.. _ch_convert:

.. contents:: Contents
   :local:
   :depth: 2

Here we describe the data reading and conversion utilities included
with the MNE software. The cheatsheet below summarizes the different
file formats supported by MNE software.

===================   ========================   =========  =================================================================
Datatype              File format                Extension  MNE-Python function
===================   ========================   =========  =================================================================
MEG                   Elekta Neuromag            .fif       :func:`mne.io.read_raw_fif`
MEG                   4-D Neuroimaging / BTI      dir       :func:`mne.io.read_raw_bti`
MEG                   CTF                         dir       :func:`mne.io.read_raw_ctf`
MEG                   KIT                         sqd       :func:`mne.io.read_raw_kit` and :func:`mne.read_epochs_kit`
EEG                   Brainvision                .vhdr      :func:`mne.io.read_raw_brainvision`
EEG                   European data format       .edf       :func:`mne.io.read_raw_edf`
EEG                   Biosemi data format        .bdf       :func:`mne.io.read_raw_edf`
EEG                   EGI simple binary          .egi       :func:`mne.io.read_raw_egi`
EEG                   EEGLAB                     .set       :func:`mne.io.read_raw_eeglab` and :func:`mne.read_epochs_eeglab`
Electrode locations   elc, txt, csd, sfp, htps   Misc       :func:`mne.channels.read_montage`
Electrode locations   EEGLAB loc, locs, eloc     Misc       :func:`mne.channels.read_montage`
===================   ========================   =========  =================================================================

.. note::
    All IO functions in MNE-Python performing reading/conversion of MEG and
    EEG data can be found in :mod:`mne.io` and start with `read_raw_`. All
    supported data formats can be read in MNE-Python directly without first
    saving it to fif.

Importing MEG data
##################

This section describes reading and converting of various MEG data formats.


Elekta NeuroMag (.fif)
======================

Neuromag Raw FIF files can be loaded using :func:`mne.io.read_raw_fif`.

.. note::
    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)``.

.. note::
    This file format also supports EEG data. An average reference will be added
    by default on reading EEG data. To change this behavior call the readers
    like this: ``mne.io.read_raw_fif(..., add_eeg_ref=False)``


Importing 4-D Neuroimaging / BTI data
=====================================

MNE-Python includes the :func:`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 :func:`mne.io.read_raw_bti` assumes these three files to be located
in the same folder.

.. note:: While reading the reference or compensation channels,
          currently, the compensation weights are not processed.
          As a result, the :class:`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. To augment the Magnes fif files with the necessary
          information, the command line tools include the utilities
          :ref:`mne_create_comp_data`, and :ref:`mne_add_to_meas_info`.
          Including the compensation channel data is recommended but not
          mandatory. 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.


Creating software gradient compensation data
--------------------------------------------

The utility mne_create_comp_data was
written to create software gradient compensation weight data for
4D Magnes fif files. This utility takes a text file containing the
compensation data as input and writes the corresponding fif file
as output. This file can be merged into the fif file containing
4D Magnes data with the utility :ref:`mne_add_to_meas_info`.
See :ref:`mne_create_comp_data` for command-line options.


Importing CTF data
==================

In MNE-Python, :func:`mne.io.read_raw_ctf` can be used to read CTF data.


Importing CTF Polhemus data
===========================

The CTF MEG systems store the Polhemus digitization data
in text files. The utility :ref:`mne_ctf_dig2fiff` was
created to convert these data files into the fif and hpts formats.


.. _BEHDDFBI:

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_compensate_data ,
which is a utility to change the software gradient compensation
state in evoked-response data files.

The fif files containing CTF data converted using the utility mne_ctf2fiff 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 :math:`n_1` MEG
sensor channels, :math:`n_2` reference sensor
channels, and :math:`n_3` other channels.
The data from all channels can be concatenated into a single vector

.. math::    x = [x_1^T x_2^T x_3^T]^T\ ,

where :math:`x_1`, :math:`x_2`,
and :math:`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 :math:`x_{(0)}` and :math:`x_{(k)}`, respectively,
are related by

.. math::    x_{(k)} = M_{(k)} x_{(0)}\ ,

where the composite compensation matrix is

.. math::    M_{(k)} = \begin{bmatrix}
		I_{n_1} & C_{(k)} & 0 \\
		0 & I_{n_2} & 0 \\
		0 & 0 & I_{n_3}
		\end{bmatrix}\ .

In the above, :math:`C_{(k)}` is a :math:`n_1` by :math:`n_2` compensation
data matrix corresponding to compensation "grade" :math:`k`.
It is easy to see that

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

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

.. math::    x_{(k)} = M_{(k)} M_{(p)}^{-1} x_{(p)}\ .

This operation is performed by :ref:`mne_compensate_data`.


Importing KIT MEG system data
=============================

MNE-Python includes the :func:`mne.io.read_raw_kit` and
:func:`mne.read_epochs_kit` to read and convert KIT MEG data.
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.

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.


.. note:: The output fif file will use the Neuromag head coordinate system convention, see :ref:`BJEBIBAI`. 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 miscellanous
  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 :math:`k` will
be:

.. math::    s(k) = \sum_{p = 1}^n {t_p(k) 2^{p - 1}}\ ,

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

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

The threshold value :math:`t` can
be adjusted with the ``stimthresh`` parameter, see below.


Importing EEG data
##################

The MNE package includes various functions and utilities for reading EEG
data and electrode templates.

Brainvision (.vhdr)
===================

Brainvision EEG files can be read in using :func:`mne.io.read_raw_brainvision`.

European data format (.edf)
===========================

EDF and EDF+ files can be read in using :func:`mne.io.read_raw_edf`.

http://www.edfplus.info/specs/edf.html

EDF (European Data Format) and EDF+ are 16-bit formats
http://www.edfplus.info/specs/edfplus.html

The EDF+ files may contain an annotation channel which can
be used to store trigger information. The Time-stamped Annotation
Lists (TALs) on the annotation  data can be converted to a trigger
channel (STI 014) using an annotation map file which associates
an annotation label with a number on the trigger channel.

Biosemi data format (.bdf)
==========================

The BDF format (http://www.biosemi.com/faq/file_format.htm) is a 24-bit variant
of the EDF format used by the EEG systems manufactured by a company called
BioSemi. It can also be read in using :func:`mne.io.read_raw_edf`.

.. warning:: The data samples in a BDF file are represented in a 3-byte (24-bit) format. Since 3-byte raw data buffers are not presently supported in the fif format these data will be changed to 4-byte integers in the conversion.

EGI simple binary (.egi)
========================

EGI simple binary files can be read in using :func:`mne.io.read_raw_egi`.
The EGI raw files are simple binary files with a header and can be exported
from using the EGI Netstation acquisition software.


EEGLAB set files (.set)
=======================

EEGLAB .set files can be read in using :func:`mne.io.read_raw_eeglab`
and :func:`mne.read_epochs_eeglab`.

Importing EEG data saved in the Tufts University format
=======================================================

The command line utility :ref:`mne_tufts2fiff` was
created in collaboration with Phillip Holcomb and Annette Schmid
from Tufts University to import their EEG data to the MNE software.

The Tufts EEG data is included in three files:

- The raw data file containing the acquired
  EEG data. The name of this file ends with the suffix ``.raw`` .

- The calibration raw data file. This file contains known calibration
  signals and is required to bring the data to physical units. The
  name of this file ends with the suffix ``c.raw`` .

- The electrode location information file. The name of this
  file ends with the suffix ``.elp`` .

See the options for the command-line utility :ref:`mne_tufts2fiff`.

Converting eXimia EEG data
==========================

EEG data from the Nexstim eXimia system can be converted
to the fif format with help of the :ref:`mne_eximia2fiff` script.
It creates a BrainVision ``vhdr`` file and calls :ref:`mne_brain_vision2fiff`.



Reading Electrode locations and Headshapes for EEG recordings
#############################################################

Some EEG formats (EGI, EDF/EDF+, BDF) neither contain electrode location
information nor head shape digitization information. Therefore, this information
has to be provided separately. For that purpose all readers have a montage
parameter to read locations from standard electrode templates or a polhemus
digitizer file. This can also be done post-hoc using the
:func:`mne.io.Raw.set_montage` method of the Raw object in memory.


When using the locations of the fiducial points the digitization data
are converted to the MEG head coordinate system employed in the
MNE software, see :ref:`BJEBIBAI`.


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 :class:`mne.io.RawArray`, :class:`mne.EpochsArray`
or :class:`mne.EvokedArray` in combination with :func:`mne.create_info`.

This functionality is illustrated in :ref:`example_io_plot_objects_from_arrays.py` .
Using 3rd party libraries such as NEO (https://pythonhosted.org/neo/) in combination
with these functions abundant electrophysiological file formats can be easily loaded
into MNE.
