diff --git a/examples/preprocessing/interpolate_to.py b/examples/preprocessing/interpolate_to.py
index b97a7251cbb..711b24190a5 100644
--- a/examples/preprocessing/interpolate_to.py
+++ b/examples/preprocessing/interpolate_to.py
@@ -2,21 +2,30 @@
 .. _ex-interpolate-to-any-montage:
 
 ======================================================
-Interpolate EEG data to any montage
+Interpolate MEG or EEG data to any montage
 ======================================================
 
-This example demonstrates how to interpolate EEG channels to match a given montage.
-This can be useful for standardizing
+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 this example, the data from the original EEG channels will be
+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.
 
@@ -30,6 +39,9 @@
 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"
@@ -75,6 +87,63 @@
 )
 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)
+
+plt.show()
+
 # %%
 # References
 # ----------
diff --git a/mne/channels/__init__.pyi b/mne/channels/__init__.pyi
index 470bc0cfd0b..628e092dcf4 100644
--- a/mne/channels/__init__.pyi
+++ b/mne/channels/__init__.pyi
@@ -30,6 +30,7 @@ __all__ = [
     "read_dig_localite",
     "read_dig_polhemus_isotrak",
     "read_layout",
+    "read_meg_montage",
     "read_polhemus_fastscan",
     "read_vectorview_selection",
     "rename_channels",
@@ -75,6 +76,7 @@ from .montage import (
     read_dig_hpts,
     read_dig_localite,
     read_dig_polhemus_isotrak,
+    read_meg_montage,
     read_polhemus_fastscan,
     transform_to_head,
 )
diff --git a/mne/channels/channels.py b/mne/channels/channels.py
index ef98efd1731..8d7c5a25805 100644
--- a/mne/channels/channels.py
+++ b/mne/channels/channels.py
@@ -41,7 +41,7 @@
     pick_info,
     pick_types,
 )
-from .._fiff.proj import _has_eeg_average_ref_proj, setup_proj
+from .._fiff.proj import setup_proj
 from .._fiff.reference import add_reference_channels, set_eeg_reference
 from .._fiff.tag import _rename_list
 from ..bem import _check_origin
@@ -968,161 +968,110 @@ def interpolate_bads(
 
         return self
 
-    def interpolate_to(self, sensors, origin="auto", method="spline", reg=0.0):
-        """Interpolate EEG data onto a new montage.
+    def interpolate_to(
+        self, sensors, origin="auto", method="MNE", mode="accurate", reg=0.0
+    ):
+        """Interpolate data onto a new sensor configuration.
 
-        .. warning::
-            Be careful, only EEG channels are interpolated. Other channel types are
-            not interpolated.
+        This method can interpolate EEG data onto a new montage or transform
+        MEG data to a different sensor configuration (e.g., Neuromag to CTF).
 
         Parameters
         ----------
-        sensors : DigMontage
-            The target montage containing channel positions to interpolate onto.
+        sensors : DigMontage | str
+            For EEG: A DigMontage object containing target channel positions.
+            For MEG: A string specifying the target MEG system. Currently
+            supported: ``'neuromag'``, ``'ctf151'`` or ``'ctf275'``.
         origin : array-like, shape (3,) | str
             Origin of the sphere in the head coordinate frame and in meters.
             Can be ``'auto'`` (default), which means a head-digitization-based
-            origin fit.
+            origin fit. Used for both EEG and MEG interpolation.
         method : str
-            Method to use for EEG channels.
-            Supported methods are 'spline' (default) and 'MNE'.
+            Interpolation method to use.
+            For EEG: ``'MNE'`` (default) or ``'spline'``.
+            For MEG:  ``'MNE'`` (default).
+        mode : str
+            Either ``'accurate'`` (default) or ``'fast'``, determines the
+            quality of the Legendre polynomial expansion used for
+            interpolation of MEG channels using the minimum-norm method.
+            Only used for MEG interpolation.
         reg : float
-            The regularization parameter for the interpolation method
-            (only used when the method is 'spline').
+            The regularization parameter for the interpolation method.
+            Only used when ``method='spline'`` for EEG channels.
 
         Returns
         -------
         inst : instance of Raw, Epochs, or Evoked
-            The instance with updated channel locations and data.
+            A new instance with interpolated data and updated channel
+            information.
 
         Notes
         -----
-        This method is useful for standardizing EEG layouts across datasets.
-        However, some attributes may be lost after interpolation.
+        **For EEG data:**
 
-        .. versionadded:: 1.10.0
-        """
-        from ..epochs import BaseEpochs, EpochsArray
-        from ..evoked import Evoked, EvokedArray
-        from ..forward._field_interpolation import _map_meg_or_eeg_channels
-        from ..io import RawArray
-        from ..io.base import BaseRaw
-        from .interpolation import _make_interpolation_matrix
-        from .montage import DigMontage
+        This method interpolates EEG channels onto a new montage using
+        spherical splines or minimum-norm estimation. Non-EEG channels
+        are preserved without modification.
 
-        # Check that the method option is valid.
-        _check_option("method", method, ["spline", "MNE"])
-        _validate_type(sensors, DigMontage, "sensors")
+        **For MEG data:**
 
-        # Get target positions from the montage
-        ch_pos = sensors.get_positions().get("ch_pos", {})
-        target_ch_names = list(ch_pos.keys())
-        if not target_ch_names:
-            raise ValueError(
-                "The provided sensors configuration has no channel positions."
-            )
+        This method transforms MEG data to a different sensor configuration
+        using field interpolation.
 
-        # Get original channel order
-        orig_names = self.info["ch_names"]
-
-        # Identify EEG channel
-        picks_good_eeg = pick_types(self.info, meg=False, eeg=True, exclude="bads")
-        if len(picks_good_eeg) == 0:
-            raise ValueError("No good EEG channels available for interpolation.")
-        # Also get the full list of EEG channel indices (including bad channels)
-        picks_remove_eeg = pick_types(self.info, meg=False, eeg=True, exclude=[])
-        eeg_names_orig = [orig_names[i] for i in picks_remove_eeg]
-
-        # Identify non-EEG channels in original order
-        non_eeg_names_ordered = [ch for ch in orig_names if ch not in eeg_names_orig]
-
-        # Create destination info for new EEG channels
-        sfreq = self.info["sfreq"]
-        info_interp = create_info(
-            ch_names=target_ch_names,
-            sfreq=sfreq,
-            ch_types=["eeg"] * len(target_ch_names),
-        )
-        info_interp.set_montage(sensors)
-        info_interp["bads"] = [ch for ch in self.info["bads"] if ch in target_ch_names]
-        # Do not assign "projs" directly.
-
-        # Compute the interpolation mapping
-        if method == "spline":
-            origin_val = _check_origin(origin, self.info)
-            pos_from = self.info._get_channel_positions(picks_good_eeg) - origin_val
-            pos_to = np.stack(list(ch_pos.values()), axis=0)
-
-            def _check_pos_sphere(pos):
-                d = np.linalg.norm(pos, axis=-1)
-                d_norm = np.mean(d / np.mean(d))
-                if np.abs(1.0 - d_norm) > 0.1:
-                    warn("Your spherical fit is poor; interpolation may be inaccurate.")
-
-            _check_pos_sphere(pos_from)
-            _check_pos_sphere(pos_to)
-            mapping = _make_interpolation_matrix(pos_from, pos_to, alpha=reg)
+        Common use cases for MEG transformation:
 
-        else:
-            assert method == "MNE"
-            info_eeg = pick_info(self.info, picks_good_eeg)
-            # If the original info has an average EEG reference projector but
-            # the destination info does not,
-            # update info_interp via a temporary RawArray.
-            if _has_eeg_average_ref_proj(self.info) and not _has_eeg_average_ref_proj(
-                info_interp
-            ):
-                # Create dummy data: shape (n_channels, 1)
-                temp_data = np.zeros((len(info_interp["ch_names"]), 1))
-                temp_raw = RawArray(temp_data, info_interp, first_samp=0)
-                # Using the public API, add an average reference projector.
-                temp_raw.set_eeg_reference(
-                    ref_channels="average", projection=True, verbose=False
-                )
-                # Extract the updated info.
-                info_interp = temp_raw.info
-            mapping = _map_meg_or_eeg_channels(
-                info_eeg, info_interp, mode="accurate", origin=origin
-            )
+        - Transform Neuromag data to CTF sensor layout for comparison
+        - Transform CTF data to Neuromag sensor layout
+        - Simulate what data would look like on a different MEG system
 
-        # Interpolate EEG data
-        data_good = self.get_data(picks=picks_good_eeg)
-        data_interp = mapping @ data_good
+        .. warning::
+            MEG field interpolation assumes that the head position relative
+            to the sensors is similar between systems. Large differences in
+            head position may affect interpolation accuracy.
 
-        # Create a new instance for the interpolated EEG channels
-        # TODO: Creating a new instance leads to a loss of information.
-        #       We should consider updating the existing instance in the future
-        #       by 1) drop channels, 2) add channels, 3) re-order channels.
-        if isinstance(self, BaseRaw):
-            inst_interp = RawArray(data_interp, info_interp, first_samp=self.first_samp)
-        elif isinstance(self, BaseEpochs):
-            inst_interp = EpochsArray(data_interp, info_interp)
-        else:
-            assert isinstance(self, Evoked)
-            inst_interp = EvokedArray(data_interp, info_interp)
-
-        # Merge only if non-EEG channels exist
-        if not non_eeg_names_ordered:
-            return inst_interp
-
-        inst_non_eeg = self.copy().pick(non_eeg_names_ordered).load_data()
-        inst_out = inst_non_eeg.add_channels([inst_interp], force_update_info=True)
-
-        # Reorder channels
-        # Insert the entire new EEG block at the position of the first EEG channel.
-        orig_names_arr = np.array(orig_names)
-        mask_eeg = np.isin(orig_names_arr, eeg_names_orig)
-        if mask_eeg.any():
-            first_eeg_index = np.where(mask_eeg)[0][0]
-            pre = orig_names_arr[:first_eeg_index]
-            new_eeg = np.array(info_interp["ch_names"])
-            post = orig_names_arr[first_eeg_index:]
-            post = post[~np.isin(orig_names_arr[first_eeg_index:], eeg_names_orig)]
-            new_order = np.concatenate((pre, new_eeg, post)).tolist()
-        else:
-            new_order = orig_names
-        inst_out.reorder_channels(new_order)
-        return inst_out
+        .. versionadded:: 1.10.0
+        """
+        from .interpolation import _interpolate_to_eeg, _interpolate_to_meg
+        from .montage import DigMontage
+
+        _check_preload(self, "interpolation")
+
+        # Determine if we're doing EEG or MEG interpolation
+        is_meg_interpolation = isinstance(sensors, str)
+        is_eeg_interpolation = isinstance(sensors, DigMontage)
+
+        if is_eeg_interpolation:
+            # Check that the method option is valid.
+            _validate_type(sensors, DigMontage, "sensors")
+
+            method = _handle_default("interpolation_method", method)
+
+            # Filter method to only include 'eeg' and 'meg'
+            supported_ch_types = ["eeg", "meg"]
+            keys_to_delete = [key for key in method if key not in supported_ch_types]
+            for key in keys_to_delete:
+                del method[key]
+
+            # Force MEG to always use MNE method,
+            # otherwise when method = "spline", the _handle_default function
+            # forces all channel types to use that method
+            if "meg" in method:
+                method["meg"] = "MNE"
+            valids = {"eeg": ("spline", "MNE"), "meg": ("MNE")}
+            for key in method:
+                _check_option("method[key]", key, tuple(valids))
+                _check_option(f"method['{key}']", method[key], valids[key])
+            logger.info("Setting channel interpolation method to %s.", method)
+
+            return _interpolate_to_eeg(self, sensors, origin, method, reg)
+
+        elif is_meg_interpolation:
+            # MEG interpolation to canonical sensor configuration
+            _check_option("sensors", sensors, ["neuromag", "ctf151", "ctf275"])
+            _check_option("method", method, ["MNE"])
+            _check_option("mode", mode, ["accurate", "fast"])
+
+            return _interpolate_to_meg(self, sensors, origin, mode)
 
 
 @verbose
diff --git a/mne/channels/data/montages/ctf151.csv b/mne/channels/data/montages/ctf151.csv
new file mode 100644
index 00000000000..40c42b0b2e8
--- /dev/null
+++ b/mne/channels/data/montages/ctf151.csv
@@ -0,0 +1,152 @@
+name,coil_type,x,y,z,ex_x,ex_y,ex_z,ey_x,ey_y,ey_z,ez_x,ez_y,ez_z
+MLC11-606,5001,-0.014323,0.048909,0.083084,0.728954,0.665674,-0.159696,-0.680884,0.680884,-0.269802,-0.070866,0.305408,0.949581
+MLC12-606,5001,-0.040601,0.042758,0.079940,0.708622,0.705572,-0.004790,-0.644792,0.644792,-0.410470,-0.286528,0.293956,0.911862
+MLC13-606,5001,-0.073375,0.034411,0.064147,0.561962,0.806275,0.184715,-0.516439,0.516439,-0.683068,-0.646135,0.288465,0.706610
+MLC14-606,5001,-0.090027,0.012140,0.050707,0.289690,0.900312,0.324835,-0.425104,0.425104,-0.799107,-0.857535,0.093405,0.505875
+MLC15-606,5001,-0.098328,-0.010274,0.030432,0.013822,0.964761,0.262763,-0.257367,0.257367,-0.931410,-0.966215,-0.054752,0.251855
+MLC21-606,5001,-0.027556,0.020578,0.089149,0.713508,0.699800,-0.034442,-0.680665,0.680665,-0.270906,-0.166137,0.216737,0.961989
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+MLF21-606,5001,-0.015126,0.121044,0.010698,0.028849,-0.170200,0.984987,0.985937,0.167115,-0.000000,-0.164606,0.971136,0.172627
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+MLF32-606,5001,-0.061468,0.093424,0.030154,0.751575,0.659104,-0.026780,-0.267996,0.267996,-0.925395,-0.602755,0.702680,0.378057
+MLF33-606,5001,-0.081174,0.072412,0.018828,0.446328,0.892010,0.071482,-0.156415,0.156415,-0.975227,-0.881093,0.424091,0.209336
+MLF34-606,5001,-0.092545,0.048544,0.009927,0.363299,0.931232,0.028655,-0.050327,0.050327,-0.997464,-0.930312,0.360936,0.065150
+MLF41-606,5001,-0.014336,0.095188,0.057546,0.822617,0.461102,-0.332695,-0.560705,0.560705,-0.609278,-0.094395,0.687747,0.719788
+MLF42-606,5001,-0.046064,0.085854,0.054833,0.780887,0.615876,-0.104459,-0.471648,0.471648,-0.745048,-0.409589,0.631067,0.658780
+MLF43-606,5001,-0.073502,0.066873,0.045243,0.547547,0.828097,0.120196,-0.366419,0.366419,-0.855262,-0.752283,0.424254,0.504063
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+MLO12-606,5001,-0.049711,-0.108916,0.011668,0.809457,-0.256524,0.528181,-0.405781,0.405781,0.818952,-0.424407,-0.877232,0.224370
+MLO21-606,5001,-0.033745,-0.119879,-0.015197,0.947630,-0.301709,0.104731,-0.083246,0.083246,0.993046,-0.308330,-0.949759,0.053770
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diff --git a/mne/channels/data/montages/ctf275.csv b/mne/channels/data/montages/ctf275.csv
new file mode 100644
index 00000000000..303f1fc79fb
--- /dev/null
+++ b/mne/channels/data/montages/ctf275.csv
@@ -0,0 +1,275 @@
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diff --git a/mne/channels/data/montages/neuromag306.csv b/mne/channels/data/montages/neuromag306.csv
new file mode 100644
index 00000000000..d1773929e34
--- /dev/null
+++ b/mne/channels/data/montages/neuromag306.csv
@@ -0,0 +1,307 @@
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+MEG 2032,3012,0.016900,-0.097200,0.039700,-0.000300,-0.372910,-0.927926,0.990028,0.130604,-0.052801,0.140881,-0.918688,0.369152
+MEG 2033,3012,0.016900,-0.097200,0.039700,0.990028,0.130604,-0.052801,0.000300,0.372910,0.927926,0.140881,-0.918688,0.369152
+MEG 2031,3024,0.016900,-0.097200,0.039700,-0.000300,-0.372910,-0.927926,0.990028,0.130604,-0.052801,0.140881,-0.918688,0.369152
+MEG 2042,3012,-0.017000,-0.097200,0.039700,-0.000500,-0.373789,-0.927473,0.989971,-0.131196,0.052398,-0.141267,-0.918145,0.370106
+MEG 2043,3012,-0.017000,-0.097200,0.039700,0.989971,-0.131196,0.052398,0.000500,0.373789,0.927473,-0.141267,-0.918145,0.370106
+MEG 2041,3024,-0.017000,-0.097200,0.039700,-0.000500,-0.373789,-0.927473,0.989971,-0.131196,0.052398,-0.141267,-0.918145,0.370106
+MEG 2113,3012,0.000000,-0.108600,0.007000,-0.000100,-0.168403,-0.985718,0.999819,0.020400,-0.003600,0.020715,-0.985540,0.168371
+MEG 2112,3012,0.000000,-0.108600,0.007000,0.999819,0.020400,-0.003600,0.000100,0.168403,0.985718,0.020715,-0.985540,0.168371
+MEG 2111,3024,0.000000,-0.108600,0.007000,-0.000100,-0.168403,-0.985718,0.999819,0.020400,-0.003600,0.020715,-0.985540,0.168371
+MEG 2122,3012,0.000000,-0.110600,-0.027500,0.000000,0.040700,-0.999200,1.000000,-0.000400,0.000000,-0.000400,-0.999200,-0.040700
+MEG 2123,3012,0.000000,-0.110600,-0.027500,1.000000,-0.000400,0.000000,0.000000,-0.040700,0.999200,-0.000400,-0.999200,-0.040700
+MEG 2121,3024,0.000000,-0.110600,-0.027500,0.000000,0.040700,-0.999200,1.000000,-0.000400,0.000000,-0.000400,-0.999200,-0.040700
+MEG 2133,3012,0.017000,-0.109800,-0.061800,0.009200,-0.010800,-0.999899,0.985499,0.169500,0.007300,0.169404,-0.985467,0.012203
+MEG 2132,3012,0.017000,-0.109800,-0.061800,0.985499,0.169500,0.007300,-0.009200,0.010800,0.999899,0.169404,-0.985467,0.012203
+MEG 2131,3024,0.017000,-0.109800,-0.061800,0.009200,-0.010800,-0.999899,0.985499,0.169500,0.007300,0.169404,-0.985467,0.012203
+MEG 2143,3012,-0.017100,-0.109800,-0.061900,-0.006200,-0.012200,-0.999906,0.982706,-0.185001,-0.003800,-0.184937,-0.982638,0.013136
+MEG 2142,3012,-0.017100,-0.109800,-0.061900,0.982706,-0.185001,-0.003800,0.006200,0.012200,0.999906,-0.184937,-0.982638,0.013136
+MEG 2141,3024,-0.017100,-0.109800,-0.061900,-0.006200,-0.012200,-0.999906,0.982706,-0.185001,-0.003800,-0.184937,-0.982638,0.013136
+MEG 2212,3012,0.051700,-0.027600,0.092800,0.859962,0.026999,-0.509577,0.107595,0.966557,0.232790,0.498820,-0.255018,0.828297
+MEG 2213,3012,0.051700,-0.027600,0.092800,0.107595,0.966557,0.232790,-0.859962,-0.026999,0.509577,0.498820,-0.255018,0.828297
+MEG 2211,3024,0.051700,-0.027600,0.092800,0.859962,0.026999,-0.509577,0.107595,0.966557,0.232790,0.498820,-0.255018,0.828297
+MEG 2223,3012,0.078600,-0.028400,0.069800,0.627473,-0.042698,-0.777467,0.223290,0.966359,0.127195,0.745881,-0.253412,0.615898
+MEG 2222,3012,0.078600,-0.028400,0.069800,0.223290,0.966359,0.127195,-0.627473,0.042698,0.777467,0.745881,-0.253412,0.615898
+MEG 2221,3024,0.078600,-0.028400,0.069800,0.627473,-0.042698,-0.777467,0.223290,0.966359,0.127195,0.745881,-0.253412,0.615898
+MEG 2233,3012,0.055300,-0.062800,0.070600,0.377694,-0.493292,-0.783588,0.759388,0.649190,-0.042699,0.529761,-0.578920,0.619796
+MEG 2232,3012,0.055300,-0.062800,0.070600,0.759388,0.649190,-0.042699,-0.377694,0.493292,0.783588,0.529761,-0.578920,0.619796
+MEG 2231,3024,0.055300,-0.062800,0.070600,0.377694,-0.493292,-0.783588,0.759388,0.649190,-0.042699,0.529761,-0.578920,0.619796
+MEG 2242,3012,0.018200,-0.054200,0.092200,-0.069799,-0.841688,-0.535392,0.989086,0.011400,-0.146898,0.129746,-0.539803,0.831706
+MEG 2243,3012,0.018200,-0.054200,0.092200,0.989086,0.011400,-0.146898,0.069799,0.841688,0.535392,0.129746,-0.539803,0.831706
+MEG 2241,3024,0.018200,-0.054200,0.092200,-0.069799,-0.841688,-0.535392,0.989086,0.011400,-0.146898,0.129746,-0.539803,0.831706
+MEG 2312,3012,0.051300,-0.086100,0.039700,0.153007,-0.302213,-0.940940,0.880738,0.473520,-0.008900,0.448244,-0.827360,0.338622
+MEG 2313,3012,0.051300,-0.086100,0.039700,0.880738,0.473520,-0.008900,-0.153007,0.302213,0.940940,0.448244,-0.827360,0.338622
+MEG 2311,3024,0.051300,-0.086100,0.039700,0.153007,-0.302213,-0.940940,0.880738,0.473520,-0.008900,0.448244,-0.827360,0.338622
+MEG 2323,3012,0.063700,-0.088400,0.006200,0.056398,-0.071297,-0.995860,0.798667,0.601776,0.002200,0.599127,-0.795485,0.090881
+MEG 2322,3012,0.063700,-0.088400,0.006200,0.798667,0.601776,0.002200,-0.056398,0.071297,0.995860,0.599127,-0.795485,0.090881
+MEG 2321,3024,0.063700,-0.088400,0.006200,0.056398,-0.071297,-0.995860,0.798667,0.601776,0.002200,0.599127,-0.795485,0.090881
+MEG 2332,3012,0.033000,-0.105100,-0.027700,-0.004900,0.023300,-0.999715,0.951414,0.307905,0.002500,0.307875,-0.951130,-0.023677
+MEG 2333,3012,0.033000,-0.105100,-0.027700,0.951414,0.307905,0.002500,0.004900,-0.023300,0.999715,0.307875,-0.951130,-0.023677
+MEG 2331,3024,0.033000,-0.105100,-0.027700,-0.004900,0.023300,-0.999715,0.951414,0.307905,0.002500,0.307875,-0.951130,-0.023677
+MEG 2343,3012,0.033300,-0.103400,0.006300,0.041500,-0.123300,-0.991501,0.948001,0.318300,0.000100,0.315583,-0.939949,0.130098
+MEG 2342,3012,0.033300,-0.103400,0.006300,0.948001,0.318300,0.000100,-0.041500,0.123300,0.991501,0.315583,-0.939949,0.130098
+MEG 2341,3024,0.033300,-0.103400,0.006300,0.041500,-0.123300,-0.991501,0.948001,0.318300,0.000100,0.315583,-0.939949,0.130098
+MEG 2412,3012,0.095200,-0.030600,0.039200,0.323999,-0.063600,-0.943897,0.309099,0.950098,0.042100,0.894117,-0.305398,0.327489
+MEG 2413,3012,0.095200,-0.030600,0.039200,0.309099,0.950098,0.042100,-0.323999,0.063600,0.943897,0.894117,-0.305398,0.327489
+MEG 2411,3024,0.095200,-0.030600,0.039200,0.323999,-0.063600,-0.943897,0.309099,0.950098,0.042100,0.894117,-0.305398,0.327489
+MEG 2423,3012,0.101700,-0.033800,0.005800,0.066101,-0.028300,-0.997412,0.314004,0.949411,-0.006100,0.947126,-0.312788,0.071643
+MEG 2422,3012,0.101700,-0.033800,0.005800,0.314004,0.949411,-0.006100,-0.066101,0.028300,0.997412,0.947126,-0.312788,0.071643
+MEG 2421,3024,0.101700,-0.033800,0.005800,0.066101,-0.028300,-0.997412,0.314004,0.949411,-0.006100,0.947126,-0.312788,0.071643
+MEG 2433,3012,0.086600,-0.063900,0.005600,0.060099,-0.044799,-0.997187,0.549293,0.835589,-0.004400,0.833435,-0.547483,0.074826
+MEG 2432,3012,0.086600,-0.063900,0.005600,0.549293,0.835589,-0.004400,-0.060099,0.044799,0.997187,0.833435,-0.547483,0.074826
+MEG 2431,3024,0.086600,-0.063900,0.005600,0.060099,-0.044799,-0.997187,0.549293,0.835589,-0.004400,0.833435,-0.547483,0.074826
+MEG 2442,3012,0.078100,-0.062900,0.039400,0.259507,-0.203505,-0.944125,0.598716,0.800921,-0.008000,0.757798,-0.563187,0.329687
+MEG 2443,3012,0.078100,-0.062900,0.039400,0.598716,0.800921,-0.008000,-0.259507,0.203505,0.944125,0.757798,-0.563187,0.329687
+MEG 2441,3024,0.078100,-0.062900,0.039400,0.259507,-0.203505,-0.944125,0.598716,0.800921,-0.008000,0.757798,-0.563187,0.329687
+MEG 2512,3012,0.063000,-0.090600,-0.027600,-0.013600,0.025200,-0.999598,0.808198,0.588899,0.003800,0.588757,-0.807821,-0.028376
+MEG 2513,3012,0.063000,-0.090600,-0.027600,0.808198,0.588899,0.003800,0.013600,-0.025200,0.999598,0.588757,-0.807821,-0.028376
+MEG 2511,3024,0.063000,-0.090600,-0.027600,-0.013600,0.025200,-0.999598,0.808198,0.588899,0.003800,0.588757,-0.807821,-0.028376
+MEG 2522,3012,0.086100,-0.066100,-0.028000,-0.021299,0.011700,-0.999673,0.571385,0.820678,-0.002600,0.820380,-0.571254,-0.024165
+MEG 2523,3012,0.086100,-0.066100,-0.028000,0.571385,0.820678,-0.002600,0.021299,-0.011700,0.999673,0.820380,-0.571254,-0.024165
+MEG 2521,3024,0.086100,-0.066100,-0.028000,-0.021299,0.011700,-0.999673,0.571385,0.820678,-0.002600,0.820380,-0.571254,-0.024165
+MEG 2533,3012,0.075700,-0.079800,-0.062000,0.014300,-0.000900,-0.999897,0.690798,0.722998,0.009300,0.722916,-0.690860,0.010961
+MEG 2532,3012,0.075700,-0.079800,-0.062000,0.690798,0.722998,0.009300,-0.014300,0.000900,0.999897,0.722916,-0.690860,0.010961
+MEG 2531,3024,0.075700,-0.079800,-0.062000,0.014300,-0.000900,-0.999897,0.690798,0.722998,0.009300,0.722916,-0.690860,0.010961
+MEG 2543,3012,0.048800,-0.099400,-0.062100,0.011000,-0.007000,-0.999915,0.903014,0.429706,0.007000,0.429621,-0.903014,0.011048
+MEG 2542,3012,0.048800,-0.099400,-0.062100,0.903014,0.429706,0.007000,-0.011000,0.007000,0.999915,0.429621,-0.903014,0.011048
+MEG 2541,3024,0.048800,-0.099400,-0.062100,0.011000,-0.007000,-0.999915,0.903014,0.429706,0.007000,0.429621,-0.903014,0.011048
+MEG 2612,3012,0.108700,-0.003300,-0.028000,-0.020500,0.003400,-0.999813,0.081001,0.996713,0.001700,0.996532,-0.080951,-0.020708
+MEG 2613,3012,0.108700,-0.003300,-0.028000,0.081001,0.996713,0.001700,0.020500,-0.003400,0.999813,0.996532,-0.080951,-0.020708
+MEG 2611,3024,0.108700,-0.003300,-0.028000,-0.020500,0.003400,-0.999813,0.081001,0.996713,0.001700,0.996532,-0.080951,-0.020708
+MEG 2623,3012,0.106800,-0.020600,-0.062100,0.013100,0.004500,-0.999904,0.215701,0.976404,0.007200,0.976343,-0.215775,0.011820
+MEG 2622,3012,0.106800,-0.020600,-0.062100,0.215701,0.976404,0.007200,-0.013100,-0.004500,0.999904,0.976343,-0.215775,0.011820
+MEG 2621,3024,0.106800,-0.020600,-0.062100,0.013100,0.004500,-0.999904,0.215701,0.976404,0.007200,0.976343,-0.215775,0.011820
+MEG 2633,3012,0.095100,-0.052400,-0.062000,0.015200,0.003300,-0.999879,0.476590,0.879082,0.010100,0.879009,-0.476686,0.011789
+MEG 2632,3012,0.095100,-0.052400,-0.062000,0.476590,0.879082,0.010100,-0.015200,-0.003300,0.999879,0.879009,-0.476686,0.011789
+MEG 2631,3024,0.095100,-0.052400,-0.062000,0.015200,0.003300,-0.999879,0.476590,0.879082,0.010100,0.879009,-0.476686,0.011789
+MEG 2642,3012,0.101700,-0.036100,-0.027800,-0.027699,0.005300,-0.999573,0.341491,0.939874,-0.004500,0.939449,-0.341469,-0.027844
+MEG 2643,3012,0.101700,-0.036100,-0.027800,0.341491,0.939874,-0.004500,0.027699,-0.005300,0.999573,0.939449,-0.341469,-0.027844
+MEG 2641,3024,0.101700,-0.036100,-0.027800,-0.027699,0.005300,-0.999573,0.341491,0.939874,-0.004500,0.939449,-0.341469,-0.027844
diff --git a/mne/channels/interpolation.py b/mne/channels/interpolation.py
index c0a70a7d6ed..896e47f3846 100644
--- a/mne/channels/interpolation.py
+++ b/mne/channels/interpolation.py
@@ -415,3 +415,207 @@ def _interpolate_bads_seeg(
             out = out.reshape(bads_shaft.size, inst._data.shape[0], -1)
             out = out.swapaxes(0, 1)
         inst._data[..., bads_shaft, :] = out
+
+
+def _interpolate_to_eeg(inst, sensors, origin, method, reg):
+    """Interpolate EEG data to a new montage.
+
+    Parameters
+    ----------
+    inst : instance of Raw, Epochs, or Evoked
+        The instance to interpolate.
+    sensors : DigMontage
+        Target montage with channel positions.
+    origin : array-like, shape (3,) | str
+        Origin of the sphere in head coordinate frame.
+    method : dict
+        Dictionary with 'eeg' key specifying interpolation method.
+    reg : float
+        Regularization parameter for spline interpolation.
+
+    Returns
+    -------
+    inst : instance of Raw, Epochs, or Evoked
+        A new instance with interpolated data.
+    """
+    from .._fiff.meas_info import create_info
+    from .._fiff.proj import _has_eeg_average_ref_proj
+    from ..bem import _check_origin
+    from ..epochs import BaseEpochs, EpochsArray
+    from ..evoked import Evoked, EvokedArray
+    from ..forward._field_interpolation import _map_meg_or_eeg_channels
+    from ..io import RawArray
+    from ..io.base import BaseRaw
+    from ..utils import warn
+
+    # Get target positions from the montage
+    ch_pos = sensors.get_positions().get("ch_pos", {})
+    target_ch_names = list(ch_pos.keys())
+    if not target_ch_names:
+        raise ValueError("The provided sensors configuration has no channel positions.")
+
+    # Get original channel order
+    orig_names = inst.info["ch_names"]
+
+    # Identify EEG channel
+    picks_good_eeg = pick_types(inst.info, meg=False, eeg=True, exclude="bads")
+    if len(picks_good_eeg) == 0:
+        raise ValueError("No good EEG channels available for interpolation.")
+    # Also get the full list of EEG channel indices (including bad channels)
+    picks_remove_eeg = pick_types(inst.info, meg=False, eeg=True, exclude=[])
+    eeg_names_orig = [orig_names[i] for i in picks_remove_eeg]
+
+    # Identify non-EEG channels in original order
+    non_eeg_names_ordered = [ch for ch in orig_names if ch not in eeg_names_orig]
+
+    # Create destination info for new EEG channels
+    sfreq = inst.info["sfreq"]
+    info_interp = create_info(
+        ch_names=target_ch_names,
+        sfreq=sfreq,
+        ch_types=["eeg"] * len(target_ch_names),
+    )
+    info_interp.set_montage(sensors)
+    info_interp["bads"] = [ch for ch in inst.info["bads"] if ch in target_ch_names]
+
+    # Compute the interpolation mapping
+    if method["eeg"] == "spline":
+        origin_val = _check_origin(origin, inst.info)
+        pos_from = inst.info._get_channel_positions(picks_good_eeg) - origin_val
+        pos_to = np.stack(list(ch_pos.values()), axis=0)
+
+        def _check_pos_sphere(pos):
+            d = np.linalg.norm(pos, axis=-1)
+            d_norm = np.mean(d / np.mean(d))
+            if np.abs(1.0 - d_norm) > 0.1:
+                warn("Your spherical fit is poor; interpolation may be inaccurate.")
+
+        _check_pos_sphere(pos_from)
+        _check_pos_sphere(pos_to)
+        mapping = _make_interpolation_matrix(pos_from, pos_to, alpha=reg)
+
+    else:
+        assert method["eeg"] == "MNE"
+        info_eeg = pick_info(inst.info, picks_good_eeg)
+        # If the original info has an average EEG reference projector but
+        # the destination info does not, update info_interp via a temporary RawArray.
+        if _has_eeg_average_ref_proj(inst.info) and not _has_eeg_average_ref_proj(
+            info_interp
+        ):
+            # Create dummy data: shape (n_channels, 1)
+            temp_data = np.zeros((len(info_interp["ch_names"]), 1))
+            temp_raw = RawArray(temp_data, info_interp, first_samp=0)
+            # Using the public API, add an average reference projector.
+            temp_raw.set_eeg_reference(
+                ref_channels="average", projection=True, verbose=False
+            )
+            # Extract the updated info.
+            info_interp = temp_raw.info
+        mapping = _map_meg_or_eeg_channels(
+            info_eeg, info_interp, mode="accurate", origin=origin
+        )
+
+    # Interpolate EEG data
+    data_good = inst.get_data(picks=picks_good_eeg)
+    data_interp = mapping @ data_good
+
+    # Create a new instance for the interpolated EEG channels
+    if isinstance(inst, BaseRaw):
+        inst_interp = RawArray(data_interp, info_interp, first_samp=inst.first_samp)
+    elif isinstance(inst, BaseEpochs):
+        inst_interp = EpochsArray(data_interp, info_interp)
+    else:
+        assert isinstance(inst, Evoked)
+        inst_interp = EvokedArray(data_interp, info_interp)
+
+    # Merge only if non-EEG channels exist
+    if not non_eeg_names_ordered:
+        return inst_interp
+
+    inst_non_eeg = inst.copy().pick(non_eeg_names_ordered).load_data()
+    inst_out = inst_non_eeg.add_channels([inst_interp], force_update_info=True)
+
+    # Reorder channels
+    # Insert the entire new EEG block at the position of the first EEG channel.
+    orig_names_arr = np.array(orig_names)
+    mask_eeg = np.isin(orig_names_arr, eeg_names_orig)
+    if mask_eeg.any():
+        first_eeg_index = np.where(mask_eeg)[0][0]
+        pre = orig_names_arr[:first_eeg_index]
+        new_eeg = np.array(info_interp["ch_names"])
+        post = orig_names_arr[first_eeg_index:]
+        post = post[~np.isin(orig_names_arr[first_eeg_index:], eeg_names_orig)]
+        new_order = np.concatenate((pre, new_eeg, post)).tolist()
+    else:
+        new_order = orig_names
+    inst_out.reorder_channels(new_order)
+    return inst_out
+
+
+def _interpolate_to_meg(inst, sensors, origin, mode):
+    """Interpolate MEG data to a canonical sensor configuration.
+
+    Parameters
+    ----------
+    inst : instance of Raw, Epochs, or Evoked
+        The instance to interpolate.
+    sensors : str
+        Target MEG system name ('neuromag', 'ctf151', or 'ctf275').
+    origin : array-like, shape (3,) | str
+        Origin of the sphere in head coordinate frame.
+    mode : str
+        Either 'accurate' or 'fast'.
+
+    Returns
+    -------
+    inst : instance of Raw, Epochs, or Evoked
+        A new instance with interpolated data.
+    """
+    from ..bem import _check_origin
+    from ..epochs import BaseEpochs, EpochsArray
+    from ..evoked import Evoked, EvokedArray
+    from ..forward._field_interpolation import _map_meg_or_eeg_channels
+    from ..io import RawArray
+    from ..io.base import BaseRaw
+    from .montage import read_meg_montage
+
+    # Get MEG channels from source
+    picks_meg = pick_types(inst.info, meg=True, ref_meg=False, exclude="bads")
+    if len(picks_meg) == 0:
+        raise ValueError("No good MEG channels available for interpolation.")
+
+    # Load target sensor configuration
+    info_to = read_meg_montage(sensors)
+
+    # Update dev_head_t from source data
+    if inst.info["dev_head_t"] is not None:
+        info_to["dev_head_t"] = inst.info["dev_head_t"].copy()
+
+    # Get source MEG info
+    info_from = pick_info(inst.info, picks_meg)
+
+    # Compute field interpolation mapping
+    origin_val = _check_origin(origin, inst.info)
+    mapping = _map_meg_or_eeg_channels(info_from, info_to, mode=mode, origin=origin_val)
+
+    # Apply mapping to MEG data
+    data_meg = inst.get_data(picks=picks_meg)
+    data_interp = mapping @ data_meg
+
+    # Create new instance with interpolated MEG data
+    if isinstance(inst, BaseRaw):
+        inst_out = RawArray(data_interp, info_to, first_samp=inst.first_samp)
+    elif isinstance(inst, BaseEpochs):
+        inst_out = EpochsArray(data_interp, info_to)
+    else:
+        assert isinstance(inst, Evoked)
+        inst_out = EvokedArray(data_interp, info_to)
+
+    # Add non-MEG channels if they exist
+    non_meg_picks = pick_types(inst.info, meg=False, exclude=[], ref_meg=False)
+    if len(non_meg_picks) > 0:
+        non_meg_names = [inst.info["ch_names"][i] for i in non_meg_picks]
+        inst_non_meg = inst.copy().pick(non_meg_names).load_data()
+        inst_out = inst_out.add_channels([inst_non_meg], force_update_info=True)
+
+    return inst_out
diff --git a/mne/channels/montage.py b/mne/channels/montage.py
index 35fdbce917c..7881fb49346 100644
--- a/mne/channels/montage.py
+++ b/mne/channels/montage.py
@@ -7,6 +7,7 @@
 from collections import OrderedDict
 from copy import deepcopy
 from dataclasses import dataclass
+from pathlib import Path
 
 import numpy as np
 
@@ -1737,6 +1738,124 @@ def read_custom_montage(
     return montage
 
 
+@verbose
+def read_meg_montage(system, *, verbose=None):
+    """Load canonical MEG sensor definitions from CSV files.
+
+    Parameters
+    ----------
+    system : str
+        The MEG system name. Currently supported: 'neuromag', 'ctf151' or
+        'ctf275'.
+    %(verbose)s
+
+    Returns
+    -------
+    info : Info
+        An Info object containing the canonical sensor definitions.
+
+    Notes
+    -----
+    This function loads pre-defined canonical sensor positions and
+    orientations from CSV files stored in mne/channels/data/montages/.
+
+    .. versionadded:: 1.11
+    """
+    # Validate system input
+    _check_option("system", system, ["neuromag", "ctf151", "ctf275"])
+
+    # Map system names to CSV filenames
+    system_files = {
+        "neuromag": "neuromag306.csv",
+        "ctf151": "ctf151.csv",
+        "ctf275": "ctf275.csv",
+    }
+
+    # Load the CSV file
+    montage_dir = Path(__file__).parent / "data" / "montages"
+    csv_file = montage_dir / system_files[system]
+
+    if not csv_file.exists():
+        raise FileNotFoundError(f"Canonical sensor file not found: {csv_file}. ")
+
+    # Read sensor definitions manually
+    ch_names = []
+    rows = []
+    with open(csv_file) as fid:
+        # Read header to get column names
+        header = fid.readline().strip().split(",")
+        # Create a mapping from column name to index
+        col_idx = {name: i for i, name in enumerate(header)}
+
+        # Read data rows
+        for line in fid:
+            values = line.strip().split(",")
+            if not values[0]:  # skip empty lines
+                continue
+            rows.append(values)
+            ch_names.append(values[col_idx["name"]])
+
+    # Create base info structure
+    # Use a dummy sample rate; it won't matter for field interpolation
+    info = create_info(ch_names=ch_names, sfreq=1000.0, ch_types="mag")
+
+    # Update each channel with full coil information
+    for idx, row in enumerate(rows):
+        ch = info["chs"][idx]
+
+        # Set coil type
+        ch["coil_type"] = int(row[col_idx["coil_type"]])
+
+        # Set position and orientation in loc field
+        # loc[0:3] = position
+        ch["loc"][:3] = [
+            float(row[col_idx["x"]]),
+            float(row[col_idx["y"]]),
+            float(row[col_idx["z"]]),
+        ]
+
+        # loc[3:6] = ex (first orientation vector)
+        ch["loc"][3:6] = [
+            float(row[col_idx["ex_x"]]),
+            float(row[col_idx["ex_y"]]),
+            float(row[col_idx["ex_z"]]),
+        ]
+
+        # loc[6:9] = ey (second orientation vector)
+        ch["loc"][6:9] = [
+            float(row[col_idx["ey_x"]]),
+            float(row[col_idx["ey_y"]]),
+            float(row[col_idx["ey_z"]]),
+        ]
+
+        # loc[9:12] = ez (third orientation vector / normal)
+        ch["loc"][9:12] = [
+            float(row[col_idx["ez_x"]]),
+            float(row[col_idx["ez_y"]]),
+            float(row[col_idx["ez_z"]]),
+        ]
+
+        # Set coordinate frame to device coordinates
+        ch["coord_frame"] = FIFF.FIFFV_COORD_DEVICE
+
+        # Set channel kind to MEG
+        ch["kind"] = FIFF.FIFFV_MEG_CH
+
+        # Set unit based on coil type
+        # Neuromag gradiometers (3012) use T/m, magnetometers (3024) use T
+        # CTF gradiometers (5001) use T
+        if ch["coil_type"] == 3012:  # Neuromag planar gradiometer
+            ch["unit"] = FIFF.FIFF_UNIT_T_M
+        else:  # Magnetometers and CTF gradiometers
+            ch["unit"] = FIFF.FIFF_UNIT_T
+
+    # Set device to head transform to identity (canonical positions)
+    # This will be updated based on the actual data being interpolated
+    info["dev_head_t"] = Transform("meg", "head", np.eye(4))
+
+    return info
+
+
 def compute_dev_head_t(montage):
     """Compute device to head transform from a DigMontage.
 
diff --git a/mne/channels/tests/test_montage.py b/mne/channels/tests/test_montage.py
index 0294cbe9d16..1dff6d0d990 100644
--- a/mne/channels/tests/test_montage.py
+++ b/mne/channels/tests/test_montage.py
@@ -1989,7 +1989,7 @@ def test_read_dig_hpts():
 
 def test_get_builtin_montages():
     """Test help function to obtain builtin montages."""
-    EXPECTED_COUNT = 28
+    EXPECTED_COUNT = 31
 
     montages = get_builtin_montages()
     assert len(montages) == EXPECTED_COUNT