data2bids.m

function cfg = data2bids(cfg, varargin)

% DATA2BIDS is a helper function to convert MEG, EEG, iEEG or anatomical MRI data to
% the Brain Imaging Data Structure. This function starts from an existing data file on
% disk or MATLAB memory, converts/writes the data file if needed and creates the
% required sidecar files.
%
% The overall idea is that you write a MATLAB script in which you call this
% function multiple times, once for each data files. For each data file it will
% write the corresponding JSON file. For MEG/EEG/iEEG data files it will also
% write the corresponding _channels.tsv and _events.tsv file.
%
% Use as
%   data2bids(cfg)
% or as
%   data2bids(cfg, data)
%
% The first input argument 'cfg' is the configuration structure, which contains the
% details for the (meta)data and which specifies the sidecar files you want to write.
% The optional 'data' argument corresponds to preprocessed raw data according to
% FT_DATAYPE_RAW or an anatomical MRI according to FT_DATAYPE_VOLUME. The optional
% data argument allows you to write a preprocessed and realigned anatomical MRI to
% disk, or to write a preprocessed electrophysiological dataset to disk.
%
% The configuration structure should contains
%   cfg.dataset                 = string, filename of the input data
%   cfg.outputfile              = string, optional filename for the output data, see below
%   cfg.keepnative              = string, 'yes' or 'no' to copy the native file rather than convert it (default = 'no')
%   cfg.presentationfile        = string, optional filename for the presentation log file, see below
%   cfg.mri.deface              = string, 'yes' or 'no' (default = 'no')
%   cfg.mri.writesidecar        = string, 'yes', 'replace', 'merge' or 'no' (default = 'yes')
%   cfg.mri.dicomfile           = string, filename of a matching DICOM file (default = [])
%   cfg.meg.writesidecar        = string, 'yes', 'replace', 'merge' or 'no' (default = 'yes')
%   cfg.eeg.writesidecar        = string, 'yes', 'replace', 'merge' or 'no' (default = 'yes')
%   cfg.ieeg.writesidecar       = string, 'yes', 'replace', 'merge' or 'no' (default = 'yes')
%   cfg.events.writesidecar     = string, 'yes', 'replace', 'merge' or 'no' (default = 'yes')
%   cfg.events.trl              = trial definition, see below
%   cfg.coordystem.writesidecar = string, 'yes', 'replace', 'merge' or 'no' (default = 'yes')
%   cfg.channels.writesidecar   = string, 'yes', 'replace', 'merge' or 'no' (default = 'yes')
%
% If you specify cfg.dataset without cfg.outputfile, this function will only
% construct and write the appropriate sidecar files matching the header details that
% it will get from the data file.
%
% If you specify cfg.dataset with cfg.outputfile, this function will  read the data
% from the input data file and convert and write it to the output file. The output
% format is NIFTI for anatomical MRIs, and BrainVision for EEG and iEEG. MEG data
% files are to be stored in BIDS in their native format, this function will NOT
% convert or rename them for you.
%
% You can specify cfg.mri.dicomfile in combination with a NIFTI file. This will
% read the detailled header information (MR scanner and sequence details) from
% the DICOM file and used to fill in the details of the JSON file.
%
% You can specify cfg.events.trl as a Nx3 matrix with the trial definition (see
% FT_DEFINETRIAL) or as a MATLAB table. When specified as table, the first three
% columns containing integer values corresponding to the begsample, endsample and
% offset, the additional colums can be of another type and can have any name. If you
% do not specify the definition of trials, the events will be read from the dataset.
%
% If you do not specify cfg.events.trl, this function will read the events from
% the MEG/EEG/iEEG data file and write them to the events.tsv file.
%
% You can specify cfg.presentationfile with a NBS presentation log file, which will
% be aligned with the data based on triggers (MEG/EEG/iEEG) or based on the
% volumes (fMRI). To indicate how triggers or volumes match the presentation events,
% you should also specify
%   cfg.trigger.eventtype       = string (default = [])
%   cfg.trigger.eventvalue      = string or number
%   cfg.presentation.eventtype  = string (default = [])
%   cfg.presentation.eventvalue = string or number
%   cfg.presentation.skip       = 'last'/'first'/'none'
%
% General BIDS options that apply to all data types are
%   cfg.TaskName                    = string
%   cfg.InstitutionName             = string
%   cfg.InstitutionAddress          = string
%   cfg.InstitutionalDepartmentName = string
%   cfg.Manufacturer                = string
%   cfg.ManufacturersModelName      = string
%   cfg.DeviceSerialNumber          = string
%   cfg.SoftwareVersions            = string
%
% General BIDS options that apply to all functional data types are
%   cfg.TaskDescription             = string
%   cfg.Instructions                = string
%   cfg.CogAtlasID                  = string
%   cfg.CogPOID                     = string
%
% There are many more BIDS options for the JSON files for specific datatypes. Rather
% than listing them here in the help, please open this function in the MATLAB editor,
% and scroll down a bit to see what those are.
%
% Example with a CTF dataset on disk that needs no conversion
%   cfg = [];
%   cfg.dataset                     = 'sub-01_ses-meg_task-language_meg.ds';
%   cfg.TaskName                    = 'language';
%   cfg.meg.PowerLineFrequency      = 50;
%   cfg.InstitutionName             = 'Radboud University';
%   cfg.InstitutionalDepartmentName = 'Donders Institute for Brain, Cognition and Behaviour';
%   data2bids(cfg)
%
% Example with an anatomical MRI on disk that needs no conversion
%   cfg = [];
%   cfg.dataset                     = 'sub-01_ses-mri_T1w.nii';
%   cfg.mri.dicomfile               = '00080_1.3.12.2.1107.5.2.43.66068.2017082413175824865636649.IMA'
%   cfg.mri.MagneticFieldStrength   = 3; % this is usually not needed, as it will be obtained from the DICOM file
%   cfg.InstitutionName             = 'Radboud University';
%   cfg.InstitutionalDepartmentName = 'Donders Institute for Brain, Cognition and Behaviour';
%   data2bids(cfg)
%
% Example with a NeuroScan EEG dataset on disk that needs to be converted
%   cfg = [];
%   cfg.dataset                     = 'subject01.cnt';
%   cfg.outputfile                  = 'sub-001_task-visual_eeg.vhdr';
%   cfg.InstitutionName             = 'Radboud University';
%   cfg.InstitutionalDepartmentName = 'Donders Institute for Brain, Cognition and Behaviour';
%   data2bids(cfg)
%
% Example with preprocessed EEG data in memory
%   cfg = [];
%   cfg.dataset                     = 'subject01.cnt';
%   cfg.bpfilter                    = 'yes';
%   cfg.bpfreq                      = [0.1 40];
%   data = ft_preprocessing(cfg);
%   cfg = [];
%   cfg.outputfile                  = 'sub-001_task-visual_eeg.vhdr';
%   cfg.InstitutionName             = 'Radboud University';
%   cfg.InstitutionalDepartmentName = 'Donders Institute for Brain, Cognition and Behaviour';
%   data2bids(cfg, data)
%
% Example with realigned and resliced anatomical MRI data in memory
%   cfg = [];
%   cfg.outputfile                  = 'sub-01_ses-mri_T1w.nii';
%   cfg.mri.MagneticFieldStrength   = 3;
%   cfg.InstitutionName             = 'Radboud University';
%   cfg.InstitutionalDepartmentName = 'Donders Institute for Brain, Cognition and Behaviour';
%   data2bids(cfg, mri)
%
% This function corresponds to version 1.1.1 of the BIDS specification.
% See http://bids.neuroimaging.io/ for further details.

% Copyright (C) 2018, Robert Oostenveld
%
% This file is part of FieldTrip, see http://www.fieldtriptoolbox.org
% for the documentation and details.
%
%    FieldTrip is free software: you can redistribute it and/or modify
%    it under the terms of the GNU General Public License as published by
%    the Free Software Foundation, either version 3 of the License, or
%    (at your option) any later version.
%
%    FieldTrip is distributed in the hope that it will be useful,
%    but WITHOUT ANY WARRANTY; without even the implied warranty of
%    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
%    GNU General Public License for more details.
%
%    You should have received a copy of the GNU General Public License
%    along with FieldTrip. If not, see <http://www.gnu.org/licenses/>.
%
% $Id$

% these are used by the ft_preamble/ft_postamble function and scripts
ft_revision = '$Id$';
ft_nargin   = nargin;
ft_nargout  = nargout;

% do the general setup of the function
ft_defaults
ft_preamble init
ft_preamble debug

if ft_abort
  % do not continue function execution in case the outputfile is present and the user indicated to keep it
  return
end

% ensure backward compatibility
cfg = ft_checkconfig(cfg, 'renamed', {'anat', 'mri'});
cfg = ft_checkconfig(cfg, 'forbidden', 'deface');       % should be cfg.mri.deface

% get the options and set the defaults
cfg.dataset                 = ft_getopt(cfg, 'dataset');
cfg.feedback                = ft_getopt(cfg, 'feedback', 'yes');
cfg.outputfile              = ft_getopt(cfg, 'outputfile', cfg.dataset);      % default is the same as the input dataset
cfg.keepnative              = ft_getopt(cfg, 'keepnative', 'no');             % default is convert to vhdr
cfg.presentationfile        = ft_getopt(cfg, 'presentationfile');             % full path to the NBS presentation log file, it will be read and parsed using FT_READ_EVENT
cfg.presentation            = ft_getopt(cfg, 'presentation');
cfg.presentation.eventtype  = ft_getopt(cfg.presentation, 'eventtype');
cfg.presentation.eventvalue = ft_getopt(cfg.presentation, 'eventvalue');
cfg.presentation.skip       = ft_getopt(cfg.presentation, 'skip',  'last'); % this is a sensible default for fMRI, for MEG one should probably do 'none'
cfg.trigger                 = ft_getopt(cfg, 'trigger');
cfg.trigger.eventtype       = ft_getopt(cfg.trigger, 'eventtype');
cfg.trigger.eventvalue      = ft_getopt(cfg.trigger, 'eventvalue');
cfg.trigger.event           = ft_getopt(cfg.trigger, 'event');

cfg.mri                     = ft_getopt(cfg, 'mri');
cfg.mri.deface              = ft_getopt(cfg.mri, 'deface', 'no');             % deface the anatomical MRI
cfg.mri.dicomfile           = ft_getopt(cfg.mri, 'dicomfile');                % get the details from one of the original DICOM files
cfg.mri.writesidecar        = ft_getopt(cfg.mri, 'writesidecar', 'yes');      % whether to write the sidecar file

cfg.meg                     = ft_getopt(cfg, 'meg');
cfg.meg.writesidecar        = ft_getopt(cfg.meg, 'writesidecar', 'yes');      % whether to write the sidecar file

cfg.eeg                     = ft_getopt(cfg, 'eeg');
cfg.eeg.writesidecar        = ft_getopt(cfg.eeg, 'writesidecar', 'yes');      % whether to write the sidecar file

cfg.ieeg                    = ft_getopt(cfg, 'ieeg');
cfg.ieeg.writesidecar       = ft_getopt(cfg.ieeg, 'writesidecar', 'yes');     % whether to write the sidecar file

cfg.channels                = ft_getopt(cfg, 'channels');
cfg.channels.writesidecar   = ft_getopt(cfg.channels, 'writesidecar', 'yes'); % whether to write the sidecar file

cfg.events                  = ft_getopt(cfg, 'events');
cfg.events.trl              = ft_getopt(cfg.events, 'trl');                   % this can contain the trial definition as Nx3 array or as table
cfg.events.writesidecar     = ft_getopt(cfg.events, 'writesidecar', 'yes');   % whether to write the sidecar file

cfg.coordsystem              = ft_getopt(cfg, 'coordsystem');
cfg.coordsystem.writesidecar = ft_getopt(cfg.coordsystem, 'writesidecar', 'yes');

%% Generic fields for all data types
cfg.TaskName                          = ft_getopt(cfg, 'TaskName'                    ); % REQUIRED. Name of the task (for resting state use the "rest" prefix). Different Tasks SHOULD NOT have the same name. The Task label is derived from this field by removing all non alphanumeric ([a-zA-Z0-9]) characters.
cfg.TaskDescription                   = ft_getopt(cfg, 'TaskDescription'             ); % OPTIONAL. Description of the task.
cfg.Instructions                      = ft_getopt(cfg, 'Instructions'                ); % OPTIONAL. Text of the instructions given to participants before the scan. This is not only important for behavioral or cognitive tasks but also in resting state paradigms (e.g. to distinguish between eyes open and eyes closed).
cfg.CogAtlasID                        = ft_getopt(cfg, 'CogAtlasID'                  ); % OPTIONAL. URL of the corresponding "Cognitive Atlas term that describes the task (e.g. Resting State with eyes closed ""http://www.cognitiveatlas.org/term/id/trm_54e69c642d89b""")
cfg.CogPOID                           = ft_getopt(cfg, 'CogPOID'                     ); % OPTIONAL. URL of the corresponding "CogPO term that describes the task (e.g. Rest "http://wiki.cogpo.org/index.php?title=Rest")
cfg.Manufacturer                      = ft_getopt(cfg, 'Manufacturer'                ); % OPTIONAL. Manufacturer of the MEG system ("CTF", ""Elekta/Neuromag"", ""4D/BTi"", ""KIT/Yokogawa"", ""ITAB"", "KRISS", "Other")
cfg.ManufacturersModelName            = ft_getopt(cfg, 'ManufacturersModelName'      ); % OPTIONAL. Manufacturer's designation of the MEG scanner model (e.g. "CTF-275"). See "Appendix VII" with preferred names
cfg.DeviceSerialNumber                = ft_getopt(cfg, 'DeviceSerialNumber'          ); % OPTIONAL. The serial number of the equipment that produced the composite instances. A pseudonym can also be used to prevent the equipment from being identifiable, as long as each pseudonym is unique within the dataset.
cfg.SoftwareVersions                  = ft_getopt(cfg, 'SoftwareVersions'            ); % OPTIONAL. Manufacturer's designation of the acquisition software.
cfg.InstitutionName                   = ft_getopt(cfg, 'InstitutionName'             ); % OPTIONAL. The name of the institution in charge of the equipment that produced the composite instances.
cfg.InstitutionAddress                = ft_getopt(cfg, 'InstitutionAddress'          ); % OPTIONAL. The address of the institution in charge of the equipment that produced the composite instances.
cfg.InstitutionalDepartmentName       = ft_getopt(cfg, 'InstitutionalDepartmentName' ); % The department in the institution in charge of the equipment that produced the composite instances. Corresponds to DICOM Tag 0008, 1040 "Institutional Department Name".

%% information for the coordsystem.json file for MEG, EEG and iEEG
cfg.coordsystem.MEGCoordinateSystem                             = ft_getopt(cfg.coordsystem, 'MEGCoordinateSystem'                            ); % REQUIRED. Defines the coordinate system for the MEG sensors. See Appendix VIII: preferred names of Coordinate systems. If "Other", provide definition of the coordinate system in [MEGCoordinateSystemDescription].
cfg.coordsystem.MEGCoordinateUnits                              = ft_getopt(cfg.coordsystem, 'MEGCoordinateUnits'                             ); % REQUIRED. Units of the coordinates of MEGCoordinateSystem. MUST be ???m???, ???cm???, or ???mm???.
cfg.coordsystem.MEGCoordinateSystemDescription                  = ft_getopt(cfg.coordsystem, 'MEGCoordinateSystemDescription'                 ); % OPTIONAL. Freeform text description or link to document describing the MEG coordinate system system in detail.
cfg.coordsystem.EEGCoordinateSystem                             = ft_getopt(cfg.coordsystem, 'EEGCoordinateSystem'                            ); % OPTIONAL. Describes how the coordinates of the EEG sensors are to be interpreted.
cfg.coordsystem.EEGCoordinateUnits                              = ft_getopt(cfg.coordsystem, 'EEGCoordinateUnits'                             ); % OPTIONAL. Units of the coordinates of EEGCoordinateSystem. MUST be ???m???, ???cm???, or ???mm???.
cfg.coordsystem.EEGCoordinateSystemDescription                  = ft_getopt(cfg.coordsystem, 'EEGCoordinateSystemDescription'                 ); % OPTIONAL. Freeform text description or link to document describing the EEG coordinate system system in detail.
cfg.coordsystem.HeadCoilCoordinates                             = ft_getopt(cfg.coordsystem, 'HeadCoilCoordinateSystem'                       ); % OPTIONAL. Key:value pairs describing head localization coil labels and their coordinates, interpreted following the HeadCoilCoordinateSystem, e.g., {"NAS": [12.7,21.3,13.9], "LPA": [5.2,11.3,9.6], "RPA": [20.2,11.3,9.1]}. Note that coils are not always placed at locations that have a known anatomical name (e.g. for Elekta, Yokogawa systems); in that case generic labels can be used (e.g. {"coil1": [122,213,123], "coil2": [67,123,86], "coil3": [219,110,81]} ).
cfg.coordsystem.HeadCoilCoordinateSystem                        = ft_getopt(cfg.coordsystem, 'HeadCoilCoordinates'                            ); % OPTIONAL. Defines the coordinate system for the coils. See Appendix VIII: preferred names of Coordinate systems. If "Other", provide definition of the coordinate system in HeadCoilCoordinateSystemDescription.
cfg.coordsystem.HeadCoilCoordinateUnits                         = ft_getopt(cfg.coordsystem, 'HeadCoilCoordinateUnits'                        ); % OPTIONAL. Units of the coordinates of HeadCoilCoordinateSystem. MUST be ???m???, ???cm???, or ???mm???.
cfg.coordsystem.HeadCoilCoordinateSystemDescription             = ft_getopt(cfg.coordsystem, 'HeadCoilCoordinateSystemDescription'            ); % OPTIONAL. Freeform text description or link to document describing the Head Coil coordinate system system in detail.
cfg.coordsystem.DigitizedHeadPoints                             = ft_getopt(cfg.coordsystem, 'DigitizedHeadPoints'                            ); % OPTIONAL. Relative path to the file containing the locations of digitized head points collected during the session (e.g., "sub-01_headshape.pos"). RECOMMENDED for all MEG systems, especially for CTF and 4D/BTi. For Elekta/Neuromag the head points will be stored in the fif file.
cfg.coordsystem.DigitizedHeadPointsCoordinateSystem             = ft_getopt(cfg.coordsystem, 'DigitizedHeadPointsCoordinateSystem'            ); % OPTIONAL. Defines the coordinate system for the digitized head points. See Appendix VIII: preferred names of Coordinate systems. If "Other", provide definition of the coordinate system in DigitizedHeadPointsCoordinateSystemDescription.
cfg.coordsystem.DigitizedHeadPointsCoordinateUnits              = ft_getopt(cfg.coordsystem, 'DigitizedHeadPointsCoordinateUnits'             ); % OPTIONAL. Units of the coordinates of DigitizedHeadPointsCoordinateSystem. MUST be ???m???, ???cm???, or ???mm???.
cfg.coordsystem.DigitizedHeadPointsCoordinateSystemDescription  = ft_getopt(cfg.coordsystem, 'DigitizedHeadPointsCoordinateSystemDescription' ); % OPTIONAL. Freeform text description or link to document describing the Digitized head Points coordinate system system in detail.
cfg.coordsystem.IntendedFor                                     = ft_getopt(cfg.coordsystem, 'IntendedFor'                                    ); % OPTIONAL. Path or list of path relative to the subject subfolder pointing to the structural MRI, possibly of different types if a list is specified, to be used with the MEG recording. The path(s) need(s) to use forward slashes instead of backward slashes (e.g. "ses-<label>/anat/sub-01_T1w.nii.gz").
cfg.coordsystem.AnatomicalLandmarkCoordinates                   = ft_getopt(cfg.coordsystem, 'AnatomicalLandmarkCoordinates'                  ); % OPTIONAL. Key:value pairs of the labels and 3-D digitized locations of anatomical landmarks, interpreted following the AnatomicalLandmarkCoordinateSystem, e.g., {"NAS": [12.7,21.3,13.9], "LPA": [5.2,11.3,9.6], "RPA": [20.2,11.3,9.1]}.
cfg.coordsystem.AnatomicalLandmarkCoordinateSystem              = ft_getopt(cfg.coordsystem, 'AnatomicalLandmarkCoordinateSystem'             ); % OPTIONAL. Defines the coordinate system for the anatomical landmarks. See Appendix VIII: preferred names of Coordinate systems. If "Other", provide definition of the coordinate system in AnatomicalLandmarkCoordinateSystemDescripti on.
cfg.coordsystem.AnatomicalLandmarkCoordinateUnits               = ft_getopt(cfg.coordsystem, 'AnatomicalLandmarkCoordinateUnits'              ); % OPTIONAL. Units of the coordinates of AnatomicalLandmarkCoordinateSystem. MUST be ???m???, ???cm???, or ???mm???.
cfg.coordsystem.AnatomicalLandmarkCoordinateSystemDescription   = ft_getopt(cfg.coordsystem, 'AnatomicalLandmarkCoordinateSystemDescription'  ); % OPTIONAL. Freeform text description or link to document describing the Head Coil coordinate system system in detail.
cfg.coordsystem.FiducialsDescription                            = ft_getopt(cfg.coordsystem, 'FiducialsDescription'                           ); % OPTIONAL. A freeform text field documenting the anatomical landmarks that were used and how the head localization coils were placed relative to these. This field can describe, for instance, whether the true anatomical locations of the left and right pre-auricular points were used and digitized, or rather whether they were defined as the intersection between the tragus and the helix (the entry of the ear canal), or any other anatomical description of selected points in the vicinity of the ears.

%% MEG specific fields
cfg.meg.SamplingFrequency             = ft_getopt(cfg.meg, 'SamplingFrequency'           ); % REQUIRED. Sampling frequency (in Hz) of all the data in the recording, regardless of their type (e.g., 2400)
cfg.meg.PowerLineFrequency            = ft_getopt(cfg.meg, 'PowerLineFrequency'          ); % REQUIRED. Frequency (in Hz) of the power grid at the geographical location of the MEG instrument (i.e. 50 or 60)
cfg.meg.DewarPosition                 = ft_getopt(cfg.meg, 'DewarPosition'               ); % REQUIRED. Position of the dewar during the MEG scan: "upright", "supine" or "degrees" of angle from vertical: for example on CTF systems, upright=15??, supine = 90??.
cfg.meg.SoftwareFilters               = ft_getopt(cfg.meg, 'SoftwareFilters'             ); % REQUIRED. List of temporal and/or spatial software filters applied, orideally key:valuepairsofpre-appliedsoftwarefiltersandtheir parameter values: e.g., {"SSS": {"frame": "head", "badlimit": 7}}, {"SpatialCompensation": {"GradientOrder": Order of the gradient compensation}}. Write "n/a" if no software filters applied.
cfg.meg.DigitizedLandmarks            = ft_getopt(cfg.meg, 'DigitizedLandmarks'          ); % REQUIRED. Boolean ("true" or "false") value indicating whether anatomical landmark points (i.e. fiducials) are contained within this recording.
cfg.meg.DigitizedHeadPoints           = ft_getopt(cfg.meg, 'DigitizedHeadPoints'         ); % REQUIRED. Boolean ("true" or "false") value indicating whether head points outlining the scalp/face surface are contained within this recording.
cfg.meg.MEGChannelCount               = ft_getopt(cfg.meg, 'MEGChannelCount'             ); % OPTIONAL. Number of MEG channels (e.g. 275)
cfg.meg.MEGREFChannelCount            = ft_getopt(cfg.meg, 'MEGREFChannelCount'          ); % OPTIONAL. Number of MEG reference channels (e.g. 23). For systems without such channels (e.g. Neuromag Vectorview), MEGREFChannelCount"=0
cfg.meg.EEGChannelCount               = ft_getopt(cfg.meg, 'EEGChannelCount'             ); % OPTIONAL. Number of EEG channels recorded simultaneously (e.g. 21)
cfg.meg.ECOGChannelCount              = ft_getopt(cfg.meg, 'ECOGChannelCount'            ); % OPTIONAL. Number of ECoG channels
cfg.meg.SEEGChannelCount              = ft_getopt(cfg.meg, 'SEEGChannelCount'            ); % OPTIONAL. Number of SEEG channels
cfg.meg.EOGChannelCount               = ft_getopt(cfg.meg, 'EOGChannelCount'             ); % OPTIONAL. Number of EOG channels
cfg.meg.ECGChannelCount               = ft_getopt(cfg.meg, 'ECGChannelCount'             ); % OPTIONAL. Number of ECG channels
cfg.meg.EMGChannelCount               = ft_getopt(cfg.meg, 'EMGChannelCount'             ); % OPTIONAL. Number of EMG channels
cfg.meg.MiscChannelCount              = ft_getopt(cfg.meg, 'MiscChannelCount'            ); % OPTIONAL. Number of miscellaneous analog channels for auxiliary signals
cfg.meg.TriggerChannelCount           = ft_getopt(cfg.meg, 'TriggerChannelCount'         ); % OPTIONAL. Number of channels for digital (TTL bit level) triggers
cfg.meg.RecordingDuration             = ft_getopt(cfg.meg, 'RecordingDuration'           ); % OPTIONAL. Length of the recording in seconds (e.g. 3600)
cfg.meg.RecordingType                 = ft_getopt(cfg.meg, 'RecordingType'               ); % OPTIONAL. Defines whether the recording is "continuous" or "epoched"; this latter limited to time windows about events of interest (e.g., stimulus presentations, subject responses etc.)
cfg.meg.EpochLength                   = ft_getopt(cfg.meg, 'EpochLength'                 ); % OPTIONAL. Duration of individual epochs in seconds (e.g. 1) in case of epoched data
cfg.meg.ContinuousHeadLocalization    = ft_getopt(cfg.meg, 'ContinuousHeadLocalization'  ); % OPTIONAL. Boolean ("true" or "false") value indicating whether continuous head localisation was performed.
cfg.meg.HeadCoilFrequency             = ft_getopt(cfg.meg, 'HeadCoilFrequency'           ); % OPTIONAL. List of frequencies (in Hz) used by the head localisation coils ("HLC" in CTF systems, "HPI" in Elekta, "COH" in 4D/BTi) that track the subject's head position in the MEG helmet (e.g. [293, 307, 314, 321])
cfg.meg.MaxMovement                   = ft_getopt(cfg.meg, 'MaxMovement'                 ); % OPTIONAL. Maximum head movement (in mm) detected during the recording, as measured by the head localisation coils (e.g., 4.8)
cfg.meg.SubjectArtefactDescription    = ft_getopt(cfg.meg, 'SubjectArtefactDescription'  ); % OPTIONAL. Freeform description of the observed subject artefact and its possible cause (e.g. "Vagus Nerve Stimulator", "non-removable implant"). If this field is set to "n/a", it will be interpreted as absence of major source of artifacts except cardiac and blinks.
cfg.meg.AssociatedEmptyRoom           = ft_getopt(cfg.meg, 'AssociatedEmptyRoom'         ); % OPTIONAL. Relative path in BIDS folder structure to empty-room file associated with the subject's MEG recording. The path needs to use forward slashes instead of backward slashes (e.g. "sub-emptyroom/ses-<label>/meg/sub-emptyroom_ses-<label>_ta sk-noise_run-<label>_meg.ds").

%% EEG specific fields
cfg.eeg.SamplingFrequency             = ft_getopt(cfg.eeg, 'SamplingFrequency'           ); % Sampling frequency (in Hz) of the EEG recording (e.g. 2400)
cfg.eeg.EEGChannelCount               = ft_getopt(cfg.eeg, 'EEGChannelCount'             ); % Number of EEG channels included in the recording (e.g. 128).
cfg.eeg.EOGChannelCount               = ft_getopt(cfg.eeg, 'EOGChannelCount'             ); % Number of EOG channels included in the recording (e.g. 2).
cfg.eeg.ECGChannelCount               = ft_getopt(cfg.eeg, 'ECGChannelCount'             ); % Number of ECG channels included in the recording (e.g. 1).
cfg.eeg.EMGChannelCount               = ft_getopt(cfg.eeg, 'EMGChannelCount'             ); % Number of EMG channels included in the recording (e.g. 2).
cfg.eeg.EEGReference                  = ft_getopt(cfg.eeg, 'EEGReference'                ); % Description of the type of reference used (common", "average", "DRL", "bipolar" ).  Any specific electrode used as reference should be indicated as such in the channels.tsv file
cfg.eeg.MiscChannelCount              = ft_getopt(cfg.eeg, 'MiscChannelCount'            ); % Number of miscellaneous analog channels for auxiliary  signals
cfg.eeg.TriggerChannelCount           = ft_getopt(cfg.eeg, 'TriggerChannelCount'         ); % Number of channels for digital and analog triggers.
cfg.eeg.PowerLineFrequency            = ft_getopt(cfg.eeg, 'PowerLineFrequency'          ); % Frequency (in Hz) of the power grid where the EEG is installed (i.e. 50 or 60).
cfg.eeg.EEGPlacementScheme            = ft_getopt(cfg.eeg, 'EEGPlacementScheme'          ); % Placement scheme of the EEG electrodes. Either the name of a placement system (e.g. "10-20", "equidistant", "geodesic") or a list of electrode positions (e.g. "Cz", "Pz").
cfg.eeg.CapManufacturer               = ft_getopt(cfg.eeg, 'CapManufacturer'             ); % name of the cap manufacturer
cfg.eeg.CapModelName                  = ft_getopt(cfg.eeg, 'CapModelName'                ); % Manufacturer's designation of the EEG cap model (e.g. "CAPML128", "actiCAP 64Ch Standard-2")
cfg.eeg.HardwareFilters               = ft_getopt(cfg.eeg, 'HardwareFilters'             ); % List of hardware (amplifier) filters applied or ideally  key:value pairs of pre-applied filters and their parameter values
cfg.eeg.SoftwareFilters               = ft_getopt(cfg.eeg, 'SoftwareFilters'             ); % List of temporal software filters applied or ideally  key:value pairs of pre-applied filters and their parameter values
cfg.eeg.RecordingDuration             = ft_getopt(cfg.eeg, 'RecordingDuration'           ); % Length of the recording in seconds (e.g. 3600)
cfg.eeg.RecordingType                 = ft_getopt(cfg.eeg, 'RecordingType'               ); % "continuous", "epoched"
cfg.eeg.EpochLength                   = ft_getopt(cfg.eeg, 'EpochLength'                 ); % Duration of individual epochs in seconds (e.g. 1). If recording was continuous, set value to Inf or leave out the field.
cfg.eeg.DeviceSoftwareVersion         = ft_getopt(cfg.eeg, 'DeviceSoftwareVersion'       ); % Manufacturer's designation of the acquisition software.
cfg.eeg.SubjectArtefactDescription    = ft_getopt(cfg.eeg, 'SubjectArtefactDescription'  ); % Freeform description of the observed subject artefact and its possible cause (e.g. "Vagus Nerve Stimulator", "non-removable implant"). If this field is left empty, it will be interpreted as absence of  a source of (constantly present) artifacts.
cfg.eeg.SimultaneousRecording         = ft_getopt(cfg.eeg, 'SimultaneousRecording'       ); % indicate over acquired modalities (keys are: fMRI, PET, MEG, NIRS)

%% iEEG specific fields
cfg.ieeg.SamplingFrequency               = ft_getopt(cfg, 'SamplingFrequency'              ); % REQUIRED. Sampling frequency (in Hz) of all the iEEG channels in the recording (e.g., 2400). All other channels should have frequency specified as well in the channels.tsv file.
cfg.ieeg.PowerLineFrequency              = ft_getopt(cfg, 'PowerLineFrequency'             ); % REQUIRED. Frequency (in Hz) of the power grid where the iEEG recording was done (i.e. 50 or 60)
cfg.ieeg.HardwareFilters                 = ft_getopt(cfg, 'HardwareFilters'                ); % REQUIRED. List of hardware (amplifier) filters applied with  key:value pairs of filter parameters and their values.
cfg.ieeg.SoftwareFilters                 = ft_getopt(cfg, 'SoftwareFilters'                ); % REQUIRED. List of temporal software filters applied or ideally  key:value pairs of pre-applied filters and their parameter values. (n/a if none).
cfg.ieeg.Manufacturer                    = ft_getopt(cfg, 'Manufacturer'                   ); % RECOMMENDED. Manufacturer of the amplifier system  (e.g. "TDT, Blackrock")
cfg.ieeg.ManufacturersModelName          = ft_getopt(cfg, 'ManufacturersModelName'         ); % RECOMMENDED. Manufacturer’s designation of the iEEG amplifier model.
cfg.ieeg.ECOGChannelCount                = ft_getopt(cfg, 'ECOGChannelCount'               ); % RECOMMENDED. Number of iEEG surface channels included in the recording (e.g. 120)
cfg.ieeg.SEEGChannelCount                = ft_getopt(cfg, 'SEEGChannelCount'               ); % RECOMMENDED. Number of iEEG depth channels included in the recording (e.g. 8)
cfg.ieeg.EEGChannelCount                 = ft_getopt(cfg, 'EEGChannelCount'                ); % RECOMMENDED. Number of scalp EEG channels recorded simultaneously (e.g. 21)
cfg.ieeg.EOGChannelCount                 = ft_getopt(cfg, 'EOGChannelCount'                ); % RECOMMENDED. Number of EOG channels
cfg.ieeg.ECGChannelCount                 = ft_getopt(cfg, 'ECGChannelCount'                ); % RECOMMENDED. Number of ECG channels
cfg.ieeg.EMGChannelCount                 = ft_getopt(cfg, 'EMGChannelCount'                ); % RECOMMENDED. Number of EMG channels
cfg.ieeg.MiscChannelCount                = ft_getopt(cfg, 'MiscChannelCount'               ); % RECOMMENDED. Number of miscellaneous analog channels for auxiliary  signals
cfg.ieeg.TriggerChannelCount             = ft_getopt(cfg, 'TriggerChannelCount'            ); % RECOMMENDED. Number of channels for digital (TTL bit level) triggers
cfg.ieeg.RecordingDuration               = ft_getopt(cfg, 'RecordingDuration'              ); % RECOMMENDED. Length of the recording in seconds (e.g. 3600)
cfg.ieeg.RecordingType                   = ft_getopt(cfg, 'RecordingType'                  ); % RECOMMENDED. Defines whether the recording is  “continuous” or  “epoched”; this latter limited to time windows about events of interest (e.g., stimulus presentations, subject responses etc.)
cfg.ieeg.EpochLength                     = ft_getopt(cfg, 'EpochLength'                    ); % RECOMMENDED. Duration of individual epochs in seconds (e.g. 1) in case of epoched data
cfg.ieeg.SubjectArtefactDescription      = ft_getopt(cfg, 'SubjectArtefactDescription'     ); % RECOMMENDED. Freeform description of the observed subject artefact and its possible cause (e.g. “door open”, ”nurse walked into room at 2 min”, ”seizure at 10 min”). If this field is left empty, it will be interpreted as absence of artifacts.
cfg.ieeg.SoftwareVersions                = ft_getopt(cfg, 'SoftwareVersions'               ); % RECOMMENDED. Manufacturer’s designation of the acquisition software.
cfg.ieeg.iEEGReference                   = ft_getopt(cfg, 'iEEGReference'                  ); % REQUIRED. General description of the reference scheme used and (when applicable) of location of the reference electrode in the raw recordings (e.g. "left mastoid”, “bipolar”, “T01” for electrode with name T01, “intracranial electrode on top of a grid, not included with data”, “upside down electrode”). If different channels have a different reference, this field should have a general description and the channel specific reference should be defined in the _channels.tsv file.
cfg.ieeg.ElectrodeManufacturer           = ft_getopt(cfg, 'ElectrodeManufacturer'          ); % RECOMMENDED. can be used if all electrodes are of the same manufacturer (e.g. AD-TECH, DIXI). If electrodes of different manufacturers are used, please use the corresponding table in the _electrodes.tsv file.
cfg.ieeg.ElectrodeManufacturersModelName = ft_getopt(cfg, 'ElectrodeManufacturersModelName'); % RECOMMENDED. If different electrode types are used, please use the corresponding table in the _electrodes.tsv file.
cfg.ieeg.iEEGGround                      = ft_getopt(cfg, 'iEEGGround'                     ); % RECOMMENDED. Description  of the location of the ground electrode (“placed on right mastoid (M2)”).
cfg.ieeg.iEEGPlacementScheme             = ft_getopt(cfg, 'iEEGPlacementScheme'            ); % RECOMMENDED. Freeform description of the placement of the iEEG electrodes. Left/right/bilateral/depth/surface (e.g. “left frontal grid and bilateral hippocampal depth” or “surface strip and STN depth” or “clinical indication bitemporal, bilateral temporal strips and left grid”).
cfg.ieeg.iEEGElectrodeGroups             = ft_getopt(cfg, 'iEEGElectrodeGroups'            ); % RECOMMENDED. Field to describe the way electrodes are grouped into strips, grids or depth probes e.g. {'grid1': "10x8 grid on left temporal pole", 'strip2': "1x8 electrode strip on xxx"}.
cfg.ieeg.ElectricalStimulation           = ft_getopt(cfg, 'ElectricalStimulation'          ); % OPTIONAL. Boolean field to specify if electrical stimulation was done during the recording (options are “true” or “false”). Parameters for event-like stimulation should be specified in the _events.tsv file (see example underneath).
cfg.ieeg.ElectricalStimulationParameters = ft_getopt(cfg, 'ElectricalStimulationParameters'); % OPTIONAL. Free form description of stimulation parameters, such as frequency, shape etc. Specific onsets can be specified in the _events.tsv file. Specific shapes can be described here in freeform text.

%% MR Scanner Hardware
cfg.mri.MagneticFieldStrength         = ft_getopt(cfg.mri, 'MagneticFieldStrength'          ); % Nominal field strength of MR magnet in Tesla. Corresponds to DICOM Tag 0018,0087 "Magnetic Field Strength" .
cfg.mri.StationName                   = ft_getopt(cfg.mri, 'StationName'                    ); % Institution defined name of the machine that produced the composite instances. Corresponds to DICOM Tag 0008, 1010 "Station Name"
cfg.mri.HardcopyDeviceSoftwareVersion = ft_getopt(cfg.mri, 'HardcopyDeviceSoftwareVersion'  ); % (Deprecated) Manufacturer's designation of the software of the device that created this Hardcopy Image (the printer). Corresponds to DICOM Tag 0018, 101A "Hardcopy Device Software Version".
cfg.mri.ReceiveCoilName               = ft_getopt(cfg.mri, 'ReceiveCoilName'                ); % Information describing the receiver coil. Corresponds to DICOM Tag 0018, 1250 "Receive Coil Name", although not all vendors populate that DICOM Tag, in which case this field can be derived from an appropriate private DICOM field.
cfg.mri.ReceiveCoilActiveElements     = ft_getopt(cfg.mri, 'ReceiveCoilActiveElements'      ); % Information describing the active/selected elements of the receiver coil.  This doesn't correspond to a tag in the DICOM ontology. The vendor-defined terminology for active coil elements can go in this field. As an example, for Siemens, coil channels are typically not activated/selected individually, but rather  in pre-defined selectable "groups" of individual channels, and the list of the  "groups" of elements that are active/selected in any given scan populates  the "Coil String" entry in Siemen's private DICOM fields (e.g., "HEA;HEP" for the Siemens standard 32 ch coil when both the anterior and posterior groups are activated). This is a flexible field that can be used as most appropriate for a given vendor and coil to define the "active" coil elements. Since individual scans can sometimes not have the intended coil elements selected, it is preferable for this field to be populated directly from the DICOM for each individual scan, so that it can be used as a mechanism for checking that a given scan was collected with the intended coil elements selected.
cfg.mri.GradientSetType               = ft_getopt(cfg.mri, 'GradientSetType'                ); % It should be possible to infer the gradient coil from the scanner model. If not,e.g. because of a custom upgrade or use of a gradient insert set, then the specifications of the actual gradient coil should be reported independently.
cfg.mri.MRTransmitCoilSequence        = ft_getopt(cfg.mri, 'MRTransmitCoilSequence'         ); % This is a relevant field if a non-standard transmit coil is used. Corresponds to DICOM Tag 0018, 9049 "MR Transmit Coil Sequence".
cfg.mri.MatrixCoilMode                = ft_getopt(cfg.mri, 'MatrixCoilMode'                 ); % (If used) A method for reducing the number of independent channels by combining in analog the signals from multiple coil elements. There are typically different default modes when using un-accelerated or accelerated (e.g. GRAPPA, SENSE) imaging.
cfg.mri.CoilCombinationMethod         = ft_getopt(cfg.mri, 'CoilCombinationMethod'          ); % Almost all fMRI studies using phased-array coils use root-sum-of-squares (rSOS) combination, but other methods exist. The image reconstruction is changed by the coil combination method (as for the matrix coil mode above), so anything non-standard should be reported.

%% MR Sequence Specifics
cfg.mri.PulseSequenceType             = ft_getopt(cfg.mri, 'PulseSequenceType'              ); % A general description of the pulse sequence used for the scan (i.e. MPRAGE, Gradient Echo EPI, Spin Echo EPI, Multiband gradient echo EPI).
cfg.mri.ScanningSequence              = ft_getopt(cfg.mri, 'ScanningSequence'               ); % Description of the type of data acquired. Corresponds to DICOM Tag 0018, 0020 "Sequence Sequence".
cfg.mri.SequenceVariant               = ft_getopt(cfg.mri, 'SequenceVariant'                ); % Variant of the ScanningSequence. Corresponds to DICOM Tag 0018, 0021 "Sequence Variant".
cfg.mri.ScanOptions                   = ft_getopt(cfg.mri, 'ScanOptions'                    ); % Parameters of ScanningSequence. Corresponds to DICOM Tag 0018, 0022 "Scan Options".
cfg.mri.SequenceName                  = ft_getopt(cfg.mri, 'SequenceName'                   ); % Manufacturer's designation of the sequence name. Corresponds to DICOM Tag 0018, 0024 "Sequence Name".
cfg.mri.PulseSequenceDetails          = ft_getopt(cfg.mri, 'PulseSequenceDetails'           ); % Information beyond pulse sequence type that identifies the specific pulse sequence used (i.e. "Standard Siemens Sequence distributed with the VB17 software," "Siemens WIP ### version #.##," or "Sequence written by X using a version compiled on MM/DD/YYYY").
cfg.mri.NonlinearGradientCorrection   = ft_getopt(cfg.mri, 'NonlinearGradientCorrection'    ); % Boolean stating if the image saved  has been corrected for gradient nonlinearities by the scanner sequence.

%% MR In-Plane Spatial Encoding
cfg.mri.NumberShots                   = ft_getopt(cfg.mri, 'NumberShots'                    ); % The number of RF excitations need to reconstruct a slice or volume. Please mind that  this is not the same as Echo Train Length which denotes the number of lines of k-space collected after an excitation.
cfg.mri.ParallelReductionFactorInPlan = ft_getopt(cfg.mri, 'ParallelReductionFactorInPlane' ); % The parallel imaging (e.g, GRAPPA) factor. Use the denominator of the fraction of k-space encoded for each slice. For example, 2 means half of k-space is encoded. Corresponds to DICOM Tag 0018, 9069 "Parallel Reduction Factor In-plane".
cfg.mri.ParallelAcquisitionTechnique  = ft_getopt(cfg.mri, 'ParallelAcquisitionTechnique'   ); % The type of parallel imaging used (e.g. GRAPPA, SENSE). Corresponds to DICOM Tag 0018, 9078 "Parallel Acquisition Technique".
cfg.mri.PartialFourier                = ft_getopt(cfg.mri, 'PartialFourier'                 ); % The fraction of partial Fourier information collected. Corresponds to DICOM Tag 0018, 9081 "Partial Fourier".
cfg.mri.PartialFourierDirection       = ft_getopt(cfg.mri, 'PartialFourierDirection'        ); % The direction where only partial Fourier information was collected. Corresponds to DICOM Tag 0018, 9036 "Partial Fourier Direction".
cfg.mri.PhaseEncodingDirection        = ft_getopt(cfg.mri, 'PhaseEncodingDirection'         ); % Possible values = [];                     % "i", "j", "k", "i-", "j-", "k-". The letters "i", "j", "k" correspond to the first, second and third axis of the data in the NIFTI file. The polarity of the phase encoding is assumed to go from zero index to maximum index unless "-" sign is present (then the order is reversed - starting from the highest index instead of zero). PhaseEncodingDirection is defined as the direction along which phase is was modulated which may result in visible distortions. Note that this is not the same as the DICOM term InPlanePhaseEncodingDirection which can have "ROW" or "COL" values. This parameter is REQUIRED if corresponding fieldmap data is present or when using multiple runs with different phase encoding directions (which can be later used for field inhomogeneity correction).
cfg.mri.EffectiveEchoSpacing          = ft_getopt(cfg.mri, 'EffectiveEchoSpacing'           ); % The "effective" sampling interval, specified in seconds, between lines in the phase-encoding direction, defined based on the size of the reconstructed image in the phase direction.  It is frequently, but incorrectly, referred to as  "dwell time" (see DwellTime parameter below for actual dwell time).  It is  required for unwarping distortions using field maps. Note that beyond just in-plane acceleration, a variety of other manipulations to the phase encoding need to be accounted for properly, including partial fourier, phase oversampling, phase resolution, phase field-of-view and interpolation. This parameter is REQUIRED if corresponding fieldmap data is present.
cfg.mri.TotalReadoutTime              = ft_getopt(cfg.mri, 'TotalReadoutTime'               ); % This is actually the "effective" total readout time , defined as the readout duration, specified in seconds, that would have generated data with the given level of distortion.  It is NOT the actual, physical duration of the readout train.  If EffectiveEchoSpacing has been properly computed, it is just EffectiveEchoSpacing * (ReconMatrixPE - 1). . This parameter is REQUIRED if corresponding "field/distortion" maps acquired with opposing phase encoding directions are present  (see 8.9.4).
cfg.mri.WaterFatShift                 = ft_getopt(cfg.mri, 'WaterFatShift'                  ); % in pixels, is defined as the displacement of the water signal with respect to fat signal in the image. Water-fat shift (WFS) is expressed in number of pixels (e.g. 3 pixels). The WFS is directly related to the bandwidth per pixel in readout direction, and also to the effective echo spacing.
cfg.mri.EchoTrainLength               = ft_getopt(cfg.mri, 'EchoTrainLength'                ); % number of EPI echoes to acquire one slice. In Siemens, this parameter is the "EPI Factor". Number of lines in k-space acquired per excitation per image.  Corresponds to In GE scanners it corresponds to the DICOM tag (0018,0091) for GE and Siemens; . In Philips, it corresponds to the tag (0019,10d9) for Philips.

%% MR Timing Parameters
cfg.mri.EchoTime                      = ft_getopt(cfg.mri, 'EchoTime'                       ); % The echo time (TE) for the acquisition, specified in seconds. This parameter is REQUIRED if corresponding fieldmap data is present or the data comes from a multi echo sequence. Corresponds to DICOM Tag 0018, 0081 "Echo Time"  (please note that the DICOM term is in milliseconds not seconds).
cfg.mri.InversionTime                 = ft_getopt(cfg.mri, 'InversionTime'                  ); % The inversion time (TI) for the acquisition, specified in seconds. Inversion time is the time after the middle of inverting RF pulse to middle of excitation pulse to detect the amount of longitudinal magnetization. Corresponds to DICOM Tag 0018, 0082 "Inversion Time"  (please note that the DICOM term is in milliseconds not seconds).
cfg.mri.SliceTiming                   = ft_getopt(cfg.mri, 'SliceTiming'                    ); % The time at which each slice was acquired within each volume (frame) of  the acquisition.  Slice timing is not slice order -- rather, it  is a list of times (in JSON format) containing the time (in seconds) of each slice acquisition in relation to the beginning of volume acquisition.  The list goes through the slices along the slice axis in the slice encoding dimension (see below). Note that to ensure the proper interpretation of the SliceTiming field, it is important to check if the (optional) SliceEncodingDirection exists. In particular,  if SliceEncodingDirection is negative, the entries in SliceTiming are defined in reverse order with respect to the slice axis (i.e., the final entry in the SliceTiming list is the time of acquisition of slice 0). This parameter is REQUIRED for sparse sequences that do not have the DelayTime field set. In addition without this parameter slice time correction will not be possible.
cfg.mri.SliceEncodingDirection        = ft_getopt(cfg.mri, 'SliceEncodingDirection'         ); % Possible values = [];                     % "i", "j", "k", "i-", "j-", "k-" (the axis of the NIfTI data along which slices were acquired, and the direction in which SliceTiming is  defined with respect to). "i", "j", "k" identifiers correspond to the first, second and third axis of the data in the NIfTI file. A "-" sign indicates that the contents of SliceTiming are defined in reverse order -- that is, the first entry corresponds to the slice with the largest index, and the final entry corresponds to slice index zero. When present ,the axis defined by SliceEncodingDirection  needs to be consistent with the "slice_dim" field in the NIfTI header. When absent, the entries in SliceTiming must be in the order of increasing slice index as defined by the NIfTI header.
cfg.mri.DwellTime                     = ft_getopt(cfg.mri, 'DwellTime'                      ); % Actual dwell time (in seconds) of the receiver per point in the readout direction, including any oversampling.  For Siemens, this corresponds to DICOM field (0019,1018) (in ns).   This value is necessary for the (optional) readout distortion correction of anatomicals in the HCP Pipelines.  It also usefully provides a handle on the readout bandwidth, which isn't captured in the other metadata tags.  Not to be confused with "EffectiveEchoSpacing", and the frequent mislabeling of echo spacing (which is spacing in the phase encoding direction) as "dwell time" (which is spacing in the readout direction).

%% MR RF & Contrast
cfg.mri.FlipAngle                     = ft_getopt(cfg.mri, 'FlipAngle'                      ); % Flip angle for the acquisition, specified in degrees. Corresponds to = [];                     % DICOM Tag 0018, 1314 "Flip Angle".

%% MR Slice Acceleration
cfg.mri.MultibandAccelerationFactor   = ft_getopt(cfg.mri, 'MultibandAccelerationFactor'    ); % The multiband factor, for multiband acquisitions.

%% Anatomical landmarks, useful for multimodaltimodal co-registration with MEG, (S)HeadCoil, TMS,etc
cfg.mri.AnatomicalLandmarkCoordinates = ft_getopt(cfg.mri, 'AnatomicalLandmarkCoordinates'  ); % Key:value pairs of any number of additional anatomical landmarks and their coordinates in voxel units (where first voxel has index 0,0,0) relative to the associated anatomical MRI, (e.g. {"AC" = []; % [127,119,149], "PC" = []; % [128,93,141], "IH" = []; % [131,114,206]}, or {"NAS" = []; % [127,213,139], "LPA" = []; % [52,113,96], "RPA" = []; % [202,113,91]}).

%% columns in the channels.tsv
cfg.channels.name               = ft_getopt(cfg.channels, 'name'               , nan);  % REQUIRED. Channel name (e.g., MRT012, MEG023)
cfg.channels.type               = ft_getopt(cfg.channels, 'type'               , nan);  % REQUIRED. Type of channel; MUST use the channel types listed below.
cfg.channels.units              = ft_getopt(cfg.channels, 'units'              , nan);  % REQUIRED. Physical unit of the data values recorded by this channel in SI (see Appendix V: Units for allowed symbols).
cfg.channels.description        = ft_getopt(cfg.channels, 'description'        , nan);  % OPTIONAL. Brief free-text description of the channel, or other information of interest. See examples below.
cfg.channels.sampling_frequency = ft_getopt(cfg.channels, 'sampling_frequency' , nan);  % OPTIONAL. Sampling rate of the channel in Hz.
cfg.channels.low_cutoff         = ft_getopt(cfg.channels, 'low_cutoff'         , nan);  % OPTIONAL. Frequencies used for the high-pass filter applied to the channel in Hz. If no high-pass filter applied, use n/a.
cfg.channels.high_cutoff        = ft_getopt(cfg.channels, 'high_cutoff'        , nan);  % OPTIONAL. Frequencies used for the low-pass filter applied to the channel in Hz. If no low-pass filter applied, use n/a. Note that hardware anti-aliasing in A/D conversion of all MEG/EEG electronics applies a low-pass filter; specify its frequency here if applicable.
cfg.channels.notch              = ft_getopt(cfg.channels, 'notch'              , nan);  % OPTIONAL. Frequencies used for the notch filter applied to the channel, in Hz. If no notch filter applied, use n/a.
cfg.channels.software_filters   = ft_getopt(cfg.channels, 'software_filters'   , nan);  % OPTIONAL. List of temporal and/or spatial software filters applied (e.g. "SSS", "SpatialCompensation"). Note that parameters should be defined in the general MEG sidecar .json file. Indicate n/a in the absence of software filters applied.
cfg.channels.status             = ft_getopt(cfg.channels, 'status'             , nan);  % OPTIONAL. Data quality observed on the channel (good/bad). A channel is considered bad if its data quality is compromised by excessive noise. Description of noise type SHOULD be provided in [status_description].
cfg.channels.status_description = ft_getopt(cfg.channels, 'status_description' , nan);  % OPTIONAL. Freeform text description of noise or artifact affecting data quality on the channel. It is meant to explain why the channel was declared bad in [status].

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%% read the information from the dataset
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

% convert dataset to headerfile and datafile
if nargin>1
  % input data was specified
  varargin{1} = ft_checkdata(varargin{1}, 'datatype', {'raw', 'volume'});
  typ = ft_datatype(varargin{1});
else
  % data should be read from disk
  cfg = ft_checkconfig(cfg, 'dataset2files', 'yes');
  typ = ft_filetype(cfg.headerfile);
end

% determine the sidecar files that are required
need_mri_json         = false;
need_meg_json         = false;
need_eeg_json         = false;
need_ieeg_json        = false;

switch typ
  case {'nifti', 'nifti2', 'nifti_fsl'}
    mri = ft_read_mri(cfg.dataset);
    if ~isempty(cfg.mri.dicomfile)
      % read the header details from the matching DICOM file specified by the user
      dcm = dicominfo(cfg.mri.dicomfile);
    else
      dcm = [];
    end
    need_mri_json = true;

  case 'dicom'
    mri = ft_read_mri(cfg.dataset);
    dcm = dicominfo(cfg.dataset);
    need_mri_json = true;

  case 'volume'
    % the input data structure represents imaging data
    mri = varargin{1};
    if ~isempty(cfg.mri.dicomfile)
      % read the header details from the dicom matching file that was specified by the user
      dcm = dicominfo(cfg.mri.dicomfile);
    elseif isfield(mri, 'hdr') && numel(mri.hdr)>1
      % it looks like an MRI read in using FT_READ_MRI using the FreeSurfer code
      % take the DICOM details from the first slice
      dcm = mri.hdr(1);
    else
      dcm = [];
    end
    need_mri_json = true;

  case {'ctf_ds', 'ctf_meg4', 'ctf_res4', 'ctf151', 'ctf275', 'neuromag_fif', 'neuromag122', 'neuromag306'}
    % it is MEG data from disk and in a supported format
    hdr = ft_read_header(cfg.headerfile, 'checkmaxfilter', false);
    if isempty(cfg.trigger.event)
      trigger = ft_read_event(cfg.datafile, 'header', hdr);
    else
      % use the triggers as specified in the cfg
      trigger = cfg.trigger.event;
    end
    if ~isequal(cfg.dataset, cfg.outputfile)
      % the data should be converted and written to disk
      dat = ft_read_data(cfg.datafile, 'header', hdr, 'checkboundary', false, 'begsample', 1, 'endsample', hdr.nSamples*hdr.nTrials);
    end
    need_meg_json = true;

  case {'brainvision_vhdr', 'edf', 'eeglab_set'}
    % it is EEG data from disk and in a supported format
    hdr = ft_read_header(cfg.headerfile, 'checkmaxfilter', false);
    if isempty(cfg.trigger.event)
      trigger = ft_read_event(cfg.datafile, 'header', hdr);
    else
      % use the triggers as specified in the cfg
      trigger = cfg.trigger.event;
    end
    if ~isequal(cfg.dataset, cfg.outputfile)
      % the data should be converted and written to disk
      dat = ft_read_data(cfg.datafile, 'header', hdr, 'checkboundary', false, 'begsample', 1, 'endsample', hdr.nSamples*hdr.nTrials);
    end
    need_eeg_json = true;

  case 'raw'
    % the input data structure represents raw electrophysiology data, which will be written in BrainVision format
    ft_warning('assuming that the input data structure represents EEG');
    need_eeg_json = true;

    % the data is not on disk, but has been passed as input argument
    % it should be written to disk in a supported file format
    hdr = ft_fetch_header(varargin{1});
    dat = ft_fetch_data(varargin{1}, 'checkboundary', false, 'begsample', 1, 'endsample', hdr.nSamples*hdr.nTrials);

    if isempty(cfg.trigger.event)
      trigger = ft_fetch_event(varargin{1});
    else
      % use the triggers as specified in the cfg
      trigger = cfg.trigger.event;
    end
    if ft_senstype(varargin{1}, 'ctf') || ft_senstype(varargin{1}, 'neuromag')
      % use the subsequent MEG-specific handling for the JSON and TSV sidecar files
      typ = ft_senstype(varargin{1});
    end

  otherwise
    % it is EEG data from disk but not in a supported format
    ft_warning('assuming that the dataset represents EEG');
    assert(~isempty(cfg.outputfile) && ~isequal(cfg.dataset, cfg.outputfile), 'unsupported dataset format for BIDS, you should specify cfg.outputfile');

    % it will be written to disk in BrainVision format
    hdr = ft_read_header(cfg.headerfile, 'checkmaxfilter', false);
    dat = ft_read_data(cfg.datafile, 'header', hdr, 'checkboundary', false, 'begsample', 1, 'endsample', hdr.nSamples*hdr.nTrials);

    if isempty(cfg.trigger.event)
      trigger = ft_read_event(cfg.datafile, 'header', hdr);
    else
      % use the triggers as specified in the cfg
      trigger = cfg.trigger.event;
    end
    need_eeg_json = true;

end % switch typ

need_events_tsv       = need_meg_json || need_eeg_json || need_ieeg_json || (need_mri_json && contains(cfg.outputfile, 'task'));
need_channels_tsv     = need_meg_json || need_eeg_json || need_ieeg_json;
need_coordsystem_json = need_meg_json; % FIXME also needed when EEG and iEEG electrodes are present

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%% get the defaults and user-specified settings for each possible sidecar file
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

% start with empty metadata descriptions
mri_json      = [];
meg_json      = [];
eeg_json      = [];
ieeg_json     = [];
events_tsv    = [];
channels_tsv  = [];
coordsystem_json = [];

% make the relevant selection, all json fields start with a capital letter
fn = fieldnames(cfg);
fn = fn(~cellfun(@isempty, regexp(fn, '[A-Z].*')));
generic_defaults = keepfields(cfg, fn);

% make the relevant selection, all json fields start with a capital letter
fn = fieldnames(cfg.mri);
fn = fn(~cellfun(@isempty, regexp(fn, '[A-Z].*')));
mri_defaults = keepfields(cfg.mri, fn);

% make the relevant selection, all json fields start with a capital letter
fn = fieldnames(cfg.meg);
fn = fn(~cellfun(@isempty, regexp(fn, '[A-Z].*')));
meg_defaults = keepfields(cfg.meg, fn);

% make the relevant selection, all json fields start with a capital letter
fn = fieldnames(cfg.eeg);
fn = fn(~cellfun(@isempty, regexp(fn, '[A-Z].*')));
eeg_defaults = keepfields(cfg.eeg, fn);

% make the relevant selection, all json fields start with a capital letter
fn = fieldnames(cfg.coordsystem);
fn = fn(~cellfun(@isempty, regexp(fn, '[A-Z].*')));
coordsystem_defaults = keepfields(cfg.coordsystem, fn);

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%% construct the json and tsv files
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

%% need_mri_json
if need_mri_json
  % get the available information from the DICOM header
  mri_json = keepfields(dcm, fn);
  % merge the information from the defaults with the information obtained from the data
  % in case fields appear in both, the first input overrules the second
  mri_json = mergeconfig(mri_json, mri_defaults, false);
  mri_json = mergeconfig(mri_json, generic_defaults, false);
end % if need_mri_json

%% need_meg_json
if need_meg_json
  % these MUST be present
  meg_json.SamplingFrequency          = hdr.Fs;
  % these SHOULD be present
  meg_json.MEGChannelCount            = sum(strcmp(hdr.chantype, 'megmag') | strcmp(hdr.chantype, 'meggrad') | strcmp(hdr.chantype, 'megplanar') | strcmp(hdr.chantype, 'megaxial'));
  meg_json.MEGREFChannelCount         = sum(strcmp(hdr.chantype, 'refmag') | strcmp(hdr.chantype, 'refgrad') | strcmp(hdr.chantype, 'refplanar') | strcmp(hdr.chantype, 'ref'));
  meg_json.EEGChannelCount            = sum(strcmp(hdr.chantype, 'eeg'));
  meg_json.ECOGChannelCount           = sum(strcmp(hdr.chantype, '???')); % FIXME
  meg_json.SEEGChannelCount           = sum(strcmp(hdr.chantype, '???')); % FIXME
  meg_json.EOGChannelCount            = sum(strcmp(hdr.chantype, 'eog'));
  meg_json.ECGChannelCount            = sum(strcmp(hdr.chantype, 'ecg'));
  meg_json.EMGChannelCount            = sum(strcmp(hdr.chantype, 'emg'));
  meg_json.MiscChannelCount           = sum(strcmp(hdr.chantype, 'misc'));
  meg_json.TriggerChannelCount        = sum(strcmp(hdr.chantype, 'trigger'));
  meg_json.RecordingDuration          = (hdr.nTrials*hdr.nSamples)/hdr.Fs;
  meg_json.EpochLength                = hdr.nSamples/hdr.Fs;
  if ft_senstype(hdr.grad, 'ctf151')
    meg_json.ContinuousHeadLocalization = any(strcmp(hdr.chantype, 'headloc')); % CTF specific
    meg_json.Manufacturer             = 'CTF';
    meg_json.ManufacturersModelName   = 'CTF-151';
  elseif ft_senstype(hdr.grad, 'ctf275')
    meg_json.ContinuousHeadLocalization = any(strcmp(hdr.chantype, 'headloc')); % CTF specific
    meg_json.Manufacturer             = 'CTF';
    meg_json.ManufacturersModelName   = 'CTF-275';
  elseif ft_senstype(hdr.grad, 'neuromag122')
    meg_json.Manufacturer             = 'Elekta/Neuromag';
    meg_json.ManufacturersModelName   = 'Neuromag-122';
  elseif ft_senstype(hdr.grad, 'neuromag306')
    meg_json.Manufacturer             = 'Elekta/Neuromag';
    % meg_json.ManufacturersModelName can not be determined, since both have 306 channels
  end

  % merge the information from the defaults with the information obtained from the data
  % in case fields appear in both, the first input overrules the second
  meg_json = mergeconfig(meg_json, meg_defaults, false);
  meg_json = mergeconfig(meg_json, generic_defaults, false);
end % if need_meg_json

%% need_eeg_json
if need_eeg_json
  % these MUST be present
  eeg_json.SamplingFrequency          = hdr.Fs;
  % these SHOULD be present
  eeg_json.EEGChannelCount            = sum(strcmp(hdr.chantype, 'eeg'));
  eeg_json.EOGChannelCount            = sum(strcmp(hdr.chantype, 'eog'));
  eeg_json.ECGChannelCount            = sum(strcmp(hdr.chantype, 'ecg'));
  eeg_json.EMGChannelCount            = sum(strcmp(hdr.chantype, 'emg'));
  eeg_json.TriggerChannelCount        = sum(strcmp(hdr.chantype, 'trigger'));
  eeg_json.MiscChannelCount           = sum(strcmp(hdr.chantype, 'misc') | strcmp(hdr.chantype, 'unknown'));
  eeg_json.RecordingDuration          = (hdr.nTrials*hdr.nSamples)/hdr.Fs;
  eeg_json.EpochLength                = hdr.nSamples/hdr.Fs;

  % merge the information from the defaults with the information obtained from the data
  % in case fields appear in both, the first input overrules the second
  eeg_json = mergeconfig(eeg_json, eeg_defaults, false);
  eeg_json = mergeconfig(eeg_json, generic_defaults, false);
end % if need_eeg_json

%% need_coordsystem_json
if need_coordsystem_json
  if ft_senstype(hdr.grad, 'ctf')
    % coordinate system for MEG sensors
    coordsystem_json.MEGCoordinateSystem            = 'CTF';
    coordsystem_json.MEGCoordinateUnits             = 'cm';
    coordsystem_json.MEGCoordinateSystemDescription = 'CTF head coordinates, orientation ALS, origin between the ears';

    % coordinate system for head localization coils
    coordsystem_json.HeadCoilCoordinates                 = []; % see below
    coordsystem_json.HeadCoilCoordinateSystem            = 'CTF';
    coordsystem_json.HeadCoilCoordinateUnits             = 'cm';
    coordsystem_json.HeadCoilCoordinateSystemDescription = 'CTF head coordinates, orientation ALS, origin between the ears';
    if isempty(coordsystem_json.HeadCoilCoordinates)
      % get the positions from the dataset header
      label = cellstr(hdr.orig.hc.names);
      position = hdr.orig.hc.head;
      for i=1:numel(label)
        coordsystem_json.HeadCoilCoordinates.(fixname(label{i})) = position(:,i)';
      end
    end
    % merge the information from the defaults with the information obtained from the data
    % in case fields appear in both, the first input overrules the second
    coordsystem_json = mergeconfig(coordsystem_defaults, coordsystem_json, false); % FIXME the order of precedence is different here
  else
    ft_warning('coordsystem handling not yet supported for %s', ft_senstype(hdr.grad));
  end
end % if need_coordsystem_json

%% need_channels_tsv
if need_channels_tsv
  % ensure that all channels have the correct details
  fn = {'name' 'type' 'units' 'description' 'sampling_frequency' 'low_cutoff' 'high_cutoff' 'notch' 'software_filters' 'status' 'status_description'};
  for i=1:numel(fn)
    if numel(cfg.channels.(fn{i}))==1
      cfg.channels.(fn{i}) = repmat(cfg.channels.(fn{i}), hdr.nChans, 1);
    end
  end

  % EEG and MEG data should also have a channels.tsv file
  name                = mergevector(hdr.label(:),    cfg.channels.name);
  type                = mergevector(hdr.chantype(:), cfg.channels.type);
  units               = mergevector(hdr.chanunit(:), cfg.channels.units);
  sampling_frequency  = mergevector(repmat(hdr.Fs, hdr.nChans, 1), cfg.channels.sampling_frequency);

  % construct a table with the corresponding columns
  % FIXME there are more columns that should be added
  channels_tsv = table(name, type, units, sampling_frequency);
end

%% need_events_tsv
if need_events_tsv
  % read the presentation file that may accompany the functional data
  if ~isempty(cfg.presentationfile)
    presentation = ft_read_event(cfg.presentationfile);
  else
    presentation = [];
  end

  if need_mri_json

    if isempty(presentation)
      ft_warning('cfg.presentationfile not specified, cannot determine events')

      onset    = [];
      duration = [];
      events_tsv = table(onset, duration);

    else
      % align the events from the presentation log file with the MR volumes
      % this requires one event per volume in the presentation file

      % merge the information with the json sidecar file
      % in case fields appear in both, the first input overrules the second
      tmp = mergeconfig(mri_json, read_json(corresponding_json(cfg.outputfile)), false);
      assert(~isempty(tmp.RepetitionTime), 'you must specify cfg.mri.RepetitionTime');

      % create a header structure that represents the fMRI timeseries
      hdr.Fs = 1/tmp.RepetitionTime;
      hdr.nSamples = mri.dim(4);

      % create a event structure with one event for each fMRI volume
      volume = [];
      for i=1:hdr.nSamples
        volume(i).type   = 'volume';
        volume(i).sample = i;
      end

      % find the presentation events corresponding to each volume
      selpres = select_event(presentation, cfg.presentation.eventtype, cfg.presentation.eventvalue);
      selpres = presentation(selpres);

      ft_info('%d volumes, %d presentation events', length(volume), length(selpres));
      if length(volume)>length(selpres)
        % this happens when the scanner keeps running while presentation has already been stopped
        n = length(volume)-length(selpres);
        ft_warning('discarding last %d volumes for realignment of events', n);
        volume = volume(1:end-n);
      elseif length(selpres)>length(volume)
        % this happens when DICOM volumes that are represented in the
        % presentation log have been deleted from disk
        switch cfg.presentation.skip
          case 'first'
            ft_warning('discarding first %d presentation events for realignment of events', n);
            selpres = selpres((n+1):end);
          case 'last'
            ft_warning('discarding last %d presentation events for realignment of events', n);
            selpres = selpres(1:end-n);
          case 'none'
            ft_error('not enough volumes to match the presentation events');
        end % case
      end

      %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
      % the following code is largely shared between the MEG and MRI section
      %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

      % predict the sample number from the timestamp
      model     = polyfit([selpres.timestamp], [volume.sample], 1);
      estimated = polyval(model, [selpres.timestamp]);

      if istrue(cfg.feedback)
        [p, f, x] = fileparts(cfg.dataset);
        figure('name', ['PRESENTATION - ' f]);
        subplot(2,1,1)
        hold on
        % presentation timestamps are expressed in units of 0.1 miliseconds
        plot([selpres.timestamp]/1e4, [volume.sample], 'b.')
        plot([selpres.timestamp]/1e4, estimated, 'ro')
        xlabel('presentation time (s)')
        ylabel('MRI volumes')
        legend({'observed', 'predicted'})

        subplot(2,1,2)
        plot([selpres.timestamp]/1e4, ([volume.sample]-estimated)/hdr.Fs, 'g.')
        xlabel('presentation time (s)')
        ylabel('difference (s)')
      end

      % estimate the time in seconds of all presentation events
      estimated = polyval(model, [presentation.timestamp]);
      estimated = round(1000*estimated)/1000; % round to three decimals
      for i=1:numel(estimated)
        presentation(i).sample = estimated(i);
      end
      % convert the event structure to a TSV table
      presentation_tsv = event2table(hdr, presentation);
      % rename the column to "volume" instead of "sample"
      sel = strcmp(presentation_tsv.Properties.VariableNames, 'sample');
      presentation_tsv.Properties.VariableNames{sel} = 'volume';

      % for fMRI the presentation log file is the only source of events
      events_tsv = presentation_tsv;
      clear presentation_tsv selpres volume

      % sort ascending on the onset of each event
      events_tsv = sortrows(events_tsv, 'onset');
    end

  elseif need_meg_json || need_eeg_json || need_ieeg_json
    % merge the events from the trigger channel with those from the (optional) presentation file

    if istable(cfg.events.trl)
      % check that the column names are valid
      assert(stcmp(cfg.events.trl.Properties.VariableNames{1}, 'begsample'));
      assert(stcmp(cfg.events.trl.Properties.VariableNames{2}, 'endsample'));
      assert(stcmp(cfg.events.trl.Properties.VariableNames{3}, 'offset'));
      % use the events table as it is
      events_tsv = cfg.events.trl;
    elseif ~isempty(cfg.events.trl)
      % convert the trl matrix to an events table
      begsample = cfg.events.trl(:,1);
      endsample = cfg.events.trl(:,2);
      offset    = cfg.events.trl(:,3); % FIXME this is not used
      if any(offset~=0)
        ft_warning('the offset in the trl matrix is ignored');
      end
      if size(trl,2)>3
        ft_warning('additional columns in the trl matrix are ignored');
      end
      % convert to the required fields
      onset     = (begsample-1)/hdr.Fs;
      duration  = (endsample-begsample+1)/hdr.Fs;
      events_tsv = table(onset, duration);
    else
      % convert the events from the dataset into a table
      events_tsv = event2table(hdr, trigger);
    end

    if ~isempty(presentation) && ~isempty(trigger)
      % align the events from the presentation log file with the triggers

      % select the correspopnding triggers and events in the presentation file
      seltrig = select_event(trigger,      cfg.trigger.eventtype,      cfg.trigger.eventvalue);
      selpres = select_event(presentation, cfg.presentation.eventtype, cfg.presentation.eventvalue);
      seltrig = trigger(seltrig);
      selpres = presentation(selpres);

      %if length(seltrig)~=length(selpres)
      %  ft_error('inconsistent number: %d triggers, %d presentation events', length(seltrig), length(selpres));
      %end

      ft_info('%d triggers, %d presentation events', length(seltrig), length(selpres));
      if length(seltrig)>length(selpres)
        % don't know how to solve this
        ft_error('inconsistent number: %d triggers, %d presentation events', length(seltrig), length(selpres));
      elseif length(selpres)>length(seltrig)
        n = length(selpres)-length(seltrig);
        % This could happen when due to acquisition problems there is more than one
        % *.ds directory. If this is a known case, cfg.presentation.skip can be used.
        % Note that this only works, if there are two ds-datasets (not more).
        switch cfg.presentation.skip
          case 'first'
            ft_warning('discarding first %d presentation events for realignment of events', n);
            selpres = selpres((n+1):end);
          case 'last'
            ft_warning('discarding last %d presentation events for realignment of events', n);
            selpres = selpres(1:end-n);
          case 'none'
            ft_error('not enough triggers to match the presentation events');
        end % case
      end

      %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
      % the following code is largely shared between the MEG and MRI section
      %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

      % predict the presentation sample number from the presentation timestamp
      model     = polyfit([selpres.timestamp], [seltrig.sample], 1);
      estimated = polyval(model, [selpres.timestamp]);

      if istrue(cfg.feedback)
        [p, f, x] = fileparts(cfg.dataset);
        figure('name', ['PRESENTATION - ' f]);
        subplot(2,1,1)
        hold on
        % presentation timestamps are expressed in units of 0.1 miliseconds
        plot([selpres.timestamp]/1e4, [seltrig.sample], 'b.')
        plot([selpres.timestamp]/1e4, estimated, 'ro')
        xlabel('presentation time (s)')
        ylabel('data samples')
        legend({'observed', 'predicted'})

        subplot(2,1,2)
        plot([selpres.timestamp]/1e4, ([seltrig.sample]-estimated)/hdr.Fs, 'g.')
        xlabel('presentation time (s)')
        ylabel('difference (s)')
      end

      % estimate the sample number and time in seconds of all presentation events
      estimated = polyval(model, [presentation.timestamp]);
      estimated = round(estimated); % round to the nearest sample
      for i=1:numel(estimated)
        presentation(i).sample = estimated(i);
      end
      % convert the event structure to a TSV table
      presentation_tsv = event2table(hdr, presentation);

      % the events from the the presentation log file should be merged with the triggers
      % trigger values are often numeric, whereas presentation event values are often strings
      if isnumeric(events_tsv.value) && ~isnumeric(presentation_tsv.value)
        % convert them, otherwise the concatenation fails
        events_tsv.value = num2cell(events_tsv.value);
      end
      % concatenate them
      events_tsv = [events_tsv; presentation_tsv];

      clear presentation_tsv selpres seltrig
    end
  end

  if ~isempty(events_tsv)
      % sort the events ascending on the onset
      events_tsv = sortrows(events_tsv, 'onset');
  end
end % if need_events

% remove fields that have an empty value
mri_json  = remove_empty(mri_json);
meg_json  = remove_empty(meg_json);
eeg_json  = remove_empty(eeg_json);
ieeg_json = remove_empty(ieeg_json);

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%% write the data to the output file
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
if ~isequal(cfg.dataset, cfg.outputfile) || istrue(cfg.mri.deface)
  switch typ
    case {'nifti', 'nifti2', 'nifti_fsl', 'dicom', 'volume'}
      [p, f, x] = fileparts(cfg.outputfile);
      if ~isequal(x, '.nii')
        cfg.outputfile = fullfile(p, [f '.nii']);
      end
      if istrue(cfg.mri.deface)
        % deface the anatomical MRI
        ft_info('defacing anatomical MRI');
        if istrue(cfg.feedback)
          tmpcfg = [];
          tmpcfg.figurename = sprintf('ORIGINAL - %s', f);
          tmpcfg.showcallinfo = 'no';
          tmpcfg.colorbar = 'no';
          ft_sourceplot(tmpcfg, mri);
        end
        tmpcfg = [];
        tmpcfg.method = 'spm';
        if ~isfield(mri, 'coordsys')
          % try to determine it from the BIDS filename
          coordsys = get_token(cfg.outputfile, 'space');
          if ~isempty(coordsys)
            mri.coordsys = coordsys;
          else
            mri.coordsys = 'unknown';
          end
        end
        mri = ft_defacevolume(tmpcfg, mri);
        if istrue(cfg.feedback)
          tmpcfg = [];
          tmpcfg.figurename = sprintf('DEFACED - %s', f);
          tmpcfg.showcallinfo = 'no';
          tmpcfg.colorbar = 'no';
          ft_sourceplot(tmpcfg, mri);
        end
      end
      ft_info('writing %s\n', cfg.outputfile);
      ft_write_mri(cfg.outputfile, mri, 'dataformat', 'nifti');

    otherwise
      if not(istrue(cfg.keepnative))
        [p, f, x] = fileparts(cfg.outputfile);
        if ~isequal(x, '.vhdr')
          cfg.outputfile = fullfile(p, [f '.vhdr']);
        end
        ft_info('writing %s\n', cfg.outputfile);
        ft_write_data(cfg.outputfile, dat, 'dataformat', 'brainvision_eeg', 'header', hdr, 'event', trigger);
      else
        [~, ~, xin] = fileparts(cfg.dataset);
        [~, ~, xout] = fileparts(cfg.outputfile);
        if strcmp(xin, xout)
          copyfile(cfg.dataset, cfg.outputfile);
        else
          ft_error('input and output filename extension don''t match. Cannot use keepnative.');
        end
      end
  end
end

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%% write the metadata to the json and tsv files
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

if ~isempty(mri_json)
  filename = corresponding_json(cfg.outputfile);ls
  if isfile(filename)
    existing = read_json(filename);
  else
    existing = [];
  end
  switch cfg.mri.writesidecar
    case 'yes'
      if ~isempty(existing)
        ft_warning('not overwriting the existing and non-empty file ''%s''', filename);
      else
        write_json(filename, mri_json);
      end
    case 'replace'
      write_json(filename, mri_json);
    case 'merge'
      write_json(filename, mergeconfig(mri_json, existing, false))
    case 'no'
      % do nothing
    otherwise
      ft_error('incorrect option for cfg.mri.writesidecar');
  end % switch
end

if ~isempty(meg_json)
  filename = corresponding_json(cfg.outputfile);
  if isfile(filename)
    existing = read_json(filename);
  else
    existing = [];
  end
  switch cfg.meg.writesidecar
    case 'yes'
      if ~isempty(existing)
        ft_warning('not overwriting the existing and non-empty file ''%s''', filename);
      else
        write_json(filename, meg_json);
      end
    case 'replace'
      write_json(filename, meg_json);
    case 'merge'
      write_json(filename, mergeconfig(meg_json, existing, false));
    case 'no'
      % do nothing
    otherwise
      ft_error('incorrect option for cfg.meg.writesidecar');
  end % switch
end

if ~isempty(eeg_json)
  filename = corresponding_json(cfg.outputfile);
  if isfile(filename)
    existing = read_json(filename);
  else
    existing = [];
  end
  switch cfg.eeg.writesidecar
    case 'yes'
      if ~isempty(existing)
        ft_warning('not overwriting the existing and non-empty file ''%s''', filename);
      else
        write_json(filename, eeg_json);
      end
    case 'replace'
      write_json(filename, eeg_json);
    case 'merge'
      write_json(filename, mergeconfig(eeg_json, existing, false));
    case 'no'
      % do nothing
    otherwise
      ft_error('incorrect option for cfg.eeg.writesidecar');
  end % switch
end

if ~isempty(ieeg_json)
  filename = corresponding_json(cfg.outputfile);
  if isfile(filename)
    existing = read_json(filename);
  else
    existing = [];
  end
  switch cfg.ieeg.writesidecar
    case 'yes'
      if ~isempty(existing)
        ft_warning('not overwriting the existing and non-empty file ''%s''', filename);
      else
        write_json(filename, ieeg_json);
      end
    case 'replace'
      write_json(filename, ieeg_json);
    case 'merge'
      write_json(filename, mergeconfig(ieeg_json, existing, false));
    case 'no'
      % do nothing
    otherwise
      ft_error('incorrect option for cfg.ieeg.writesidecar');
  end % switch
end

if ~isempty(coordsystem_json)
  [p, f, x] = fileparts(cfg.outputfile);
  f = remove_token(f, 'datatype'); % remove _bold, _meg, etc.
  f = remove_token(f, 'task');     % remove _task-something
  f = remove_token(f, 'run');      % remove _run-something
  f = remove_token(f, 'acq');      % remove _acq-something
  f = remove_token(f, 'proc');     % remove _proc-something
  filename = fullfile(p, [f '_coordsystem.json']);
  if isfile(filename)
    existing = read_json(filename);
  else
    existing = [];
  end
  switch cfg.coordsystem.writesidecar
    case 'yes'
      if ~isempty(existing)
        ft_warning('not overwriting the existing and non-empty file ''%s''', filename);
      else
        write_json(filename, coordsystem_json);
      end
    case 'replace'
      write_json(filename, coordsystem_json);
    case 'merge'
      write_json(filename, mergeconfig(coordsystem_json, existing, false))
    case 'no'
      % do nothing
    otherwise
      ft_error('incorrect option for cfg.coordsystem.writesidecar');
  end % switch
end

if ~isempty(channels_tsv)
  [p, f, x] = fileparts(cfg.outputfile);
  f = remove_token(f, 'datatype'); % remove _bold, _meg, etc.
  filename = fullfile(p, [f '_channels.tsv']);
  if isfile(filename)
    existing = read_tsv(filename);
  else
    existing = [];
  end
  switch cfg.channels.writesidecar
    case 'yes'
      if ~isempty(existing)
        ft_warning('not overwriting the existing and non-empty file ''%s''', filename);
      else
        write_tsv(filename, channels_tsv);
      end
    case 'replace'
      write_tsv(filename, channels_tsv);
    case 'merge'
      if ~isempty(existing)
        channels_tsv = unique(cat(1, channels_tsv, existing));
      end
      write_tsv(filename, channels_tsv);
    case 'no'
      % do nothing
    otherwise
      ft_error('incorrect option for cfg.channels.writesidecar');
  end % switch
end

if ~isempty(events_tsv)
  [p, f, x] = fileparts(cfg.outputfile);
  f = remove_token(f, 'datatype'); % remove _bold, _meg, etc.
  filename = fullfile(p, [f '_events.tsv']);
  if isfile(filename)
    existing = read_tsv(filename);
  else
    existing = [];
  end
  switch cfg.events.writesidecar
    case 'yes'
      if ~isempty(existing)
        ft_warning('not overwriting the existing and non-empty file ''%s''', filename);
      else
        write_tsv(filename, events_tsv);
      end
    case 'replace'
      write_tsv(filename, events_tsv);
    case 'merge'
      if ~isempty(existing)
        events_tsv = unique(cat(1, events_tsv, existing));
      end
      write_tsv(filename, events_tsv);
    case 'no'
      % do nothing
    otherwise
      ft_error('incorrect option for cfg.events.writesidecar');
  end % switch
end

% do not return an output variable if not requested
if ~ft_nargout
  clear cfg
end

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% SUBFUNCTION
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function val = get_token(f, tok)
% ensure that it is only the filename
[p, f, x] = fileparts(f);
pieces = tokenize(f, '_');
val = '';
for i=1:numel(pieces)
  if startsWith([pieces{i} '-'], tok)
    val = pieces{i}((numel(tok)+2):end);
    return
  end
end

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% SUBFUNCTION
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function f = remove_token(f, tok)
switch tok
  case 'datatype'
    typ = {'_T1w', '_T2w', '_dwi', '_bold', '_meg', '_eeg', '_ieeg'};
    for i=1:numel(typ)
      if endsWith(f, typ{i})
        f = f(1:end-length(typ{i}));
        return
      end
    end
  otherwise
    part = regexp(f, sprintf('_%s-[a-zA-Z0-9]*', tok), 'split');
    if numel(part)>1 && ~isempty(part{2})
      f = [part{1} part{2}];
    else
      f = part{1};
    end
end % switch


%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% SUBFUNCTION convert event structure into table
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function tab = event2table(hdr, trigger)
if isempty(trigger)
  tab = [];
else
  for i=1:numel(trigger)
    % normalize missing values
    if isempty(trigger(i).sample)
      trigger(i).sample = NaN;
    end
    if isempty(trigger(i).duration)
      trigger(i).duration = NaN;
    end
    if isempty(trigger(i).value)
      trigger(i).value = NaN;
    end
  end
  onset        = (([trigger.sample]-1)/hdr.Fs)';   % in seconds
  duration     = ([trigger.duration]/hdr.Fs)';     % in seconds
  sample       = ([trigger.sample])';              % in samples, the first sample of the file is 1
  type         = {trigger.type}';
  value        = {trigger.value}';
  if all(cellfun(@isnumeric, type))
    % this can be an array of strings or values
    type = cell2mat(type);
  end
  if all(cellfun(@isnumeric, value))
    % this can be an array of strings or values
    value = cell2mat(value);
  end
  tab = table(onset, duration, sample, type, value);
end

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% SUBFUNCTION
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function tsv = read_tsv(filename)
ft_info('reading %s\n', filename);
tsv = readtable(filename, 'Delimiter', 'tab', 'FileType', 'text', 'TreatAsEmpty', 'n/a', 'ReadVariableNames', true);

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% SUBFUNCTION
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function write_tsv(filename, tsv)
ft_info('writing %s\n', filename);
fn = tsv.Properties.VariableNames;
for i=1:numel(fn)
  val = [tsv.(fn{i})];
  if isnumeric(val) && any(isnan(val))
    sel = isnan(val);
    val = num2cell(val);
    val(sel) = {'n/a'};
    % replace the numeric value NaN with the string 'n/a'
    tsv.(fn{i}) = val;
  elseif iscell(val)
    for j=1:numel(val)
      if isnan(val{j})
        val{j} = 'n/a';
      end
    end
    % replace the numeric value NaN with the string 'n/a'
    tsv.(fn{i}) = val;
  end
end
writetable(tsv, filename, 'Delimiter', 'tab', 'FileType', 'text');

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% SUBFUNCTION
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function json = read_json(filename)
ft_info('reading %s\n', filename);
ft_hastoolbox('jsonlab', 1);
json = loadjson(filename);
json = ft_struct2char(json); % convert strings into char-arrays

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% SUBFUNCTION
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function write_json(filename, json)
json = remove_empty(json);
json = sort_fields(json);
json = ft_struct2char(json); % convert strings into char-arrays
ft_info('writing %s\n', filename);
ft_hastoolbox('jsonlab', 1);
% write nan as 'n/a'
% write boolean as True/False
str = savejson('', json, 'NaN', 'n/a', 'ParseLogical', true);
fid = fopen(filename, 'w');
fwrite(fid, str);
fclose(fid);

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% SUBFUNCTION
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function filename = corresponding_json(filename)
[p, f, x] = fileparts(filename);
if isequal(x, '.gz') && endsWith(f, '.nii')
  % it is a gzip compressed nifti file
  f = f(1:end-4);
  x = '.nii.gz';
end
filename = fullfile(p, [f '.json']);

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% SUBFUNCTION
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function x = mergevector(x, y)
assert(isequal(size(x), size(y)));
for i=1:numel(x)
  if isnumeric(x) && isnumeric(y) && isnan(x(i)) && ~isnan(y(i))
    x(i) = y(i);
  end
  if iscell(x) && iscell(y) && isempty(x{i}) && ~isempty(y{i})
    x{i} = y{i};
  end
  if iscell(x) && isnumeric(y) && isempty(x{i}) && ~isnan(y{i})
    x{i} = y(i);
  end
  if iscell(x) && iscell(y) && isequal(x{i}, 'unknown') && ~isequal(y{i}, 'unknown')
    x{i} = y{i};
  end
end

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% SUBFUNCTION
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function sel = select_event(event, eventtype, eventvalue)
% this code is copied from FT_TRIALFUN_GENERAL

% start by selecting all events
sel = true(1, length(event)); % this should be a row vector

% select all events of the specified type
if ~isempty(eventtype)
  for i=1:numel(event)
    sel(i) = sel(i) && ismatch(event(i).type, eventtype);
  end
end

% select all events with the specified value
if ~isempty(eventvalue)
  for i=1:numel(event)
    sel(i) = sel(i) && ismatch(event(i).value, eventvalue);
  end
end

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% SUBFUNCTION
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function s = remove_empty(s)
if isempty(s)
  return
else
  fn = fieldnames(s);
  fn = fn(structfun(@isempty, s));
  s = removefields(s, fn);
end

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% SUBFUNCTION
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function y = sort_fields(x)
fn = fieldnames(x);
fn = sort(fn);
for i=1:numel(fn)
  y.(fn{i}) = x.(fn{i});
end