```python
import mne
from mne.datasets import fetch_fsaverage
from mne.preprocessing import ICA
from mne.minimum_norm import make_inverse_operator, apply_inverse

# Load your data here (this is a placeholder)
data_path = mne.datasets.spm_face.data_path()
raw_fname = data_path + '/MEG/spm/SPM_CTF_MEG_example_faces1_3D.ds'

# Read the raw data
raw = mne.io.read_raw_ctf(raw_fname, preload=True)

# Filter data
raw.filter(1, 40)

# Set up and perform ICA
ica = ICA(n_components=20, random_state=97, max_iter=800)
ica.fit(raw)
ica.exclude = [0]  # assume 0 is EOG/ECG artifact component index
ica.apply(raw)

# Epoching
events = mne.find_events(raw, stim_channel='UPPT001')
event_id = {'faces': 1, 'scrambled': 2}
epochs = mne.Epochs(raw, events, event_id, tmin=-0.2, tmax=0.5, baseline=(None, 0), preload=True)

# Compute and visualize the contrast
contrast = mne.combine_evoked([epochs['faces'].average(), epochs['scrambled'].average()], weights=[1, -1])
contrast.plot_joint()

# Estimate noise covariance
noise_cov = mne.compute_covariance(epochs, tmax=0., method='shrunk')

# Visualize sensor alignment
info = epochs.info
mne.viz.plot_alignment(info, trans='fsaverage', subject='fsaverage', dig=True, meg=['helmet', 'sensors'], subjects_dir=fetch_fsaverage())

# Compute forward model
fwd = mne.make_forward_solution(info, trans='fsaverage-trans.fif', src='fsaverage-src.fif', bem='fsaverage-bem.fif', meg=True, eeg=False, mindist=5.0)

# Compute inverse operator
inverse_operator = make_inverse_operator(info, fwd, noise_cov)

# Apply dSPM (dynamic statistical parametric mapping) to get source estimates
method = "dSPM"
snr = 3.
lambda2 = 1. / snr ** 2
stc = apply_inverse(contrast, inverse_operator, lambda2, method=method, pick_ori=None)

# Plot the contrast in 3D
brain = stc.plot(subject='fsaverage', hemi='both', subjects_dir=fetch_fsaverage(), initial_time=0.1, time_viewer=True)
```