4D Neuroimaging/BTi phantom dataset tutorial

Here we read 4DBTi epochs data obtained with a spherical phantom using four different dipole locations. For each condition we compute evoked data and compute dipole fits.

Data are provided by Jean-Michel Badier from MEG center in Marseille, France.

# Authors: Alex Gramfort <alexandre.gramfort@inria.fr>
#
# License: BSD-3-Clause
# Copyright the MNE-Python contributors.

import os.path as op

import numpy as np

import mne
from mne.datasets import phantom_4dbti

Read data and compute a dipole fit at the peak of the evoked response

data_path = phantom_4dbti.data_path()
raw_fname = op.join(data_path, "{}/e,rfhp1.0Hz")

dipoles = list()
sphere = mne.make_sphere_model(r0=(0.0, 0.0, 0.0), head_radius=0.080)

t0 = 0.07  # peak of the response

pos = np.empty((4, 3))
ori = np.empty((4, 3))

for ii in range(4):
    raw = mne.io.read_raw_bti(
        raw_fname.format(
            ii + 1,
        ),
        rename_channels=False,
        preload=True,
    )
    raw.info["bads"] = ["A173", "A213", "A232"]
    events = mne.find_events(raw, "TRIGGER", mask=4350, mask_type="not_and")
    epochs = mne.Epochs(
        raw, events=events, event_id=8192, tmin=-0.2, tmax=0.4, preload=True
    )
    evoked = epochs.average()
    evoked.plot(time_unit="s")
    cov = mne.compute_covariance(epochs, tmax=0.0)
    dip = mne.fit_dipole(evoked.copy().crop(t0, t0), cov, sphere)[0]
    pos[ii] = dip.pos[0]
    ori[ii] = dip.ori[0]

Compute localisation errors

actual_pos = 0.01 * np.array(
    [[0.16, 1.61, 5.13], [0.17, 1.35, 4.15], [0.16, 1.05, 3.19], [0.13, 0.80, 2.26]]
)
actual_pos = np.dot(actual_pos, [[0, 1, 0], [-1, 0, 0], [0, 0, 1]])

errors = 1e3 * np.linalg.norm(actual_pos - pos, axis=1)
print(f"errors (mm) : {errors}")

Plot the dipoles in 3D

actual_amp = np.ones(len(dip))  # fake amp, needed to create Dipole instance
actual_gof = np.ones(len(dip))  # fake GOF, needed to create Dipole instance
dip = mne.Dipole(dip.times, pos, actual_amp, ori, actual_gof)
dip_true = mne.Dipole(dip.times, actual_pos, actual_amp, ori, actual_gof)

fig = mne.viz.plot_alignment(evoked.info, bem=sphere, surfaces=[])

# Plot the position of the actual dipole
fig = mne.viz.plot_dipole_locations(
    dipoles=dip_true, mode="sphere", color=(1.0, 0.0, 0.0), fig=fig
)
# Plot the position of the estimated dipole
fig = mne.viz.plot_dipole_locations(
    dipoles=dip, mode="sphere", color=(1.0, 1.0, 0.0), fig=fig
)

Estimated memory usage: 0 MB