  Here is the Python code for the full pipeline on the SPM Faces dataset using the MNE-Python library:
```
import mne
import numpy as np

# Load and filter data
raw = mne.io.read_raw_fif('faces_raw.fif', preload=True)
raw.filter(1, 40, h_trans_bandwidth=0.5, filter_length='auto',
           l_trans_bandwidth=0.5, n_jobs=1)

# Set up epochs
events = mne.find_events(raw, stim_channel='STI 014')
epochs = mne.Epochs(raw, events, event_id, tmin, tmax, proj=True,
                    baseline=None, reject=reject, flat=flat,
                    reject_by_annotation=reject_by_annotation,
                    picks=picks, add_channels=add_channels,
                    reject_by_annotation=reject_by_annotation,
                    decim=decim, verbose=verbose)

# Artifact removal
ica = mne.preprocessing.ICA(n_components=0.95, method='fastica',
                            random_state=42)
ica.fit(epochs)
epochs_ica = ica.apply(epochs)

# Averaging epochs
epochs_avg = epochs.average()

# Forward model computation
fwd = mne.make_forward_model(epochs_avg, fname='faces_fwd.fif',
                             overwrite=True)

# Source reconstruction using dSPM
stc = mne.minimum_norm.apply(epochs_avg, fwd, 'faces - scrambled',
                             method='dSPM', verbose=True)

# Estimate noise covariance
noise_cov = mne.compute_covariance(epochs, tmin=0, tmax=0.2,
                                   method='shrunk', verbose=True)

# Visualize fields on MEG helmet
mne.viz.plot_meg_helmet(fwd, trans=None, subject='faces',
                        subjects_dir=None, show_axes=True,
                        show_names=True, show_scrollbars=True,
                        show=True)

# Compute forward and inverse solutions
fwd_sol = mne.minimum_norm.solve_forward(fwd, stc, noise_cov,
                                         method='dSPM', verbose=True)
inv_sol = mne.minimum_norm.solve_inverse(fwd_sol, stc, noise_cov,
                                         method='dSPM', verbose=True)

# Plot contrast in 3D
mne.viz.plot_source_estimate(stc, fwd, subject='faces',
                             subjects_dir=None, time_unit='s',
                             time_viewer=True, show_traces=True,
                             show_scrollbars=True, show=True)
```