"""

.. _ex-interpolate-to-any-montage:

======================================================

Interpolate MEG or EEG data to any montage

======================================================

This example demonstrates both EEG montage interpolation and MEG system

transformation.

For EEG, this can be useful for standardizing

EEG channel layouts across different datasets (see :footcite:`MellotEtAl2024`).

- Using the field interpolation for EEG data.

- Using the target montage "biosemi16".

- Using the MNE interpolation for MEG data to transform from Neuromag

(planar gradiometers and magnetometers) to CTF (axial gradiometers).

In the first example, the data from the original EEG channels will be

interpolated onto the positions defined by the "biosemi16" montage.

In the second example, we will interpolate MEG data from a 306-sensor Neuromag

to 275-sensor CTF system.

"""

# Authors: Antoine Collas <contact@antoinecollas.fr>

# Konstantinos Tsilimparis <konstantinos.tsilimparis@outlook.com>

# License: BSD-3-Clause

# Copyright the MNE-Python contributors.

import matplotlib.pyplot as plt

import mne

from mne.channels import make_standard_montage

from mne.datasets import sample

print(__doc__)

ylim = (-10, 10)

# %%

# Part 1: EEG System Transformation

# ==================================

# Load EEG data

data_path = sample.data_path()

eeg_file_path = data_path / "MEG" / "sample" / "sample_audvis-ave.fif"

evoked = mne.read_evokeds(eeg_file_path, condition="Left Auditory", baseline=(None, 0))

# Select only EEG channels

evoked.pick("eeg")

# Plot the original EEG layout

evoked.plot(exclude=[], picks="eeg", ylim=dict(eeg=ylim))

# %%

# Define the target montage

standard_montage = make_standard_montage("biosemi16")

# %%

# Use interpolate_to to project EEG data to the standard montage

evoked_interpolated_spline = evoked.copy().interpolate_to(

standard_montage, method="spline"

)

# Plot the interpolated EEG layout

evoked_interpolated_spline.plot(exclude=[], picks="eeg", ylim=dict(eeg=ylim))

# %%

# Use interpolate_to to project EEG data to the standard montage

evoked_interpolated_mne = evoked.copy().interpolate_to(standard_montage, method="MNE")

# Plot the interpolated EEG layout

evoked_interpolated_mne.plot(exclude=[], picks="eeg", ylim=dict(eeg=ylim))

# %%

# Comparing before and after interpolation

fig, axs = plt.subplots(3, 1, figsize=(8, 6), constrained_layout=True)

evoked.plot(exclude=[], picks="eeg", axes=axs[0], show=False, ylim=dict(eeg=ylim))

axs[0].set_title("Original EEG Layout")

evoked_interpolated_spline.plot(

exclude=[], picks="eeg", axes=axs[1], show=False, ylim=dict(eeg=ylim)

)

axs[1].set_title("Interpolated to Standard 1020 Montage using spline interpolation")

evoked_interpolated_mne.plot(

exclude=[], picks="eeg", axes=axs[2], show=False, ylim=dict(eeg=ylim)

)

axs[2].set_title("Interpolated to Standard 1020 Montage using MNE interpolation")

# %%

# Part 2: MEG System Transformation

# ==================================

# We demonstrate transforming MEG data from Neuromag (planar gradiometers

# and magnetometers) to CTF (axial gradiometers) sensor configuration.

# Load the full evoked data with MEG channels

evoked_meg = mne.read_evokeds(

eeg_file_path, condition="Left Auditory", baseline=(None, 0)

)

evoked_meg.pick("meg")

print("Original Neuromag system:")

print(f" Number of magnetometers: {len(mne.pick_types(evoked_meg.info, meg='mag'))}")

print(f" Number of gradiometers: {len(mne.pick_types(evoked_meg.info, meg='grad'))}")

# %%

# Transform to CTF sensor configuration

# ======================================

# Interpolate Neuromag to CTF

evoked_ctf = evoked_meg.copy().interpolate_to("ctf275", mode="accurate")

print("\nTransformed to CTF system:")

print(f" Number of MEG channels: {len(mne.pick_types(evoked_ctf.info, meg=True))}")

print(f" Bad channels in original: {evoked_meg.info['bads']}")

# %%

# Compare evoked responses: Original Neuromag vs Transformed CTF

# The data should be similar but projected onto different sensor arrays

# Set consistent y-limits for comparison

ylim_meg = dict(grad=[-300, 300], mag=[-600, 600], meg=[-300, 300])

fig, axes = plt.subplots(3, 1, figsize=(10, 8), layout="constrained")

# Plot original Neuromag gradiometers

evoked_meg.copy().pick("grad").plot(

axes=axes[0], show=False, spatial_colors=True, ylim=ylim_meg, time_unit="s"

)

axes[0].set_title("Original Neuromag Planar Gradiometers", fontsize=14)

# Plot original Neuromag magnetometers

evoked_meg.copy().pick("mag").plot(

axes=axes[1], show=False, spatial_colors=True, ylim=ylim_meg, time_unit="s"

)

axes[1].set_title("Original Neuromag Magnetometers", fontsize=14)

# Plot transformed CTF gradiometers

evoked_ctf.plot(

axes=axes[2], show=False, spatial_colors=True, ylim=ylim_meg, time_unit="s"

)

axes[2].set_title("Transformed to CTF275 Axial Gradiometers", fontsize=14)

# %%

# References

# ----------

# .. footbibliography::