示例#1
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def analyze_task(subject, args, hcp_params):
    flags = {}
    events = dict(face=1, tools=2)
    baseline = (-0.5, 0)

    all_events = collect_events(hcp_params)
    files_exist = all([
        op.isfile(meg.get_cond_fname(meg.EPO, event))
        and op.isfile(meg.get_cond_fname(meg.EVO, event))
        for event in events.keys()
    ])
    if not files_exist:
        epochs_hcp, evokeds_from_epochs_hcp = using_preprocessed_epochs(
            all_events, events, baseline, hcp_params)
        for event in events.keys():
            epochs_hcp[event].save(meg.get_cond_fname(meg.EPO, event))
            mne.write_evokeds(meg.get_cond_fname(meg.EVO, event),
                              evokeds_from_epochs_hcp[event])

    flags = meg.calc_fwd_inv_wrapper(subject, args, events, flags)
    flags, stcs_conds, _ = meg.calc_stc_per_condition_wrapper(
        subject, events, args.inverse_method, args, flags)
    flags = meg.calc_labels_avg_per_condition_wrapper(subject, events,
                                                      args.atlas,
                                                      args.inverse_method,
                                                      stcs_conds, args, flags)
示例#2
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def calc_evoked(indices, epochs_fname, overwrite_epochs=False, overwrite_evoked=False):
    epochs = mne.read_epochs(epochs_fname, preload=False)
    print(epochs.events.shape)
    for event_name, event_indices in indices.items():
        evoked_event_fname = meg.get_cond_fname(meg.EVO, event_name)
        epochs_event_fname = meg.get_cond_fname(meg.EPO, event_name)
        if not op.isfile(epochs_event_fname) or overwrite_epochs:
            print('Saving {} epochs to {}, events num: {}'.format(event_name, epochs_event_fname, len(event_indices)))
            event_epochs = epochs[event_indices]
            event_epochs.save(epochs_event_fname)
        if not op.isfile(evoked_event_fname) or overwrite_evoked:
            print('Saving {} evoked to {}'.format(event_name, evoked_event_fname))
            mne.write_evokeds(evoked_event_fname, event_epochs.average())
示例#3
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def calc_evoked(indices, epochs_fname, overwrite_epochs=False, overwrite_evoked=False):
    epochs = mne.read_epochs(epochs_fname, preload=False)
    print(epochs.events.shape)
    for event_name, event_indices in indices.items():
        evoked_event_fname = meg.get_cond_fname(meg.EVO, event_name)
        epochs_event_fname = meg.get_cond_fname(meg.EPO, event_name)
        if not op.isfile(epochs_event_fname) or overwrite_epochs:
            print('Saving {} epochs to {}, events num: {}'.format(event_name, epochs_event_fname, len(event_indices)))
            event_epochs = epochs[event_indices]
            event_epochs.save(epochs_event_fname)
        if not op.isfile(evoked_event_fname) or overwrite_evoked:
            print('Saving {} evoked to {}'.format(event_name, evoked_event_fname))
            mne.write_evokeds(evoked_event_fname, event_epochs.average())
示例#4
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文件: eeg.py 项目: XujiahuaBNU/mmvt
def save_evoked_to_blender(mri_subject, events, args, evoked=None):
    fol = op.join(MMVT_DIR, mri_subject, 'eeg')
    utils.make_dir(fol)
    if '{cond}' in meg.EVO:
        for event_ind, event_id in enumerate(events.keys()):
            if evoked is None:
                evo = mne.read_evokeds(meg.get_cond_fname(meg.EVO, event_id))
            else:
                evo = evoked[event_id]
            if event_ind == 0:
                ch_names = np.array(evo[0].ch_names)
                dt = np.diff(evo[0].times[:2])[0]
                data = np.zeros((evo[0].data.shape[0], evo[0].data.shape[1], 2))
            data[:, :, event_ind] = evo[0].data
    else:
        if evoked is None:
            evoked = mne.read_evokeds(meg.EVO)
        data = evoked[0].data
        data = data[..., np.newaxis]
        ch_names = np.array(evoked[0].ch_names)
        dt = np.diff(evoked[0].times[:2])[0]
    if 'Event' in ch_names:
        event_ind = np.where(ch_names == 'Event')[0]
        ch_names = np.delete(ch_names, event_ind)
        data = np.delete(data, event_ind, 0)
    data_max, data_min = utils.get_data_max_min(data, args.norm_by_percentile, args.norm_percs)
    max_abs = utils.get_max_abs(data_max, data_min)
    if args.normalize_evoked:
        data = data / max_abs
    np.save(op.join(fol, 'eeg_data.npy'), data)
    np.savez(op.join(fol, 'eeg_data_meta.npz'), names=ch_names, conditions=list(events.keys()), dt=dt,
             minmax=(-max_abs, max_abs))
    return True
示例#5
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文件: eeg.py 项目: pelednoam/mmvt
def save_evoked_to_blender(mri_subject, events, args, evoked=None):
    fol = op.join(MMVT_DIR, mri_subject, 'eeg')
    utils.make_dir(fol)
    if '{cond}' in meg.EVO:
        for event_ind, event_id in enumerate(events.keys()):
            if evoked is None:
                evo = mne.read_evokeds(meg.get_cond_fname(meg.EVO, event_id))
            else:
                evo = evoked[event_id]
            if event_ind == 0:
                ch_names = np.array(evo[0].ch_names)
                dt = np.diff(evo[0].times[:2])[0]
                data = np.zeros((evo[0].data.shape[0], evo[0].data.shape[1], 2))
            data[:, :, event_ind] = evo[0].data
    else:
        if evoked is None:
            evoked = mne.read_evokeds(meg.EVO)
        data = evoked[0].data
        data = data[..., np.newaxis]
        ch_names = np.array(evoked[0].ch_names)
        dt = np.diff(evoked[0].times[:2])[0]
    if 'Event' in ch_names:
        event_ind = np.where(ch_names == 'Event')[0]
        ch_names = np.delete(ch_names, event_ind)
        data = np.delete(data, event_ind, 0)
    if args.normalize_evoked:
        data_max, data_min = utils.get_data_max_min(data, args.norm_by_percentile, args.norm_percs)
        max_abs = utils.get_max_abs(data_max, data_min)
        data = data / max_abs
    np.save(op.join(fol, 'eeg_data.npy'), data)
    np.savez(op.join(fol, 'eeg_data_meta.npz'), names=ch_names, conditions=list(events.keys()), dt=dt)
    return True
示例#6
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def plot_evoked(indices):
    for event_name in indices.keys():
        evoked_fname = meg.get_cond_fname(meg.EVO, event_name)
        print('Reading {}'.format(event_name))
        evoked = mne.read_evokeds(evoked_fname)[0]
        evoked.plot()
示例#7
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def plot_evoked(indices):
    for event_name in indices.keys():
        evoked_fname = meg.get_cond_fname(meg.EVO, event_name)
        print('Reading {}'.format(event_name))
        evoked = mne.read_evokeds(evoked_fname)[0]
        evoked.plot()
示例#8
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             evoked_hcp.data.ravel()[::10] * 1e15,
             linestyle='None',
             marker='o',
             alpha=0.1,
             mec='orange',
             color='orange')
    plt.annotate("r=%0.3f" % r1, xy=(-300, 250))
    plt.ylabel('evoked from scratch with MNE-HCP')
    plt.xlabel('evoked from HCP evoked file')
    plt.show()


if __name__ == '__main__':
    tmin, tmax = -1.5, 2.5
    decim = 4
    events_id = dict(face=1, tools=2)
    baseline = (-0.5, 0)

    all_events = collect_events()
    # all_epochs, evokeds = reprocess_the_data_from_scratch(all_events, event_id, tmin, tmax, baseline, decim)
    all_epochs_hcp, evokeds_from_epochs_hcp = using_preprocessed_epochs(
        all_events, events_id)
    # evoked_hcp = official_ERF()

    all_epochs_hcp.save(meg.EPO)
    for event, evoked in zip(events_id.keys(), evokeds_from_epochs_hcp):
        mne.write_evokeds(meg.get_cond_fname(meg.EVO, event), evoked)

    mne.write_evokeds(meg.EVO, evokeds_from_epochs_hcp)
    print('Finish!')