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# ds2brainstorm

## (Toolbox/ds2brainstorm.m in BrainStorm 2.0 (Alpha))

### Function Synopsis

    [F,Channel,imegsens,ieegsens,iothersens,irefsens,grad_order_no,no_trials,filter,Time,RunTitle] = ds2brainstorm(ds_directory,VERBOSE,READRES,CHANNELS, TIME, NO_TRIALS, DCOffset);

### Help Text

    DS2BRAINSTORM - Convert a DS CTF dataset into BrainStorm format
     function [F,Channel,imegsens,ieegsens,iothersens,irefsens,grad_order_no,no_trials,filter,Time,RunTitle] = ds2brainstorm(ds_directory,VERBOSE,READRES,CHANNELS,TIME, NO_TRIALS, DCOffset);
     [F,Channel,imegsens,ieegsens,iothersens,irefsens,grad_order_no,no_trials,filter,Time, RunTitle]
                          ... = ds2brainstorm(ds_directory,VERBOSE,READRES,CHANNELS,TIME, NO_TRIALS, DCOffset);
     Reads CTF Systems Inc. Data and Resource file formats (4.1 MEG Data Format)

     INPUTS %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

     DS_DIRECTORY: full path to the .ds directory containing the orginal data set

     VERBOSE = 1 : Toggle VERBOSE mode ON
                   -> Activate screen display
                   -> Save channel information in channel_file.txt
                   -> Save coefficient information in coef_file.txt

     VERBOSE = 0 : Toggle VERBOSE mode OFF

    ---> The rest of the input parameters are optionnal (more specific to data block extraction)

     READRES = 0: Do not read the original .res4 file assuming it has been read previously.
                  Read  _res4.mat instead. If _res4.mat does not exist, automatic switch to READRES = 1.
     READRES = 1: Read the original .res4 file and writes out a file called **_res4.mat in the .ds folder,
                  which is a shortened version of the CTF resources file .res4.
                  MEG/EEG data are also read from the .meg4 file ONLY IF other input arguments are specified
     READRES = 2: Same as READRES = 1, without the long reading of coefficient information
                  but reads ONLY a short version of data information and returns them in F as a structure.
                  MEG/EEG data are nor read from the .meg4 file.
                  This is useful for display in importdata_gui for instance.
     READRES = 3: Do not read .meg4 file (useful when data need to be kept in native file format when importing into BrainStorm)
                  F is then a string indicating the name of the corresponding .ds folder.

     Set READRES to 0 when you want to extract another block of data after you have already read one block with READRES = 1.

     If READRES is left blank, the whole data are read - ATTENTION when data set is large - possible 'out of memory' issues

     If the optional fields below are left blank, ds2brainstorm only reads the res4 file and generates a *_res4.mat file in the current .ds folder
     with all useful information regarding EEG, MEG channels etc. for subsequent data extraction.

     CHANNELS: the indices of the channels to be extracted as in IEEGSENS, IMEGSENS or IREFSENS
     If you don't know what are the indices of these channels, run ds2brainstorm with READRES = 1 first.

     TIME: the time window to be extracted in seconds within a trial; ie a subset of Time, a vector saved in **_res4.mat (see above)

     NO_TRIALS: a vector containing the number(s) of the trial(s) to be extracted.

     DCOffset: A flag indicating whether DC offset needs to be removed from every MEG channel.
     Acceptable values:
                       0 - DC offset is not removed from MEG channels (DEFAULT)
                       1 - DC offset is removed from MEG channels based on the entire trial length
                       2 - DC offset is removed from MEG channels based on pretigger time period

      Example; ds2brainstorm('tipouic.ds',1,0,[10:30],[-.5 3],2)
     .. extracts in VERBOSE mode from tipouic.ds, assuming tipouic_res4.mat was already created during a previous call to ds2brainstorm with READRES = 1
     channels 10 to 30 (could be MEG or other -> check IEEGSENS and IMEGSENS first), between -0.5sec and 3sec, in trial 2.

     %
     OUTPUTS %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

     CHANNEL - MEG,EEG, REFERENCE and OTHER channel information
     CHANNEL is a cell array of structures following the BrainStorm data format.
     F - Data Matrix: One sensor per row; one time sample per column / alternatively a structure containing all data information
        (resources contained in .res4 file).
     IMEGSENS - Indices of the MEG data rows in F
     IEEGSENS - Indices of the EEG data rows in F
     IOTHERSENS - Indices of the OTHER data rows (not including reference coils) in F
     IREFSENS - Indices of the REFERENCE data rows in F
     GRAD_ORDER_NO - Order of the gradient correction to be taken into account in the forward model only
     NO_TRIALS - Number of trials in the .ds folder.
     FILTER - An array of structures; each structure containing information related to each filter
     TIME - A vector containing the time instants at every data sample
     RUNTITLE - A character array labelling the current run.

     Please refer to CTF Systems Inc. Technical Note #1 for details on DataSet file formats

### Cross-Reference Information

This function calls

    bst_message_window                 C:\BrainStorm_2001\Toolbox\bst_message_window.m
    findclosest                        C:\BrainStorm_2001\Toolbox\findclosest.m
    good_channel                       C:\BrainStorm_2001\Toolbox\good_channel.m
    offset                             C:\BrainStorm_2001\Toolbox\offset.m

This function is called by

    bst_extractmarkersctf              C:\BrainStorm_2001\Toolbox\bst_extractmarkersctf.m
    extract_ctf_markers                C:\BrainStorm_2001\Toolbox\extract_ctf_markers.m
    importdata                         C:\BrainStorm_2001\Toolbox\importdata.m
    meg4read                           C:\BrainStorm_2001\Toolbox\meg4read.m
    meg4write                          C:\BrainStorm_2001\Toolbox\meg4write.m

### Listing of function C:\BrainStorm_2001\Toolbox\ds2brainstorm.m

    function [F,Channel,imegsens,ieegsens,iothersens,irefsens,grad_order_no,no_trials,filter,Time,RunTitle] = ds2brainstorm(ds_directory,VERBOSE,READRES,CHANNELS, TIME, NO_TRIALS, DCOffset);
    %DS2BRAINSTORM - Convert a DS CTF dataset into BrainStorm format
    % function [F,Channel,imegsens,ieegsens,iothersens,irefsens,grad_order_no,no_trials,filter,Time,RunTitle] = ds2brainstorm(ds_directory,VERBOSE,READRES,CHANNELS,TIME, NO_TRIALS, DCOffset);
    % [F,Channel,imegsens,ieegsens,iothersens,irefsens,grad_order_no,no_trials,filter,Time, RunTitle]
    %                      ... = ds2brainstorm(ds_directory,VERBOSE,READRES,CHANNELS,TIME, NO_TRIALS, DCOffset);
    % Reads CTF Systems Inc. Data and Resource file formats (4.1 MEG Data Format)
    %
    % INPUTS %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
    %
    % DS_DIRECTORY: full path to the .ds directory containing the orginal data set
    %
    % VERBOSE = 1 : Toggle VERBOSE mode ON
    %               -> Activate screen display
    %               -> Save channel information in channel_file.txt
    %               -> Save coefficient information in coef_file.txt
    %
    % VERBOSE = 0 : Toggle VERBOSE mode OFF
    %
    %---> The rest of the input parameters are optionnal (more specific to data block extraction)
    %
    % READRES = 0: Do not read the original .res4 file assuming it has been read previously.
    %              Read  _res4.mat instead. If _res4.mat does not exist, automatic switch to READRES = 1.
    % READRES = 1: Read the original .res4 file and writes out a file called **_res4.mat in the .ds folder,
    %              which is a shortened version of the CTF resources file .res4.
    %              MEG/EEG data are also read from the .meg4 file ONLY IF other input arguments are specified
    % READRES = 2: Same as READRES = 1, without the long reading of coefficient information
    %              but reads ONLY a short version of data information and returns them in F as a structure.
    %              MEG/EEG data are nor read from the .meg4 file.
    %              This is useful for display in importdata_gui for instance.
    % READRES = 3: Do not read .meg4 file (useful when data need to be kept in native file format when importing into BrainStorm)
    %              F is then a string indicating the name of the corresponding .ds folder.
    %
    %
    % Set READRES to 0 when you want to extract another block of data after you have already read one block with READRES = 1.
    %
    % If READRES is left blank, the whole data are read - ATTENTION when data set is large - possible 'out of memory' issues
    %
    % If the optional fields below are left blank, ds2brainstorm only reads the res4 file and generates a *_res4.mat file in the current .ds folder
    % with all useful information regarding EEG, MEG channels etc. for subsequent data extraction.
    %
    % CHANNELS: the indices of the channels to be extracted as in IEEGSENS, IMEGSENS or IREFSENS
    % If you don't know what are the indices of these channels, run ds2brainstorm with READRES = 1 first.
    %
    % TIME: the time window to be extracted in seconds within a trial; ie a subset of Time, a vector saved in **_res4.mat (see above)
    %
    % NO_TRIALS: a vector containing the number(s) of the trial(s) to be extracted.
    %
    % DCOffset: A flag indicating whether DC offset needs to be removed from every MEG channel.
    % Acceptable values:
    %                   0 - DC offset is not removed from MEG channels (DEFAULT)
    %                   1 - DC offset is removed from MEG channels based on the entire trial length
    %                   2 - DC offset is removed from MEG channels based on pretigger time period
    %
    %  Example; ds2brainstorm('tipouic.ds',1,0,[10:30],[-.5 3],2)
    % .. extracts in VERBOSE mode from tipouic.ds, assuming tipouic_res4.mat was already created during a previous call to ds2brainstorm with READRES = 1
    % channels 10 to 30 (could be MEG or other -> check IEEGSENS and IMEGSENS first), between -0.5sec and 3sec, in trial 2.
    %
    % %
    % OUTPUTS %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
    %
    % CHANNEL - MEG,EEG, REFERENCE and OTHER channel information
    % CHANNEL is a cell array of structures following the BrainStorm data format.
    % F - Data Matrix: One sensor per row; one time sample per column / alternatively a structure containing all data information
    %    (resources contained in .res4 file).
    % IMEGSENS - Indices of the MEG data rows in F
    % IEEGSENS - Indices of the EEG data rows in F
    % IOTHERSENS - Indices of the OTHER data rows (not including reference coils) in F
    % IREFSENS - Indices of the REFERENCE data rows in F
    % GRAD_ORDER_NO - Order of the gradient correction to be taken into account in the forward model only
    % NO_TRIALS - Number of trials in the .ds folder.
    % FILTER - An array of structures; each structure containing information related to each filter
    % TIME - A vector containing the time instants at every data sample
    % RUNTITLE - A character array labelling the current run.
    %
    % Please refer to CTF Systems Inc. Technical Note #1 for details on DataSet file formats

    %<autobegin> ---------------------- 09-Jul-2004 22:17:03 -----------------------
    % --------- Automatically Generated Comments Block Using AUTO_COMMENTS ---------
    %
    % CATEGORY: Data Processing
    %
    % Alphabetical list of external functions (non-Matlab):
    %   toolbox\bst_message_window.m
    %   toolbox\findclosest.m
    %   toolbox\good_channel.m
    %
    % Subfunctions in this file, in order of occurrence in file:
    %   PrintCoilPos(channel_file, CoilRec_ext,nCoils) ;
    %   save_sensor_locs(Channel,SensorLocFile)
    %
    % At Check-in: $Author: Mosher $  $Revision: 31 $  $Date: 7/09/04 8:42p $
    %
    % This software is part of BrainStorm Toolbox Version 2.0 (Alpha) 09-Jul-2004
    %
    % Principal Investigators and Developers:
    % ** Richard M. Leahy, PhD, Signal & Image Processing Institute,
    %    University of Southern California, Los Angeles, CA
    % ** John C. Mosher, PhD, Biophysics Group,
    %    Los Alamos National Laboratory, Los Alamos, NM
    % ** Sylvain Baillet, PhD, Cognitive Neuroscience & Brain Imaging Laboratory,
    %    CNRS, Hopital de la Salpetriere, Paris, France
    %
    % See BrainStorm website at http://neuroimage.usc.edu for further information.
    %
    % Copyright (c) 2004 BrainStorm by the University of Southern California
    % This software distributed  under the terms of the GNU General Public License
    % as published by the Free Software Foundation. Further details on the GPL
    % license can be found at http://www.gnu.org/copyleft/gpl.html .
    %
    % FOR RESEARCH PURPOSES ONLY. THE SOFTWARE IS PROVIDED "AS IS," AND THE
    % UNIVERSITY OF SOUTHERN CALIFORNIA AND ITS COLLABORATORS DO NOT MAKE ANY
    % WARRANTY, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO WARRANTIES OF
    % MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE, NOR DO THEY ASSUME ANY
    % LIABILITY OR RESPONSIBILITY FOR THE USE OF THIS SOFTWARE.
    %<autoend> ------------------------ 09-Jul-2004 22:17:03 -----------------------

    % /---Script Authors-------------------------------------\
    % |                                                      |
    % |  *** Sylvain Baillet, Ph.D.                          |
    % |  Cognitive Neuroscience & Brain Imaging Laboratory   |
    % |  CNRS UPR640 - LENA                                  |
    % |  Hopital de la Salpetriere, Paris, France            |
    % |  sylvain.baillet@chups.jussieu.fr                    |
    % |                                                      |
    % |  *** John C. Mosher, Ph.D.                           |
    % |  Biophysics Group                                    |
    % |                                                      |
    % |  -> with contributions from                          |
    % |       Line Garnero, Ph.D. and Antoine Ducorps, Ph.D. |
    % |       MEG Center &                                   |
    % |       Cognitive Neuroscience                         |
    % |       & Brain Imaging Laboratory - CNRS              |
    % |       La Salpetriere Hospital, Paris - France        |
    % \------------------------------------------------------/

    % Script History ----------------------------------------------------------------------------------------
    %
    % 11/28/00 - SB   : Updated to read the 3rd-order gradient information
    % 03/09/01 - SB   : Data block extraction
    % 15/11/01 - SB   : Extraction of the ADC Input channels in iothersens, combined with the STIM channel
    % 19/11/01 - SB   : Save iothersens indices in the _res4.mat file
    % JCM 06-Jun-2002 : made waitbar update less often, autocomments
    % SB 26-Jul-2002  : Added the READRES = 2 condition to read basic file information for display importdata_gui.
    % ..............    Removed creation of channel_file.txt.
    % SB 27-Jul-2002  : Minor alterations in the READRES conditions - see help header.
    % SB 28-Jul-2002  : When CHANNELS is emptu, all channels, including reference channels are read.
    % ..............    Changed name of sensor location result file (for visualization in MRITool or 3DViewer)
    % SB 10-Sep-2002  : Minor layout editing.
    % SB 07-Nov-2002  : Display all progress information into bst_message_window
    % SB 15-Nov-2002  : EEG channel .loc field is now only 3x1 (was 3x2, second column filled with zeros).
    %                   Added DCOffset input argurment to remove DC Offset from MEG channels.
    % SB 20-Nov-2002  : Assign "MEG REF" type to MEG reference channels for fast retrieval with GOOD_CHANNEL
    %                   Removed .Gcoef and .i*sens fields form Channel structure.
    % SB 23-Dec-2002  : Time now returns only the time window on which data has been extracted (not the whole original time span of data)
    % SB 21-Jan-2003  : Fixed 1-sample bug for total Time length
    % SB 23-Jan-2003  : Added READRES = 3 option.
    % SB 06-Feb-2003  : Fixed bug when accessing data with multiple trials specificied in NO_TRIALS
    %                   From the second NO_TRIALS entry on, data was always read from beguinning of trial
    %                   not from the first entry specified in TIME.
    % JCM 10-Dec-2003 : Added additional blank SensorNames to account for type "17" in the 275 channel arrays. Moved around
    %                   redundant definitions of SensorNames. Thanks to Fred Carver NIMH for finding this.
    % JCM 11-Dec-2003 : Updating SensorNames based on email from CTF, plus correcting 0 indexing scheme
    % SB  09-Jul-2004 : Issuing a message when time clipping occurs (requested time window is outside original trial time window)
    % --------------------------------------------------------------------------------------------------------

    % Check input arguments

    if nargin == 2
       READRES = 1;
       DCOffset = 0;
    elseif nargin==5 % No Marker file and marker range are defined
       MARKER_FILE = 0;
       MARKER_RANGE = [];
       DCOffset = 0;
    elseif nargin==3
       % Read only file information.
       % Check that READRES has acceptable values - if not, the whole file could be read
       % which could end-up in loading very large files into Matlab format
       % and cause memory crash.
       if READRES ~= 1 | READRES ~= 2
          READRES = 2; % Read data information only.
       end
       MARKER_FILE = 0;
       MARKER_RANGE = [];
       CHANNELS = [];
       TIME = [];
       NO_TRIALS = [];
       DCOffset = 0;
    end

    % SensorNames ={'Ref Magnetometer','Ref Gradiometer' ,''  , '' , '' ,'MEG Sensor','' , '', '','EEG Sensor','ADC Input','Stimulation input',''}; % CHEAT - added the 13th '' ofor testing purposes
    % 10 December 2003 Fred Carver at NIMH found that 275 channel arrays had a type 17. Added extra blanks to account for.
    % 11 December 2003, based on email directly from Dough McKenzie at CTF:
    % Comments on types, email of 10 December 2003:
    %The new types are
    %
    %   SAM Sensor       -- Synthetic electrode channel from SAM
    %   Virtual Channel  -- Linear combination of sensor channels
    %   System Clock     -- 32 bit counter incremented at 12,000
    %                       ticks per second for Omega 2000 system
    %                       ( new systems like NIMH )
    %                       and incremented at 12,500 ticks per
    %                       second for older systems.
    %
    %   Video Time       -- A 32 bit encoded value that can be recorded from
    %                       a SONY Video time output from a video player or
    %                       recorder
    %
    %                       The format is 'hhmmssff'  where each byte contains
    %                            hh    hours in decimal ( 0--
    %                            mm    minutes          ( 0 ~ 59 )
    %                            ss    seconds          ( 0 ~ 59 )
    %                            ff    frames           ( 0 ~ 29 )
    %
    %   ADC Input Voltage -- Used if the ADC channels are measuring
    %                        voltage. == 11 if measuring current
    %
    %NOTE: There will be some additional channels types defined
    %      mid next year.
    SensorNames ={...
          'Ref Magnetometer',... % Sensor Type Index of 0
          'Ref Gradiometer' ,... % Index of 1
          ''  ,...               % 2
          '' ,...                % 3
          '' ,...                % 4
          'MEG Sensor',...       % 5
          '' ,...                % 6
          '',...                 % 7
          '',...                 % 8
          'EEG Sensor',...       % 9
          'ADC Input Current',...% 10 ADC Input Current (Amps)
          'Stimulation input',...% 11
          'Video Time',...       % 12
          '',...                 % 13
          '',...                 % 14
          'SAM Sensor',...       % 15
          'Virtual Channel',...  % 16
          'System Clock',...     % 17 System Time Ref
          'ADC Input Voltage',...% 18 ADC Input Voltage (Volts)
       };

    %% Checking files
    cd(ds_directory)
    [path,rootname] = fileparts(ds_directory);
    meg4file = [rootname,'.meg4'];
    rec4file = [rootname,'.res4'];
    if isdir('BrainStorm') % Was a BrainStorm directory created in .ds folder ? Yes if ds2brainstorm called from importdata
       res4_mat = fullfile('BrainStorm',[rootname,'_res4.mat']);
    else
       res4_mat = [rootname,'_res4.mat'];
    end

    if ~exist(rec4file,'file') | ~exist(meg4file,'file')
       errordlg([rec4file ' or ' meg4file ' missing'])
       return
    end

    % If .mat version of the .res4 does not exist while we supposed it did - read it.
    if ~exist(res4_mat,'file') & READRES == 0
       % Force reading of original .res4
       READRES = 1;
    end

    if VERBOSE
       bst_message_window(['Working in...',ds_directory ])
    end

    %*********************************************

    if READRES > 0 % Read the .res4 file

       %% Define constants
       MAX_COILS = 8;
       MAX_BALANCING = 50;
       SENSOR_LABEL = 31;
       MAX_AVERAGE_BINS = 8;

       %***** nfSetUp **** Data Structure
       gSetUp = struct('no_samples',[],'no_channels',[],'sample_rate',[],...
          'epoch_time',[],'no_trials',[],'preTrigPts',[],'no_trials_done',[],'no_trials_display',[],...
          'save_trials',[],'primaryTrigger',[],'secondaryTrigger',[],'triggerPolarityMask',[],...
          'trigger_mode',[],'accept_reject_Flag',[],'run_time_display',[],'zero_Head_Flag',[],...
          'artifact_mode',[]);

       nfSetUp = struct('nf_run_name','','nf_run_title','','nf_instruments','','nf_collectdescriptor','',...
          'nf_subject_id','','nf_operator','','nf_sensorFileName','');

       GenRes4 = struct('appName','','dataOrigin','','dataDescription','',...
          'no_trials_avgd',[],'data_time',[],'data_date',[],'gSetUp',gSetUp,'nfSetUp',nfSetUp,'rdlen',[]);

       %*********************************************

       %% Reading Resources------------------------------------------------------------------------
       [rec,message] = fopen(rec4file,'rb','s'); % Big-endian byte ordering
       if rec < 0
          errordlg(message)
          return
       end

       % Read HEADER
       header = fread(rec,8,'char')';
       if VERBOSE
          bst_message_window({...
                sprintf('ds2brainstorm -> %s file format',char(header))...
             })
       end

       % Read nfSetUp
       GenRes4.appName = char(fread(rec,256,'char')');
       GenRes4.dataOrigin = char(fread(rec,256,'char')');
       GenRes4.dataDescription = char(fread(rec,256,'char')');

       GenRes4.no_trials_avgd = (fread(rec,1,'int16')');
       GenRes4.data_time = char(fread(rec,255,'char')');
       GenRes4.data_date = char(fread(rec,255,'char')');

       gSetUp.no_samples = (fread(rec,1,'int32')');
       gSetUp.no_channels = (fread(rec,1,'int16')');

       fseek(rec,ceil(ftell(rec)/8)*8,-1);
       gSetUp.sample_rate = (fread(rec,1,'double')');
       fseek(rec,ceil(ftell(rec)/8)*8,-1);
       gSetUp.epoch_time = (fread(rec,1,'double')');
       gSetUp.no_trials =  (fread(rec,1,'int16')');
       fseek(rec,ceil(ftell(rec)/4)*4,-1);
       gSetUp.preTrigPts =  (fread(rec,1,'int32')');
       gSetUp.no_trials_done = (fread(rec,1,'int16')');
       gSetUp.no_trials_bst_message_windowlay = (fread(rec,1,'int16')');
       fseek(rec,ceil(ftell(rec)/4)*4,-1);
       gSetUp.save_trials = (fread(rec,1,'int32')');
       gSetUp.primaryTrigger = char(fread(rec,1,'uchar')');
       gSetUp.secondaryTrigger = char(fread(rec,MAX_AVERAGE_BINS,'uchar')');
       gSetUp.triggerPolarityMask = char(fread(rec,1,'uchar')');

       gSetUp.trigger_mode = (fread(rec,1,'int16')');
       fseek(rec,ceil(ftell(rec)/4)*4,-1);
       gSetUp.accept_reject_Flag = (fread(rec,1,'int32')');
       gSetUp.run_time_bst_message_windowlay = (fread(rec,1,'int16')');
       fseek(rec,ceil(ftell(rec)/4)*4,-1);
       gSetUp.zero_Head_Flag = (fread(rec,1,'int32')');
       fseek(rec,ceil(ftell(rec)/4)*4,-1);
       gSetUp.artifact_mode = (fread(rec,1,'int32')');
       gSetUp.padding = (fread(rec,1,'int32')');

       nfSetUp.nf_run_name = char(fread(rec,32,'char')');
       nfSetUp.nf_run_title = char(fread(rec,256,'char')');

       RunTitle = nfSetUp.nf_run_title ;

       nfSetUp.nf_instruments = char(fread(rec,32,'char')');
       nfSetUp.nf_collect_descriptor = char(fread(rec,32,'char')');
       nfSetUp.nf_subject_id = char(fread(rec,32,'char')');
       nfSetUp.nf_operator = char(fread(rec,32,'char')');
       tmp = fread(rec,60,'char')';
       tmp(tmp<0) = 0; % prevent out of range character conversion warning
       nfSetUp.nf_sensorFileName = tmp; clear tmp

       fseek(rec,ceil(ftell(rec)/4)*4,-1);
       nfSetUp.rdlen = fread(rec,1,'int32')';

       GenRes4.gSetUp = gSetUp;
       GenRes4.nfSetUp = nfSetUp;

       % Time span of data record
       Time =  linspace(-GenRes4.gSetUp.preTrigPts/GenRes4.gSetUp.sample_rate,...
          GenRes4.gSetUp.epoch_time/GenRes4.gSetUp.no_trials-(GenRes4.gSetUp.preTrigPts+1)/GenRes4.gSetUp.sample_rate,...
          GenRes4.gSetUp.no_samples);
       %
       %----------------------------------------------------------------------------------------

       if VERBOSE
          ON_OFF = {'Off','On'};
          bst_message_window({...
                sprintf('ds2brainstorm -> Header information'),...
                sprintf('                 Collected %s starting at %s', GenRes4.data_date, GenRes4.data_time),...
                sprintf('                 Run Name: %s', nfSetUp.nf_run_name),...
                sprintf('                 Run Title: %s', nfSetUp.nf_run_title),...
                sprintf('                 Col Desc: %s', nfSetUp.nf_collect_descriptor),...
                sprintf('                 Run Desc: %s', GenRes4.dataDescription),...
                sprintf('                 Operator: %s', nfSetUp.nf_operator),...
                sprintf('                 Subject : %s', nfSetUp.nf_subject_id),...
                sprintf('                 Channels: %d',gSetUp.no_channels),...
                sprintf('                 Samples : %d per trial', gSetUp.no_samples),...
                sprintf('                 Rate    : %g samples/sec', gSetUp.sample_rate),...
                sprintf('                 Trials  : %d (average of %d)', gSetUp.no_trials, GenRes4.no_trials_avgd),...
                sprintf('                 Duration: %g seconds/trial', gSetUp.epoch_time/gSetUp.no_trials),...
                sprintf('                 Pre-trig: %g samples', gSetUp.preTrigPts),...
                sprintf('                 Sensor file name : %s', nfSetUp.nf_sensorFileName),...
                sprintf('                 Head zeroing: %s', ON_OFF{gSetUp.zero_Head_Flag+1}),...
                sprintf('_________________')...
             })
       end

       %-------------------------------------------------------------------------------

       %----------------------------------READ FILTERS---------------------------------

       fseek(rec,1844,-1);

       % Run Description
       rundescript = char(fread(rec,nfSetUp.rdlen,'char'));
       if VERBOSE
          bst_message_window({...
                sprintf('ds2brainstorm -> Run Description: %s', rundescript )...
             })
       end

       classType = {'CLASSERROR','BUTTERWORTH'};
       filtType = {'TYPERROR','LOWPASS','HIGHPASS','NOTCH'};

       % Number of filters
       no_filters = fread(rec,1,'int16');
       if VERBOSE
          bst_message_window({...
                'ds2brainstorm -> Filter information',...
                sprintf('Number of filters: %d', no_filters)...
             })
       end

       % JCM fixes 10/26/00, each of these should be looped through, not read in bulk
       % old
       if(0)
          filter.freq = fread(rec,no_filters,'double');
          filter.fClass = classType{fread(rec,no_filters,'int32')+1};
          filter.fType = filtType{fread(rec,no_filters,'int32')+1};
          filter.numParam = fread(rec,no_filters,'int16');

          filter.params = [];
          for fi = 1:no_filters
             for p = 1: filter(fi).numParam
                filter(fi).params= fread(rec,filter(fi).numParam,'double');
             end
          end
       else % new
          [filter(1:no_filters)] = struct('freq',[],'fClass',[],'fType',[],'numParam',[],'params',[]);
          for fi = 1:no_filters,
             filter(fi).freq = fread(rec,1,'double');
             filter(fi).fClass = classType{fread(rec,1,'int32')+1};
             filter(fi).fType = filtType{fread(rec,1,'int32')+1};
             filter(fi).numParam = fread(rec,1,'int16');
             filter(fi).params= fread(rec,filter(fi).numParam,'double');
          end
       end

       if VERBOSE
          for fi = 1:no_filters
             bst_message_window({...
                   sprintf('  Filter - %d',fi),...
                   sprintf('         -> Frequency: %g Hz',filter(fi).freq),...
                   sprintf('         -> Class: %s',filter(fi).fClass),...
                   sprintf('         -> Type: %s',filter(fi).fType),...
                   sprintf('         -> Number of parameters: %d',filter(fi).numParam)...
                })

             if ~isempty(filter(fi).params)
                bst_message_window({...
                      sprintf('         -> Parameter Value(s): %g',filter(fi).params)...
                   })
             end
          end

          bst_message_window({...
                sprintf('ds2brainstorm -> Reading Filter Information - DONE'),...
                sprintf('ds2brainstorm -> Reading Channel Information. . .')...
             })
       end

       % Channel Names
       for chan = 1:gSetUp.no_channels
          channel_name{chan} = fread(rec,32,'char')';
          tmp = channel_name{chan};
          tmp(tmp>127) = 0;
          tmp(tmp<0) = 0;
          channel_name{chan} = strtok(tmp,char(0));
          ChannelName{chan} = char(strtok(channel_name{chan},'-'));
       end

       % Sensor Resources
       CoilType = {'CIRCULAR','SQUARE','???'};

       for chan = 1:gSetUp.no_channels
          oldtell = ftell(rec);
          SensorRes(chan).sensorTypeIndex = fread(rec,1,'int16');
          SensorRes(chan).originalRunNum = fread(rec,1,'int16');

          id = fread(rec,1,'int32')+1;

          if isempty(id)
             id = -1;
          end

          if id > 3 | id <0
             id = 3;
          end

          SensorRes(chan).coilShape = CoilType{id};

          SensorRes(chan).properGain = fread(rec,1,'double');
          SensorRes(chan).qGain = fread(rec,1,'double');
          SensorRes(chan).ioGain= fread(rec,1,'double');
          SensorRes(chan).ioOffset = fread(rec,1,'double');
          SensorRes(chan).numCoils = fread(rec,1,'int16');
          SensorRes(chan).grad_order_no = fread(rec,1,'int16');

          padding = fread(rec,1,'int32');

          for coil = 1:MAX_COILS
             SensorRes(chan).coilTbl(coil).position.x = fread(rec,1,'double');
             SensorRes(chan).coilTbl(coil).position.y = fread(rec,1,'double');
             SensorRes(chan).coilTbl(coil).position.z = fread(rec,1,'double');
             SensorRes(chan).coilTbl(coil).position.junk = fread(rec,1,'double');
             SensorRes(chan).coilTbl(coil).orient.x = fread(rec,1,'double');
             SensorRes(chan).coilTbl(coil).orient.y = fread(rec,1,'double');
             SensorRes(chan).coilTbl(coil).orient.z = fread(rec,1,'double');
             SensorRes(chan).coilTbl(coil).orient.junk = fread(rec,1,'double');
             SensorRes(chan).coilTbl(coil).numturns = fread(rec,1,'int16');
             padding = fread(rec,1,'int32');
             padding = fread(rec,1,'int16');
             SensorRes(chan).coilTbl(coil).area = fread(rec,1,'double');
          end

          for coil = 1:MAX_COILS
             SensorRes(chan).HdcoilTbl(coil).position.x = fread(rec,1,'double');
             SensorRes(chan).HdcoilTbl(coil).position.y = fread(rec,1,'double');
             SensorRes(chan).HdcoilTbl(coil).position.z = fread(rec,1,'double');
             SensorRes(chan).HdcoilTbl(coil).position.junk = fread(rec,1,'double');
             SensorRes(chan).HdcoilTbl(coil).orient.x = fread(rec,1,'double');
             SensorRes(chan).HdcoilTbl(coil).orient.y = fread(rec,1,'double');
             SensorRes(chan).HdcoilTbl(coil).orient.z = fread(rec,1,'double');
             SensorRes(chan).HdcoilTbl(coil).orient.junk = fread(rec,1,'double');
             SensorRes(chan).HdcoilTbl(coil).numturns = fread(rec,1,'int16');
             padding = fread(rec,1,'int32');
             padding = fread(rec,1,'int16');
             SensorRes(chan).HdcoilTbl(coil).area = fread(rec,1,'double');
          end

       end

       if VERBOSE
          bst_message_window({...
                'ds2brainstorm -> Reading Channel Information - DONE',...
                'ds2brainstorm -> Converting Channel Information. . .'...
             })

       end

       nchan= length(channel_name); % Number of channels

       % Explode data according to channel type
       imegsens = find([SensorRes.sensorTypeIndex] == 5); % Indices of MEG sensors
       ieegsens = find([SensorRes.sensorTypeIndex] == 9); % Indices of EEG sensors
       iothersens = [find([SensorRes.sensorTypeIndex] == 11),find([SensorRes.sensorTypeIndex] == 10)]; % Indices of OTHER sensors ('Stimulation input' and 'ADC')
       irefsens = find([SensorRes.sensorTypeIndex] == 0); % Reference Channels
       irefsens = [irefsens,find([SensorRes.sensorTypeIndex] == 1)]; % Reference Channels

       if READRES == 2 % Enough data information - send it out of here
          % Initialize output arguments
          [F,Channel,grad_order_no,no_trials,Time, RunTitle] = deal([]);
          F = GenRes4;
          F.filter = filter;
          F.imegsens = imegsens;
          F.ieegsens = ieegsens;
          F.irefsens = irefsens;
          F.iothersens = iothersens;
          F.ChannelNames = char(ChannelName);
          F.grad_order_no = [SensorRes(:).grad_order_no];
          F.nchan = length([imegsens,ieegsens,irefsens,iothersens]);
          F.Time = Time;
          return
       end

       ieeg = 0; imeg = 0;
       iother = 0;
       iother2 = 0;
       eegID = [];
       otherID = [];
       other2ID = [];
       megID = [];

       iref = 0;
       refID = [];

       chaneeg = [];% Index of EEG channels
       chanmeg = [];chanother = [];

       [Channel(1:nchan)] = deal(struct('Loc',[],'Orient',[],'Comment',[],'Weight',[],'Type',[],'Name',''));

       for chan = 1:nchan
          switch SensorRes(chan).sensorTypeIndex
          case {0,1} %'References coils
             iref = iref+1;
             MAX_COILS = 2; % Take only into account the 2 first coils for every sensor
             tmp = [SensorRes(chan).HdcoilTbl(:).position];
             tmpx  = [tmp(1:MAX_COILS).x]/100; % In meters
             tmpy  = [tmp(1:MAX_COILS).y]/100;
             tmpz  = [tmp(1:MAX_COILS).z]/100;

             Channel(chan).Loc = ...
                [tmpx;tmpy;tmpz];

             tmp = [SensorRes(chan).HdcoilTbl(:).orient];
             tmpx  = [tmp(1:MAX_COILS).x];
             tmpy  = [tmp(1:MAX_COILS).y];
             tmpz  = [tmp(1:MAX_COILS).z];

             Channel(chan).Orient = ...
                [tmpx;tmpy;tmpz];

             % Reorient along the outward pointing normal
             loc =  Channel(chan).Loc';
             ps1=sum((loc(1,:).*Channel(chan).Orient(:,1)')')';
             ps2=sum((loc(2,:).*Channel(chan).Orient(:,2)')')';
             Channel(chan).Orient(:,1) = (sign(ps1)*ones(1,3).*Channel(chan).Orient(:,1)')';
             Channel(chan).Orient(:,2) = (sign(ps2)*ones(1,3).*Channel(chan).Orient(:,2)')';

             Channel(chan).Comment = ''; % SB 20-Nov-2002
             Channel(chan).Name = [SensorNames{SensorRes(chan).sensorTypeIndex+1},' ',int2str(chan)];% SB 20-Nov-2002
             Channel(chan).Type = 'MEG REF'; % SB 20-Nov-2002

             % Channel(chan).Type = SensorNames{SensorRes(chan).sensorTypeIndex+1} ;
             %             if isempty(deblank(Channel(chan).Type))
             %                 Channel(chan).Type = 'MEG REF';
             %             end

             chanmeg = [chanmeg,chan];
             refID(iref) = chan;
             Channel(chan).Weight = [1 -1] ;

          case 5 % MEG
             imeg = imeg+1;
             MAX_COILS = 2; % Take only into account the 2 first coils for every sensor
             tmp = [SensorRes(chan).HdcoilTbl(:).position];
             tmpx  = [tmp(1:MAX_COILS).x]/100; % In meters
             tmpy  = [tmp(1:MAX_COILS).y]/100;
             tmpz  = [tmp(1:MAX_COILS).z]/100;

             Channel(chan).Loc = ...
                [tmpx;tmpy;tmpz];

             tmp = [SensorRes(chan).HdcoilTbl(:).orient];
             tmpx  = [tmp(1:MAX_COILS).x];
             tmpy  = [tmp(1:MAX_COILS).y];
             tmpz  = [tmp(1:MAX_COILS).z];

             Channel(chan).Orient = ...
                [tmpx;tmpy;tmpz];

             % Reorient along the outward pointing normal
             loc =  Channel(chan).Loc';
             ps1=sum((loc(1,:).*Channel(chan).Orient(:,1)')')';
             ps2=sum((loc(2,:).*Channel(chan).Orient(:,2)')')';
             Channel(chan).Orient(:,1) = (sign(ps1)*ones(1,3).*Channel(chan).Orient(:,1)')';
             Channel(chan).Orient(:,2) = (sign(ps2)*ones(1,3).*Channel(chan).Orient(:,2)')';

             Channel(chan).Comment = ' ';%[SensorNames{SensorRes(chan).sensorTypeIndex+1},' ',int2str(chan)];
             Channel(chan).Type = 'MEG';%SensorNames{SensorRes(chan).sensorTypeIndex+1} ;

             chanmeg = [chanmeg,chan];
             megID(imeg) = chan;
             Channel(chan).Weight = [1 -1] ;
             Channel(chan).Name = ChannelName{chan}; %char(strtok(channel_name{chan},'-'));

          case 9 % EEG
             ieeg = ieeg + 1;
             MAX_COILS = 1; % Take only into account the first sensor location for EEG (second pseudo-coil location is [0 0 0] as writtent in .ds files)
             tmp = [SensorRes(chan).HdcoilTbl(:).position];
             tmpx  = [tmp(1:MAX_COILS).x]/100;% In meters
             tmpy  = [tmp(1:MAX_COILS).y]/100;
             tmpz  = [tmp(1:MAX_COILS).z]/100;

             Channel(chan).Loc = ...
                [tmpx;tmpy;tmpz];

             Channel(chan).Orient = [];

             Channel(chan).Type = 'EEG';%SensorNames{SensorRes(chan).sensorTypeIndex+1} ;
             Channel(chan).Comment = ' ';%[SensorNames{SensorRes(chan).sensorTypeIndex+1},' ',int2str(chan)];
             Channel(chan).Weight = [];
             Channel(chan).Name = ChannelName{chan}; %char(strtok(channel_name{chan},'-'));%int2str(ieeg) ;
             chaneeg = [chaneeg,chan];
             eegID(ieeg) = chan;

          case 11 % STIM
             iother = iother+1;
             Channel(chan).Loc = [];
             Channel(chan).Orient = [];
             Channel(chan).Type = SensorNames{SensorRes(chan).sensorTypeIndex+1} ;
             if isempty(deblank(Channel(chan).Type ))
                Channel(chan).Type = 'STIM';
             end
             Channel(chan).Comment = [SensorNames{SensorRes(chan).sensorTypeIndex+1},' ',int2str(chan)];
             Channel(chan).Weight = [];
             chanother = [chanother,chan];
             otherID(iother) = chan;

          otherwise
             iother2 = iother2+1;
             Channel(chan).Loc = [];
             Channel(chan).Orient = [];
             Channel(chan).Type = SensorNames{SensorRes(chan).sensorTypeIndex+1} ;
             if isempty(deblank(Channel(chan).Type ))
                Channel(chan).Type = 'OTHER';
             end
             Channel(chan).Comment = [SensorNames{SensorRes(chan).sensorTypeIndex+1},' ',int2str(chan)];
             Channel(chan).Weight = [];
             chanother = [chanother,chan];
             other2ID(iother) = chan;

          end
       end

       if VERBOSE
          bst_message_window('ds2brainstorm -> Converting Channel Information  - DONE')
       end
       %-------------------------------------------------------------------------------

       %----------------------------------READ Coefficients---------------------------------

       % Number of coefficient records
       nrec = fread(rec,1,'int16');

       channel_name = cellstr(char(channel_name{:}));
       hexadef = {'00000000','47314252','47324252','47334252','47324f49','47334f49'};
       strdef = {'NOGRAD','G1BR','G2BR','G3BR','G2OI','G3OI'};

       CoefInfo = cell(length(channel_name),length(strdef)-1);

       %SensorCoefResRec
       if VERBOSE
          h = waitbar(0,'Reading Coefficient Information...');
       end

       for k = 1:nrec
          SensorCoefResRec(k).sensorName = char(fread(rec,32,'char')');
          SensorCoefResRec(k).coefType = (fread(rec,1,'bit32'));
          padding = fread(rec,1,'int32');
          SensorCoefResRec(k).CoefResRec.num_of_coefs = fread(rec,1,'int16');

          SensorCoefResRec(k).CoefResRec.sensor_list = char(fread(rec,[SENSOR_LABEL,MAX_BALANCING],'uchar')');
          SensorCoefResRec(k).CoefResRec.coefs_list = fread(rec,MAX_BALANCING,'double');

          cchannel = strmatch( deblank(SensorCoefResRec(k).sensorName),channel_name);
          CoefType = find(hex2dec(hexadef)==(SensorCoefResRec(k).coefType));

          if CoefType > 0

             CoefInfo{cchannel,CoefType-1}.num_of_coefs =  SensorCoefResRec(k).CoefResRec.num_of_coefs;

             for i=1:CoefInfo{cchannel,CoefType-1}.num_of_coefs
                CoefInfo{cchannel,CoefType-1}.sensor_list(i) = irefsens(strmatch(strtok(SensorCoefResRec(k).CoefResRec.sensor_list(i,:),char(0)),channel_name(irefsens)));
                CoefInfo{cchannel,CoefType-1}.coefs(i) = SensorCoefResRec(k).CoefResRec.coefs_list(i);
             end
          end
          if VERBOSE
             if(~rem(k,floor(nrec/10))), % only occasionally
                waitbar(k/nrec);
             end
          end

       end

       if VERBOSE
          delete(h)
       end

       % Channel Gains
       gain_chan = zeros(size(channel_name,1),1);
       gain_chan(imegsens) = ([SensorRes(imegsens).properGain]'.*[SensorRes(imegsens).qGain]');
       gain_chan(irefsens) = ([SensorRes(irefsens).properGain]'.*[SensorRes(irefsens).qGain]');
       %  gain_chan(ieegsens) = 1./([SensorRes(ieegsens).qGain]'*1e-6);
       gain_chan(ieegsens) = 1./([SensorRes(ieegsens).qGain]');
       gain_chan(iothersens) = ([SensorRes(iothersens).qGain]'); % Don't know exactly which gain to apply here

       if VERBOSE
          bst_message_window({...
                'ds2brainstorm -> Coefficient Information',...
                sprintf('                 Number of coefficient records = %d ', nrec)...
             })

          %         [coef_file, message] = fopen('coef_file.txt','wt+');
          %
          %         if coef_file < 0
          %             errordlg(message)
          %             return
          %         end
          %
          %         fprintf(coef_file,'Number of coefficient records = %d \n', nrec);
          %
          %         for k = 1:nrec
          %             coefType = dec2hex(SensorCoefResRec(k).coefType);
          %             coefType = strdef{hex2dec(hexadef)==hex2dec(coefType)};
          %
          %             fprintf(coef_file,'Channel %s type %s, ',SensorCoefResRec(k).sensorName, coefType);
          %
          %             num_of_coefs = SensorCoefResRec(k).CoefResRec.num_of_coefs;
          %             fprintf(coef_file,'number = %d:\n', num_of_coefs);
          %             if 0
          %                 for j=1:num_of_coefs
          %                     fprintf(coef_file,' %s ', SensorCoefResRec(k).CoefResRec.sensor_list{j});
          %                     fprintf(coef_file,' %g ',SensorCoefResRec(k).CoefResRec.coefs_list(j));
          %                 end
          %                 fprintf(coef_file,'\n');
          %             end
          %         end
          %
          %         bst_message_window({...
          %                 sprintf('ds2brainstorm -> Coefficient Information saved in coef_file.txt'),...
          %                 sprintf('ds2brainstorm -> Reading Coefficient Information - DONE'),...
          %                 ' '...
          %             })
          bst_message_window({...
                sprintf('ds2brainstorm -> Reading Coefficient Information - DONE'),...
                ' '...
             })
       end

       % Calculus of the matrix for nth-order gradient correction
       % Coefficients for unused reference channels are weigthed by zeros in
       % the correction matrix.
       Gcoef = zeros(length(imegsens),length(min(irefsens):max(irefsens)));
       grad_order_no = [SensorRes(:).grad_order_no];
       for k = 1:length(imegsens)

          % Reference coils for channel k
          if grad_order_no(imegsens(k)) == 0
             %Data is saved as RAW
             %Save 3rd order gradient sensor-list for subsequent correction if requested later by the user
             [refs] = (CoefInfo{imegsens(k),3}.sensor_list);
             Gcoef(k,refs-min(irefsens)+1) = CoefInfo{imegsens(k),3}.coefs ...
                .* gain_chan(refs)'/gain_chan(imegsens(k));
          else
             [refs] = (CoefInfo{imegsens(k),grad_order_no(imegsens(k))}.sensor_list);
             Gcoef(k,refs-min(irefsens)+1) = CoefInfo{imegsens(k),grad_order_no(imegsens(k))}.coefs ...
                .* gain_chan(refs)'/gain_chan(imegsens(k));
          end

       end

       Channel(imegsens(1)).Comment = Gcoef;
       %     Channel(imegsens(1)).imegsens = imegsens;
       %     Channel(imegsens(1)).ieegsens = imegsens;
       %     Channel(imegsens(1)).irefsens = irefsens;
       %     Channel(imegsens(1)).RefChannel = Channel; % Store MEG reference information for future call to MEG forward routines and proper processing of gradient correction

       SensorLocFile = strrep(ds_directory,[fileparts(ds_directory),filesep],'');
       SensorLocFile = strrep(SensorLocFile,'.ds','_SensorLoc_Results.mat');
       save_sensor_locs(Channel,SensorLocFile)

       if VERBOSE
          bst_message_window('wrap',...
             sprintf('ds2brainstorm -> Sensor locations can be visualized with the MRI Tool by loading the file: %s',SensorLocFile)...
             )
       end

       fclose('all');

       % Create the BrainStorm res4 file for subsequent fast access to the data file
       no_channels = length(Channel);
       gain_chan(ieegsens)=1./gain_chan(ieegsens);
       no_trials = [1:gSetUp.no_trials];

       save(res4_mat,'gSetUp','meg4file','ieegsens','irefsens', 'iothersens', 'imegsens','Time','no_channels','gain_chan','channel_name','Channel','grad_order_no','filter','RunTitle','no_trials');

       if READRES == 1
          READRES = 0; %read the res4.mat file just created and read the data by block.
          if nargin == 2 % Stop there
             return
             %CHANNELS = 1:length(Channel); % With all available channels...
             %TIME = Time; % whole time duration...
             %NO_TRIALS = 1:gSetUp.no_trials; % All trials
          elseif nargin > 2
             % Fill empty arguments
             if isempty(CHANNELS)
                CHANNELS = 1:length(Channel); % Read all available channels
             end
             if isempty(TIME)
                TIME = Time; % Read entire trial duration
             end
             if isempty(NO_TRIALS)
                NO_TRIALS = 1:gSetUp.no_trials; % Read all trials
             end
          end
       end

    else % READRES == 0
       if VERBOSE
          bst_message_window({...
                sprintf('ds2brainstorm -> Loading resources : %s',res4_mat),...
             })
       end
       load(res4_mat);
    end

    %--------------------------- Readind DATA FILE ------------------------------

    if VERBOSE
       bst_message_window({...
             sprintf('ds2brainstorm -> Reading Data')...
          })
    end

    if nargin == 2 & READRES ~= 3 % Read entire block of data

       no_trials = gSetUp.no_trials;

       [meg,message] = fopen(meg4file,'rb','s'); % Big-endian byte ordering
       if meg < 0
          errordlg(message)
          return
       end

       F = cell(gSetUp.no_trials,1); % allocate space for the data array % ASSUME SINGLE TRIAL FOR THE MOMENT - ie enough memory space
       header = char(fread(meg,8,'char')');

       nref=length(irefsens);

       for trial = 1:gSetUp.no_trials

          if VERBOSE
             bst_message_window({...
                   sprintf('ds2brainstorm -> Trial %d/%d',trial,gSetUp.no_trials)...
                })
          end

          F{trial} = zeros(gSetUp.no_channels,gSetUp.no_samples);
          F{trial} = fread(meg,[gSetUp.no_samples gSetUp.no_channels],'int32')';

          if VERBOSE
             bst_message_window({...
                   sprintf('ds2brainstorm -> Done')...
                })
          end
          % IMPORTANT NOTICE : Applying Gradient Correction
          % Data are saved in a given nth-order gradient correction
          % Applying gradient correction si only needed for forward model computation
          % or if it is desired to reverse to lower-order gradient correction (see importdata.m for instance).

          % Apply channel gains
          F{trial}(ieegsens,:) = diag(1./gain_chan(ieegsens))*F{trial}(ieegsens,:) ; %To get Microvolts->already inverted when saved _res4.mat
          F{trial}(imegsens,:) = diag(1./gain_chan(imegsens))*F{trial}(imegsens,:)  ;

          % Removing DC Offset
          switch(DCOffset)
          case {1,2}
             if VERBOSE
                bst_message_window({...
                      'ds2brainstorm -> Removing DC offset'...
                   })
             end

             if DCOffset == 1 % Based on entire trial length
                F{trial}(imegsens,:) = F{trial}(imegsens,:) - repmat(mean(F{trial}(imegsens,:)')',1,size(F{trial},2));
                F{trial}(irefsens,:) = F{trial}(irefsens,:) - repmat(mean(F{trial}(irefsens,:)')',1,size(F{trial},2));
             else % Based on pretrigger
                TimeNeg = TIME(TIME<0); % Find pretrigger time points
                if ~isempty(TimeNeg)
                   F{trial}(imegsens,:) = F{trial}(imegsens,:) - repmat(mean(F{trial}(imegsens,TimeNeg)')',1,size(F{trial},2));
                   F{trial}(irefsens,:) = F{trial}(irefsens,:) - repmat(mean(F{trial}(irefsens,TimeNeg)')',1,size(F{trial},2));
                else
                   % Do nothing
                end
             end

             if VERBOSE
                bst_message_window({...
                      'ds2brainstorm -> Removing DC offset - DONE'...
                   })
             end

          otherwise
             % Do nothing
          end

          F{trial}(irefsens,:) = diag(1./gain_chan(irefsens))*F{trial}(irefsens,:) ;
          F{trial}(iothersens,:) = diag(1./gain_chan(iothersens))*F{trial}(iothersens,:) ;

       end

       fclose('all');

       %------------------------------------------------------------------------------------------------------------------------------------

    else % Reads sub-block of data

       if READRES == 3

          F = ds_directory;

          if isempty(NO_TRIALS) % argument NO_TRIALS not defined
             no_trials = [1:gSetUp.no_trials];
          else
             no_trials = [NO_TRIALS];
          end

       else

          %ds2brainstorm(ds_directory,VERBOSE,READRES,CHANNELS,TIME, NO_TRIALS);
          if isempty(NO_TRIALS) % argument NO_TRIALS not defined
             no_trials = [1:gSetUp.no_trials];
          else
             no_trials = [NO_TRIALS];
          end

          if isempty(CHANNELS)
             CHANNELS = [1:length(Channel)];
          end

          fclose('all');
          [meg,message] = fopen(meg4file,'rb','s'); % Big-endian byte ordering
          if meg < 0
             errordlg(message)
             return
          end

          % no_samples = round((TIME(end)-TIME(1))*gSetUp.sample_rate) +1; % Number of time samples in a trial

          F = cell(length(no_trials),1); % allocate space for the data array % ASSUME SINGLE TRIAL FOR THE MOMENT - ie enough memory space
          % Adjust time range to the closest time samples;
          if isempty(TIME)
             return
          end

          header = char(fread(meg,8,'char')');
          implicit.sample_rate = 1/(Time(2) - Time(1)); % i.e. the one used in the data file given the time begin_time end period.

          if ((min(TIME) < Time(1)) |(max(TIME) > Time(end))) & VERBOSE
              bst_message_window('wrap',...
                  'ds2brainstorm -> Requested time window outside origial trial time edges; some clipping will occur.')
          end

          tmp = findclosest([TIME],Time);
          t_in = tmp(1);
          t_out = tmp(2);
          no_samples = length(t_in:t_out);

          try
             Time = Time(t_in:t_out); % SB 23-Dec-2002
          catch
             error(...
                sprintf('Data time ranges from %3.1f to %3.2f ms. Please adjust time extraction window',Time(1),Time(end)));
          end

          diff_trials = diff(no_trials)-1; % Number of trials between each selected trials (useful when skipping a few trials)

          ByteSizeOfTrial= no_channels*gSetUp.no_samples*4; % Byte size of a single trial of data (Int32 coding)

          samples_skip = gSetUp.no_samples-no_samples; %Number of time samples to skip per channel

          LastChannelSkip = (no_channels - max(CHANNELS))*gSetUp.no_samples*4; % Skip data from last channels

          channels = [min(CHANNELS):max(CHANNELS)]; % Block of channels to extract.

          FirstChannelSkip = (min(CHANNELS)-1)*gSetUp.no_samples*4; % Skip data from first channels
          no_channels = length(channels);

          itrial = 0;

          for trial = no_trials
             itrial = itrial+1;
             if VERBOSE
                bst_message_window({...
                      sprintf('ds2brainstorm -> Trial %d / %d',itrial,length(no_trials))...
                   })
             end

             F{itrial} = zeros(no_channels,no_samples);

             if trial == no_trials(1) % Read first trial
                fseek(meg,(trial-1)*ByteSizeOfTrial + FirstChannelSkip + (t_in-1)*4 ,0);
             else % just shift from the size of a trial
                fseek(meg,LastChannelSkip + diff_trials(itrial-1)*ByteSizeOfTrial + FirstChannelSkip + (t_in-1)*4 ,0);
             end

             F{itrial} = fread(meg,[no_samples no_channels],[num2str(no_samples),'*int32=>int32'], samples_skip*4)';
             F{itrial} = F{itrial}(CHANNELS-min(CHANNELS)+1,:);

             % IMPORTANT NOTICE : Applying Gradient Correction
             % Data are saved in a given nth-order gradient correction
             % Applying gradient correction is only needed for forward model computation
             % or if it is desired to reverse to lower-order gradient correction (see importdata.m for instance).

             % Apply channel gains
             tmp = gain_chan(CHANNELS);
             tmp(tmp == 0) = eps;
             gain_chan(CHANNELS) = tmp; clear tmp
             F{itrial} = diag(1./gain_chan(CHANNELS))*double(F{itrial});

             % Removing DC Offset
             if isempty(DCOffset)
                DCOffset = 0;
             end
             switch(DCOffset)
             case {1,2}
                if VERBOSE
                   bst_message_window({...
                         'ds2brainstorm -> Removing DC offset'...
                      })
                end

                if DCOffset == 1 % Based on entire trial length
                   F{itrial}(imegsens,:) = F{itrial}(imegsens,:) - repmat(mean(F{itrial}(imegsens,:)')',1,size(F{itrial},2));
                   F{itrial}(irefsens,:) = F{itrial}(irefsens,:) - repmat(mean(F{itrial}(irefsens,:)')',1,size(F{itrial},2));
                else % Based on pretrigger
                   TimeNeg = intersect(find(Time(t_in:t_out) >= TIME(1)),find(Time(t_in:t_out)<0)); % Find pretrigger time points
                   if ~isempty(TimeNeg)
                      F{itrial}(imegsens,:) = F{itrial}(imegsens,:) - repmat(mean(F{itrial}(imegsens,TimeNeg)')',1,size(F{itrial},2));
                      F{itrial}(irefsens,:) = F{itrial}(irefsens,:) - repmat(mean(F{itrial}(irefsens,TimeNeg)')',1,size(F{itrial},2));
                   else
                      % Do nothing
                   end
                end

                if VERBOSE
                   bst_message_window({...
                         'ds2brainstorm -> Removing DC offset - DONE'...
                      })
                end

             otherwise
                % Do nothing
             end

             if VERBOSE
                bst_message_window({...
                      sprintf('ds2brainstorm -> Done')...
                   })
             end

          end

          fclose('all');
       end

    end

    function PrintCoilPos(channel_file, CoilRec_ext,nCoils) ;

    % Subroutine to DS2BRAINSTORM
    % Formatted output of channel locations in a file (channel_file is the pointer to this file)
    % as defined in theCoilRec_ext structure, according to the number of coils nCoils for every gradiometer

    for coil=1:nCoils
       fprintf(channel_file,'Coil %d:', coil);
       fprintf(channel_file,' pX %g,', CoilRec_ext(coil).position.x);
       fprintf(channel_file,' pY %g,', CoilRec_ext(coil).position.y);
       fprintf(channel_file,' pZ %g ', CoilRec_ext(coil).position.z);
       fprintf(channel_file,'-');
       fprintf(channel_file,' oX %g,', CoilRec_ext(coil).orient.x);
       fprintf(channel_file,' oY %g,', CoilRec_ext(coil).orient.y);
       fprintf(channel_file,' oZ %g ', CoilRec_ext(coil).orient.z);
       fprintf(channel_file,'-');
       fprintf(channel_file,' turns %d,', CoilRec_ext(coil).numturns);
       fprintf(channel_file,' area %g', CoilRec_ext(coil).area);

       if coil < nCoils
          fprintf(channel_file,' --- ');
       end

       fprintf(channel_file,'\n');
    end

    return

    function  save_sensor_locs(Channel,SensorLocFile)
    % Save Channel location in a pseudo-result file for visualization in the MRITool

    nchan = length(Channel);
    meg = good_channel(Channel,ones(nchan,1),'MEG');
    eeg = good_channel(Channel,ones(nchan,1),'EEG');

    nchan= length([meg,eeg]);
    SourceLoc = cell(1,nchan);

    i = 0;
    for k = [meg,eeg]
       i = i+1;
       SourceLoc{i} = Channel(k).Loc(:,1);
       SourceOrder(i) = -1;
       Comment = 'Sensor Locations';
    end
    DataFlag = 'Sensors';
    if isdir('BrainStorm')
       save(fullfile('BrainStorm',SensorLocFile),'SourceLoc','SourceOrder','DataFlag','Comment')
    else
       save sensor_result SourceLoc SourceOrder DataFlag Comment
    end

    return

Produced by color_mat2html, a customized BrainStorm 2.0 (Alpha) version of mat2html on Tue Nov 2 16:17:59 2004
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