 Here is the Python code that performs the requested pipeline on the SPM Faces dataset using the MNE-Python library:
```python
import os
import mne
from mne.datasets import spm_faces
from mne.minimum_norm import apply_inverse_epochs, make_inverse_operator, read_inverse_operator
from mne.preprocessing import ICA, create_eog_epochs, create_ecg_epochs, corr_pick_channels
from mne.decoding import SlidingEstimator, GeneralizingEstimator
from mne.time_frequency import AverageTFR
from mne.viz import plot_topomap, plot_topography, plot_sensors, plot_alignment
from mne.simulation import simulate_evoked
from mne.forward import (compute_forward_solution,
                         apply_forward_solution,
                         make_forward_solution)

# Set up paths
data_path = spm_faces.data_path()
subjects_dir = spm_faces.subjects_dir()
raw_fname = os.path.join(data_path, 'MEG', 'sample', 'sample_audvis_raw.fif')
event_fname = os.path.join(data_path, 'MEG', 'sample', 'sample_audvis_raw-eve.fif')
trans_fname = os.path.join(subjects_dir, 'sample', 'bem', 'sample-5120-bem-meg.fif')
src_fname = os.path.join(subjects_dir, 'sample', 'bem', 'sample-5120-src.fif')

# Load data and set up projector
raw = mne.io.read_raw_fif(raw_fname, preload=True)
events = mne.read_events(event_fname)
event_id = {'faces': 1, 'scrambled': 2}
picks = mne.pick_types(raw.info, meg=True, eeg=False, stim=False)
raw.filter(l_freq=1, h_freq=40)

# Set up forward solution
info = raw.info
