Note

Go to the end to download the full example code

Importing data from MEG devices#

This section describes how to read data for various MEG manufacturers.

MEGIN/Elekta Neuromag VectorView and TRIUX (.fif)#

Neuromag Raw FIF files can be loaded using mne.io.read_raw_fif().

If the data were recorded with MaxShield on and have not been processed with MaxFilter, they may need to be loaded with mne.io.read_raw_fif(..., allow_maxshield=True).

FIL OPM (.bin)#

MEG data from the OPM system used by the FIL at UCL can be read with mne.io.read_raw_fil(). For related OPM processing methods, see Preprocessing optically pumped magnetometer (OPM) MEG data.

Artemis123 (.bin)#

MEG data from the Artemis123 system can be read with mne.io.read_raw_artemis123().

4-D Neuroimaging / BTI data (dir)#

MNE-Python provides mne.io.read_raw_bti() to read and convert 4D / BTI data. This reader function will by default replace the original channel names, typically composed of the letter A and the channel number with Neuromag. To import the data, the following input files are mandatory:

A data file (typically c,rfDC) containing the recorded MEG time series.
A hs_file containing the digitizer data.
A config file containing acquisition information and metadata.

By default mne.io.read_raw_bti() assumes that these three files are located in the same folder.

Note

While reading the reference or compensation channels, the compensation weights are currently not processed. As a result, the mne.io.Raw object and the corresponding fif file does not include information about the compensation channels and the weights to be applied to realize software gradient compensation. If the data are saved in the Magnes system are already compensated, there will be a small error in the forward calculations, whose significance has not been evaluated carefully at this time.

CTF data (dir)#

The function mne.io.read_raw_ctf() can be used to read CTF data.

CTF Polhemus data#

The function mne.channels.read_dig_polhemus_isotrak() can be used to read Polhemus data.

Applying software gradient compensation#

Since the software gradient compensation employed in CTF systems is a reversible operation, it is possible to change the compensation status of CTF data in the data files as desired. This section contains information about the technical details of the compensation procedure and a description of mne.io.Raw.apply_gradient_compensation().

The raw instances returned by mne.io.read_raw_ctf() contain several compensation matrices which are employed to suppress external disturbances with help of the reference channel data. The reference sensors are located further away from the brain than the helmet sensors and are thus measuring mainly the external disturbances rather than magnetic fields originating in the brain. Most often, a compensation matrix corresponding to a scheme nicknamed Third-order gradient compensation is employed.

Let us assume that the data contain \(n_1\) MEG sensor channels, \(n_2\) reference sensor channels, and \(n_3\) other channels. The data from all channels can be concatenated into a single vector

\[x = [x_1^T x_2^T x_3^T]^T\ ,\]

where \(x_1\), \(x_2\), and \(x_3\) are the data vectors corresponding to the MEG sensor channels, reference sensor channels, and other channels, respectively. The data before and after compensation, denoted here by \(x_{(0)}\) and \(x_{(k)}\), respectively, are related by

\[x_{(k)} = M_{(k)} x_{(0)}\ ,\]

where the composite compensation matrix is

\[\begin{split}M_{(k)} = \begin{bmatrix} I_{n_1} & C_{(k)} & 0 \\ 0 & I_{n_2} & 0 \\ 0 & 0 & I_{n_3} \end{bmatrix}\ .\end{split}\]

In the above, \(C_{(k)}\) is a \(n_1\) by \(n_2\) compensation data matrix corresponding to compensation “grade” \(k\). It is easy to see that

\[\begin{split}M_{(k)}^{-1} = \begin{bmatrix} I_{n_1} & -C_{(k)} & 0 \\ 0 & I_{n_2} & 0 \\ 0 & 0 & I_{n_3} \end{bmatrix}\ .\end{split}\]

To convert from compensation grade \(k\) to \(p\) one can simply multiply the inverse of one compensate compensation matrix by another and apply the product to the data:

\[x_{(k)} = M_{(k)} M_{(p)}^{-1} x_{(p)}\ .\]

This operation is performed by mne.io.Raw.apply_gradient_compensation().

Ricoh/KIT MEG system data (.con/.sqd)#

MNE-Python includes the mne.io.read_raw_kit() and mne.read_epochs_kit() to read and convert Ricoh/KIT MEG data.

Channel naming

In MNE 0.21 This reader function will by default replace the original channel names, which typically with index starting with zero, with ones with an index starting with one. In 0.22 it will use native names when possible. Use the standardize_names argument to control this behavior.

To import continuous data, only the input .sqd or .con file is needed. For epochs, an Nx3 matrix containing the event number/corresponding trigger value in the third column is needed.

The following input files are optional:

A KIT marker file (mrk file) or an array-like containing the locations of the HPI coils in the MEG device coordinate system. These data are used together with the elp file to establish the coordinate transformation between the head and device coordinate systems.
A Polhemus points file (elp file) or an array-like containing the locations of the fiducials and the head-position indicator (HPI) coils. These data are usually given in the Polhemus head coordinate system.
A Polhemus head shape data file (hsp file) or an array-like containing locations of additional points from the head surface. These points must be given in the same coordinate system as that used for the elp file.

Modern Ricoh systems may encode this information it the file itself, in which case mrk, elp, and hsp can all be None and the data will be read from the file itself.

Note

The output fif file will use the Neuromag head coordinate system convention, see MEG/EEG and MRI coordinate systems. A coordinate transformation between the Polhemus head coordinates and the Neuromag head coordinates is included.

By default, KIT-157 systems assume the first 157 channels are the MEG channels, the next 3 channels are the reference compensation channels, and channels 160 onwards are designated as miscellaneous input channels (MISC 001, MISC 002, etc.). By default, KIT-208 systems assume the first 208 channels are the MEG channels, the next 16 channels are the reference compensation channels, and channels 224 onwards are designated as miscellaneous input channels (MISC 001, MISC 002, etc.).

In addition, it is possible to synthesize the digital trigger channel (STI 014) from available analog trigger channel data by specifying the following parameters:

A list of trigger channels (stim) or default triggers with order: ‘<’ | ‘>’ Channel-value correspondence when converting KIT trigger channels to a Neuromag-style stim channel. By default, we assume the first eight miscellaneous channels are trigger channels. For ‘<’, the largest values are assigned to the first channel (little endian; default). For ‘>’, the largest values are assigned to the last channel (big endian). Can also be specified as a list of trigger channel indexes.
The trigger channel slope (slope) : ‘+’ | ‘-’ How to interpret values on KIT trigger channels when synthesizing a Neuromag-style stim channel. With ‘+’, a positive slope (low-to-high) is interpreted as an event. With ‘-’, a negative slope (high-to-low) is interpreted as an event.
A stimulus threshold (stimthresh) : float The threshold level for accepting voltage changes in KIT trigger channels as a trigger event.

The synthesized trigger channel data value at sample \(k\) will be:

\[s(k) = \sum_{p = 1}^n {t_p(k) 2^{p - 1}}\ ,\]

where \(t_p(k)\) are the thresholded from the input channel data d_p(k):

\[\begin{split}t_p(k) = \Bigg\{ \begin{array}{l} 0 \text{ if } d_p(k) \leq t\\ 1 \text{ if } d_p(k) > t \end{array}\ .\end{split}\]

The threshold value \(t\) can be adjusted with the stimthresh parameter.

FieldTrip MEG/EEG data (.mat)#

MNE-Python includes mne.io.read_raw_fieldtrip(), mne.read_epochs_fieldtrip() and mne.read_evoked_fieldtrip() to read data coming from FieldTrip.

The data is imported directly from a .mat file.

The info parameter can be explicitly set to None. The import functions will still work but:

All channel locations will be in head coordinates.
Channel orientations cannot be guaranteed to be accurate.
All channel types will be set to generic types.

This is probably fine for anything that does not need that information, but if you intent to do things like interpolation of missing channels, source analysis or look at the RMS pairs of planar gradiometers, you most likely run into problems.

It is highly recommended to provide the info parameter as well. The info dictionary can be extracted by loading the original raw data file with the corresponding MNE-Python functions:

original_data = mne.io.read_raw_fiff('original_data.fif', preload=False)
original_info = original_data.info
data_from_ft = mne.read_evoked_fieldtrip('evoked_data.mat', original_info)

The imported data can have less channels than the original data. Only the information for the present ones is extracted from the info dictionary.

As of version 0.17, importing FieldTrip data has been tested on a variety of systems with the following results:

System	Read Raw Data	Read Epoched Data	Read Evoked Data
BTI	Works	Untested	Untested
CNT	Data imported as microvolts. Otherwise fine.	Data imported as microvolts. Otherwise fine.	Data imported as microvolts. Otherwise fine.
CTF	Works	Works	Works
EGI	Mostly Ok. Data imported as microvolts. FieldTrip does not apply calibration.	Mostly Ok. Data imported as microvolts. FieldTrip does not apply calibration.	Mostly Ok. Data imported as microvolts. FieldTrip does not apply calibration.
KIT	Does not work. Channel names are different in MNE-Python and FieldTrip.	Does not work. Channel names are different in MNE-Python and FieldTrip.	Does not work. Channel names are different in MNE-Python and FieldTrip.
Neuromag	Works	Works	Works
eximia	Works	Untested	Untested

Creating MNE data structures from arbitrary data (from memory)#

Arbitrary (e.g., simulated or manually read in) raw data can be constructed from memory by making use of mne.io.RawArray, mne.EpochsArray or mne.EvokedArray in combination with mne.create_info().

This functionality is illustrated in Creating MNE-Python data structures from scratch. Using 3rd party libraries such as NEO in combination with these functions abundant electrophysiological file formats can be easily loaded into MNE.

Estimated memory usage: 10 MB

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