne.create_info(n_channels, sampling_rate, ch_types=[chtype])
        raw = mne.io.RawArray(data, info)
        assert _handle_kind(raw) == kind

    # if the situation is ambiguous (EEG and iEEG channels both), raise error
    with pytest.raises(ValueError, match='Both EEG and iEEG channels found'):
        info = mne.create_info(2, sampling_rate,
                               ch_types=['eeg', 'ecog'])
        raw = mne.io.RawArray(random((2, sampling_rate)), info)
        _handle_kind(raw)

    # if we cannot find a proper channel type, we raise an error
    with pytest.raises(ValueError, match='Neither MEG/EEG/iEEG channels'):
        info = mne.create_info(n_channels, sampling_rate, ch_types=['misc'])
        raw = mne.io.RawArray(data, info)
        _handle_kind(raw) 

# 需要导入模块: import mne [as 别名]
# 或者: from mne import create_info [as 别名]
def create_raw(npts, ch_names=['F4-M1', 'F3-M2'], sf=100):
    """Utility function for test fit to data."""
    nchan = len(ch_names)
    info = mne.create_info(ch_names=ch_names, sfreq=sf,
                           ch_types=['eeg'] * nchan, verbose=0)
    data = np.random.rand(nchan, npts)
    raw = mne.io.RawArray(data, info, verbose=0)
    return raw 

# 需要导入模块: import mne [as 别名]
# 或者: from mne import create_info [as 别名]
def write_mnefiff(data, filename):
    """Export data to MNE using FIFF format.

    Parameters
    ----------
    data : instance of ChanTime
        data with only one trial
    filename : path to file
        file to export to (include '.mat')

    Notes
    -----
    It cannot store data larger than 2 GB.
    The data is assumed to have only EEG electrodes.
    It overwrites a file if it exists.
    """
    from mne import create_info, set_log_level
    from mne.io import RawArray

    set_log_level(WARNING)

    TRIAL = 0
    info = create_info(list(data.axis['chan'][TRIAL]), data.s_freq, ['eeg', ] *
                       data.number_of('chan')[TRIAL])

    UNITS = 1e-6  # mne wants data in uV
    fiff = RawArray(data.data[0] * UNITS, info)

    if data.attr['chan']:
        fiff.set_channel_positions(data.attr['chan'].return_xyz(),
                                   data.attr['chan'].return_label())

    fiff.save(filename, overwrite=True) 

# 需要导入模块: import mne [as 别名]
# 或者: from mne import create_info [as 别名]
def preprocess(self):
        # filtering
        self.eeg = butter_bandpass_filter(self.eeg, 0.15, self.sfreq)
        self.emg = butter_bandpass_filter(self.emg, 10.0, self.sfreq)
        self.eog = butter_bandpass_filter(self.eog, 0.15, self.sfreq)
        
        # resampling
        if not np.isclose(self.sfreq, 100): 
            print('resampling from {} Hz to {} Hz'.format(self.sfreq, 100))
            if self.use_mp and hasattr(SleepData,'pool') and type(SleepData.pool) is Pool:
                res_eeg = SleepData.pool.apply_async(mne.io.RawArray(np.stack([self.eeg]), mne.create_info(1, self.sfreq, 'eeg'), verbose=0).resample, args = (100.,))
                res_emg = SleepData.pool.apply_async(mne.io.RawArray(np.stack([self.emg]), mne.create_info(1, self.sfreq, 'eeg'), verbose=0).resample, args = (100.,))
                res_eog = SleepData.pool.apply_async(mne.io.RawArray(np.stack([self.eog]), mne.create_info(1, self.sfreq, 'eeg'), verbose=0).resample, args = (100.,))
                eeg,_ = res_eeg.get(timeout=30)[0,:]
                emg,_ = res_emg.get(timeout=30)[0,:]
                eog,_ = res_eog.get(timeout=30)[0,:]

            else:
                eeg,_ = mne.io.RawArray(np.stack([self.eeg]), mne.create_info(1, self.sfreq, 'eeg'), verbose=0).resample(100.)[0,:]
                emg,_ = mne.io.RawArray(np.stack([self.emg]), mne.create_info(1, self.sfreq, 'eeg'), verbose=0).resample(100.)[0,:]
                eog,_ = mne.io.RawArray(np.stack([self.eog]), mne.create_info(1, self.sfreq, 'eeg'), verbose=0).resample(100.)[0,:]
                  
            self.sfreq = 100
            self.eeg = eeg.squeeze()
            self.emg = emg.squeeze()
            self.eog = eog.squeeze() 

# 需要导入模块: import mne [as 别名]
# 或者: from mne import create_info [as 别名]
def create_empty_raw():
    n_channels = 3
    n_points = 300
    data = np.empty([n_channels, n_points])
    fs = 200.
    info = mne.create_info(ch_names=n_channels, sfreq=fs)
    raw = mne.io.RawArray(data, info)
    return raw 

# 需要导入模块: import mne [as 别名]
# 或者: from mne import create_info [as 别名]
def _simulate_data(fwd_fixed, source_vertno1, source_vertno2):
    """Simulate two oscillators on the cortex."""

    sfreq = 50.  # Hz.
    base_freq = 10
    t_rand = 0.001
    std = 0.1
    times = np.arange(10. * sfreq) / sfreq  # 10 seconds of data
    n_times = len(times)
    # Generate an oscillator with varying frequency and phase lag.
    iflaw = base_freq / sfreq + t_rand * np.random.randn(n_times)
    signal1 = np.exp(1j * 2.0 * np.pi * np.cumsum(iflaw))
    signal1 *= np.conj(signal1[0])
    signal1 = signal1.real

    # Add some random fluctuations to the signal.
    signal1 += std * np.random.randn(n_times)
    signal1 *= 1e-7

    # Make identical signal
    signal2 = signal1.copy()

    # Add random fluctuations
    signal1 += 1e-8 * np.random.randn(len(times))
    signal2 += 1e-8 * np.random.randn(len(times))

    # Construct a SourceEstimate object
    stc = mne.SourceEstimate(
        np.vstack((signal1[np.newaxis, :], signal2[np.newaxis, :])),
        vertices=[np.array([source_vertno1]), np.array([source_vertno2])],
        tmin=0,
        tstep=1 / sfreq,
        subject='sample',
    )

    # Create an info object that holds information about the sensors
    info = mne.create_info(fwd_fixed['info']['ch_names'], sfreq,
                           ch_types='grad')
    info.update(fwd_fixed['info'])  # Merge in sensor position information

    # Simulated sensor data.
    raw = mne.apply_forward_raw(fwd_fixed, stc, info)

    # Add noise
    noise = random.randn(*raw._data.shape) * 1e-14
    raw._data += noise

    # Define a single epoch
    epochs = mne.Epochs(raw, np.array([[0, 0, 1]]), event_id=1, tmin=0,
                        tmax=raw.times[-1], preload=True)

    # Compute the cross-spectral density matrix
    csd = csd_morlet(epochs, frequencies=[10, 20])

    return csd 

# 需要导入模块: import mne [as 别名]
# 或者: from mne import create_info [as 别名]
def _connect(self):
        """Connect to stream and record data to list."""
        self._stream_inlet = _get_stream_inlet(self.lsl_predicate)
        self._active = True

        # Extract stream info.
        info = self._stream_inlet.info()

        # Get sampling frequency.
        sfreq = float(info.nominal_srate())

        # Get channel names.
        ch_names = []
        this_child = info.desc().child('channel')
        for _ in range(info.channel_count()):
            ch_names.append(this_child.child_value('name'))
            this_child = this_child.next_sibling('channel')

        # Get the EEG measurement unit (e.g., microvolts).
        units = []
        this_child = info.desc().child('channel')
        for _ in range(info.channel_count()):
            units.append(this_child.child_value('unit'))
            this_child = this_child.next_sibling('channel')
        if all(units):
            self._eeg_unit = units[0]
        else:
            logger.warning("Could not find EEG measurement unit.")

        # Add stimulus channel.
        ch_types = ['eeg' for _ in ch_names] + ['stim']
        ch_names.append('STI 014')

        # Create mne.Info object.
        try:
            self.info = create_info(ch_names=ch_names,
                                    sfreq=sfreq, ch_types=ch_types,
                                    montage=self.key)
        except ValueError:
            self.info = create_info(ch_names=ch_names,
                                    sfreq=sfreq, ch_types=ch_types,
                                    montage=None)
            logger.warning("Could not find montage for '{}'"
                           "".format(self.key))

        # Add time of recording.
        dt = datetime.datetime.now()
        timestamp = time.mktime(dt.timetuple())
        self.info['meas_date'] = [timestamp, 0]

        # Record data in a while loop.
        self._record_data_indefinitely(self._stream_inlet) 

# 需要导入模块: import mne [as 别名]
# 或者: from mne import create_info [as 别名]
def read_raw_xdf(fname, stream_id, *args, **kwargs):
    """Read XDF file.

    Parameters
    ----------
    fname : str
        Name of the XDF file.
    stream_id : int
        ID (number) of the stream to load.

    Returns
    -------
    raw : mne.io.Raw
        XDF file data.
    """
    from pyxdf import load_xdf, match_streaminfos, resolve_streams

    streams, header = load_xdf(fname)
    for stream in streams:
        if stream["info"]["stream_id"] == stream_id:
            break  # stream found

    n_chans = int(stream["info"]["channel_count"][0])
    fs = float(stream["info"]["nominal_srate"][0])
    labels, types, units = [], [], []
    try:
        for ch in stream["info"]["desc"][0]["channels"][0]["channel"]:
            labels.append(str(ch["label"][0]))
            if ch["type"]:
                types.append(ch["type"][0])
            if ch["unit"]:
                units.append(ch["unit"][0])
    except (TypeError, IndexError):  # no channel labels found
        pass
    if not labels:
        labels = [str(n) for n in range(n_chans)]
    if not units:
        units = ["NA" for _ in range(n_chans)]
    info = mne.create_info(ch_names=labels, sfreq=fs, ch_types="eeg")
    # convert from microvolts to volts if necessary
    scale = np.array([1e-6 if u == "microvolts" else 1 for u in units])
    raw = mne.io.RawArray((stream["time_series"] * scale).T, info)
    raw._filenames = [fname]
    first_samp = stream["time_stamps"][0]
    markers = match_streaminfos(resolve_streams(fname), [{"type": "Markers"}])
    for stream_id in markers:
        for stream in streams:
            if stream["info"]["stream_id"] == stream_id:
                break
        onsets = stream["time_stamps"] - first_samp
        descriptions = [item for sub in stream["time_series"] for item in sub]
        raw.annotations.append(onsets, [0] * len(onsets), descriptions)
    return raw