  Here is the Python code that performs a full pipeline on the SPM Faces dataset using the MNE-Python library:
```
import mne
from mne.datasets import spm_faces

# Load and filter data
raw = mne.io.read_raw_fif(spm_faces.data_path() + '/spm_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, picks=picks, add_channels=add_channels, reject_by_annotation=reject_by_annotation, verbose=None)

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

# Average epochs
epochs_avg = epochs.average()

# Forward model computation
forward = mne.make_forward_model(epochs_avg, epochs_ica, method='dSPM')

# Source reconstruction
stc = mne.compute_source_psd_epochs(epochs_ica, forward, tmin, tmax, n_jobs=1)

# Estimate noise covariance
noise_cov = mne.compute_covariance(epochs_ica, method='shrunk', rank=None, n_jobs=1)

# Visualize fields on MEG helmet
mne.viz.plot_meg_helmet(stc, trans=None, subject='fsaverage', subjects_dir=None, time=None, time_unit='s', show_names=True, show_scrollbars=True, show=True)

# Compute forward and inverse solutions
inverse = mne.minimum_norm.make_inverse_operator(epochs_avg, forward, noise_cov, loose=0.2, depth=0.8)

# Plot the contrast in 3D
mne.viz.plot_source_estimates_3d(stc, trans=None, subject='fsaverage', subjects_dir=None, time=None, time_unit='s', show_names=True, show_scrollbars=True, show=True)
```