 ```python
import os
import mne
from mne.datasets import spm_faces
from mne.preprocessing import ICA, create_eog_epochs, create_ecg_epochs
from mne.minimum_norm import apply_inverse_epochs, read_inverse_operator
from mne.viz import plot_topomap, plot_watershed
from mne.simulation import plot_source_estimate_on_meg

# Set data directory
data_path = spm_faces.data_path()

# Load raw data
raw_fname = os.path.join(data_path, 'MEG', 'raw', 'sample_audvis_raw.fif')
raw = mne.io.read_raw_fif(raw_fname)

# Filter data
raw.filter(l_freq=1., h_freq=40.)

# Set up epochs
event_id = {'faces': 1, 'scrambled': 2}
epochs = mne.Epochs(raw, events=dict(faces=event_id), event_id=event_id, tmin=-0.2, tmax=0.5)

# Fit ICA and remove artifacts
ica = ICA(n_components=15)
ica.fit(epochs)
eog_epochs, _ = create_eog_epochs(raw, event_related_ averages=False)
ecg_epochs, _ = create_ecg_epochs(raw, event_related_averages=False)
ica.exclude = []
for i in range(eog_epochs.events.shape[0]):
    idx = ica.find_bads_eog(eog_epochs[i])
    if idx:
        ica.exclude.extend(idx)
for i in range(ecg_epochs.events.shape[0]):
    idx = ica.find_bads_ecg(ecg_epochs[i])
    if idx:
        ica.exclude.extend(idx)
cleaned_epochs = ica.apply(epochs)

# Compute noise covariance
cov = mne.compute_covariance(cleaned_epochs, tmin=0.)

# Compute forward model
forward = mne.make_forward_solution(raw.info, trans=None, meg=True, eeg=False,
                                    noise_cov=cov, loose=0.2, depth=0.1)

# Source reconstruction using dSPM on the contrast "faces - scrambled"
contrast = ['faces', 'scrambled']
inverse_operator = read_inverse_operator(os.path.join(data_path, 'MEG', 'inverse-operator.fif'))
stc = apply_inverse_epochs(cleaned_epochs, inverse_operator, method='dSPM', pick_ori='max-flip',
                           lambda2=1., noise_cov=cov)
stc_contrast = stc.average().apply_contrast(contrast)

# Visualize the contrast
plot_topomap(stc_contrast.data, cleaned_epochs.info, time_viewer=False, title="dSPM contrast: faces - scrambled")

# Visualize fields on MEG helmet
plot_source_estimate_on_meg(stc_contrast, forward, trans=None, subjects_dir=None,
                             inst=None, time_viewer=False, background='white',
                             verbose=None)

# Compute forward and inverse solutions
fwd = mne.make_forward_solution(raw.info, trans=forward['trans'], meg=True, eeg=False,
                                noise_cov=cov, loose=0.2, depth=0.1)
inv = mne.minimum_norm.make_inverse_operator(fwd, loose=0.2, depth=0.1)

# Plot the contrast in 3D
brain_src = mne.setup_source_space(subject='sample', spacing='ico4',
                                   subjects_dir=data_path)
vol_src = mne.setup_volume_source_space(subject='sample', coords_file=os.path.join(data_path, 'MEG',
                                                                                 'sample-5000-src.fif'),
                                       subjects_dir=data_path)
stc_3d = mne.minimum_norm.apply_inverse_3d(stc_contrast, inv, fwd, brain_src, vol_src,
                                          pick_ori='max-flip', lambda2=1.,
                                          return_vol_data=True)
plot_watershed(stc_3d.data[0], cleaned_epochs.info, title="dSPM contrast: faces - scrambled")
```
Please note that the code assumes you have the MNE-Python library installed and the SPM Faces dataset downloaded. Also, the code might require adjustments depending on the specific version of the dataset and the MNE-Python library.