/
pop_loadbv.m
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pop_loadbv.m
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% pop_loadbv() - load Brain Vision Data Exchange format dataset and
% return EEGLAB EEG structure
%
% Usage:
% >> [EEG, com] = pop_loadbv; % pop-up window mode
% >> [EEG, com] = pop_loadbv(path, hdrfile);
% >> [EEG, com] = pop_loadbv(path, hdrfile, srange);
% >> [EEG, com] = pop_loadbv(path, hdrfile, [], chans);
% >> [EEG, com] = pop_loadbv(path, hdrfile, srange, chans);
%
% Optional inputs:
% path - path to files
% hdrfile - name of Brain Vision vhdr-file (incl. extension)
% srange - scalar first sample to read (up to end of file) or
% vector first and last sample to read (e.g., [7 42];
% default: all)
% chans - vector channels channels to read (e.g., [1:2 4];
% default: all)
%
% Outputs:
% EEG - EEGLAB EEG structure
% com - history string
%
% Author: Andreas Widmann & Arnaud Delorme, 2004-
% Copyright (C) 2004 Andreas Widmann, University of Leipzig, widmann@uni-leipzig.de
%
% This program 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 2 of the License, or
% (at your option) any later version.
%
% This program 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 this program; if not, write to the Free Software
% Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA
% $Id: pop_loadbv.m 53 2010-05-22 21:57:38Z arnodelorme $
% Revision 1.5 2010/03/23 21:19:52 roy
% added some lines so that the function can deal with the space lines in the ASCII multiplexed data file
function [EEG, com] = pop_loadbv(path, hdrfile, srange, chans)
com = '';
EEG = [];
if nargin < 2
[hdrfile path] = uigetfile2('*.vhdr', 'Select Brain Vision vhdr-file - pop_loadbv()');
if hdrfile(1) == 0, return; end
drawnow;
uigeom = {[1 0.5] [1 0.5]};
uilist = {{ 'style' 'text' 'string' 'Interval (samples; e.g., [7 42]; default: all):'} ...
{ 'style' 'edit' 'string' ''} ...
{ 'style' 'text' 'string' 'Channels (e.g., [1:2 4]; default: all):'} ...
{ 'style' 'edit' 'string' ''}};
result = inputgui(uigeom, uilist, 'pophelp(''pop_loadbv'')', 'Load a Brain Vision Data Exchange format dataset');
if isempty(result), return, end
if ~isempty(result{1}),
srange = str2num(result{1});
end
if ~isempty(result{2}),
chans = str2num(result{2});
end
end
% Header file
disp('pop_loadbv(): reading header file');
hdr = readbvconf(path, hdrfile);
% Common Infos
try
EEG = eeg_emptyset;
catch
end
EEG.comments = ['Original file: ' hdr.commoninfos.datafile];
hdr.commoninfos.numberofchannels = str2double(hdr.commoninfos.numberofchannels);
EEG.srate = 1000000 / str2double(hdr.commoninfos.samplinginterval);
% Binary Infos
if strcmpi(hdr.commoninfos.dataformat, 'binary')
switch lower(hdr.binaryinfos.binaryformat)
case 'int_16', binformat = 'int16'; bps = 2;
case 'uint_16', binformat = 'uint16'; bps = 2;
case 'ieee_float_32', binformat = 'float32'; bps = 4;
otherwise, error('Unsupported binary format');
end
end
% Channel Infos
if ~exist('chans', 'var')
chans = 1:hdr.commoninfos.numberofchannels;
EEG.nbchan = hdr.commoninfos.numberofchannels;
elseif isempty(chans)
chans = 1:hdr.commoninfos.numberofchannels;
EEG.nbchan = hdr.commoninfos.numberofchannels;
else
EEG.nbchan = length(chans);
end
if any(chans < 1) || any(chans > hdr.commoninfos.numberofchannels)
error('chans out of available channel range');
end
if isfield(hdr, 'channelinfos')
for chan = 1:length(chans)
try
[EEG.chanlocs(chan).labels, chanlocs(chan).ref, chanlocs(chan).scale, chanlocs(chan).unit] = strread(hdr.channelinfos{chans(chan)}, '%s%s%s%s', 1, 'delimiter', ',');
catch % Octave compatible code below
str = hdr.channelinfos{chans(chan)};
[EEG.chanlocs(chan).labels str] = strtok(str, ',');
[chanlocs(chan).ref str] = strtok(str, ',');
[chanlocs(chan).scale str] = strtok(str, ',');
[chanlocs(chan).unit str] = strtok(str, ',');
end;
EEG.chanlocs(chan).labels = char(EEG.chanlocs(chan).labels);
chanlocs(chan).scale = str2double(char(chanlocs(chan).scale));
% chanlocs(chan).unit = native2unicode(double(char(chanlocs(chan).scale)), 'UTF-8');
% EEG.chanlocs(chan).datachan = chans(chan);
end
if isempty([chanlocs.scale])
chanlocs = rmfield(chanlocs, 'scale');
end
end;
% [EEG.chanlocs.type] = deal([]);
% Coordinates
if isfield(hdr, 'coordinates')
hdr.coordinates(end+1:length(chans)) = { [] };
onenon0channel = 0;
for chan = 1:length(chans)
if ~isempty(hdr.coordinates{chans(chan)})
if ismatlab,
[EEG.chanlocs(chan).sph_radius, theta, phi] = strread(hdr.coordinates{chans(chan)}, '%f%f%f', 'delimiter', ',');
else
str = hdr.coordinates{chans(chan)};
[EEG.chanlocs(chan).sph_radius str] = strtok(str, ','); EEG.chanlocs(chan).sph_radius = str2num(EEG.chanlocs(chan).sph_radius);
[theta str] = strtok(str, ','); theta = str2num(theta);
[phi str] = strtok(str, ','); phi = str2num(phi);
end;
if EEG.chanlocs(chan).sph_radius == 0 && theta == 0 && phi == 0
EEG.chanlocs(chan).sph_radius = [];
EEG.chanlocs(chan).sph_theta = [];
EEG.chanlocs(chan).sph_phi = [];
else
onenon0channel = 1;
EEG.chanlocs(chan).sph_theta = phi - 90 * sign(theta);
EEG.chanlocs(chan).sph_phi = -abs(theta) + 90;
end
end;
end
try,
if onenon0channel
[EEG.chanlocs, EEG.chaninfo] = pop_chanedit(EEG.chanlocs, 'convert', 'sph2topo');
[EEG.chanlocs, EEG.chaninfo] = pop_chanedit(EEG.chanlocs, 'convert', 'sph2cart');
end;
catch, end
end
% Open data file and find the number of data points
% -------------------------------------------------
disp('pop_loadbv(): reading EEG data');
[IN, message] = fopen(fullfile(path, hdr.commoninfos.datafile), 'r');
if IN == -1
[IN, message] = fopen(fullfile(path, lower(hdr.commoninfos.datafile)));
if IN == -1
error(message)
end;
end
if isfield(hdr.commoninfos, 'datapoints')
if isempty(hdr.commoninfos.datapoints)
fseek(IN, 0, 'eof');
hdr.commoninfos.datapoints = ftell(IN) / (hdr.commoninfos.numberofchannels * bps);
fseek(IN, 0, 'bof');
else
hdr.commoninfos.datapoints = str2double(hdr.commoninfos.datapoints);
end;
elseif strcmpi(hdr.commoninfos.dataformat, 'binary')
fseek(IN, 0, 'eof');
hdr.commoninfos.datapoints = ftell(IN) / (hdr.commoninfos.numberofchannels * bps);
fseek(IN, 0, 'bof');
else
hdr.commoninfos.datapoints = NaN;
end
if ~strcmpi(hdr.commoninfos.dataformat, 'binary') % ASCII
tmppoint = hdr.commoninfos.datapoints;
tmpchan = fscanf(IN, '%s', 1);
tmpdata = fscanf(IN, '%f', inf);
hdr.commoninfos.datapoints = length(tmpdata);
chanlabels = 1;
if isnan(str2double(tmpchan))
hdr.commoninfos.datapoints = hdr.commoninfos.datapoints+1;
chanlabels = 0;
end;
end;
% Sample range
if ~exist('srange', 'var') || isempty(srange)
srange = [ 1 hdr.commoninfos.datapoints];
EEG.pnts = hdr.commoninfos.datapoints;
elseif length(srange) == 1
EEG.pnts = hdr.commoninfos.datapoints - srange(1) + 1;
else
EEG.pnts = srange(2) - srange(1) + 1;
end
if any(srange < 1) || any(srange > hdr.commoninfos.datapoints)
error('srange out of available data range');
end
% Read data
if strcmpi(hdr.commoninfos.dataformat, 'binary')
switch lower(hdr.commoninfos.dataorientation)
case 'multiplexed'
if EEG.nbchan == hdr.commoninfos.numberofchannels % Read all channels
fseek(IN, (srange(1) - 1) * EEG.nbchan * bps, 'bof');
EEG.data = fread(IN, [EEG.nbchan, EEG.pnts], [binformat '=>float32']);
else % Read channel subset
EEG.data = repmat(single(0), [EEG.nbchan, EEG.pnts]); % Preallocate memory
for chan = 1:length(chans)
fseek(IN, (srange(1) - 1) * hdr.commoninfos.numberofchannels * bps + (chans(chan) - 1) * bps, 'bof');
EEG.data(chan, :) = fread(IN, [1, EEG.pnts], [binformat '=>float32'], (hdr.commoninfos.numberofchannels - 1) * bps);
end
end
case 'vectorized'
if isequal(EEG.pnts, hdr.commoninfos.datapoints) && EEG.nbchan == hdr.commoninfos.numberofchannels % Read entire file
EEG.data = fread(IN, [EEG.pnts, EEG.nbchan], [binformat '=>float32']).';
else % Read fraction of file
EEG.data = repmat(single(0), [EEG.nbchan, EEG.pnts]); % Preallocate memory
for chan = 1:length(chans)
fseek(IN, ((chans(chan) - 1) * hdr.commoninfos.datapoints + srange(1) - 1) * bps, 'bof');
EEG.data(chan, :) = fread(IN, [1, EEG.pnts], [binformat '=>float32']);
end
end
otherwise
error('Unsupported data orientation')
end
else % ASCII data
disp('If this function does not work, export your data in binary format');
EEG.data = repmat(single(0), [EEG.nbchan, EEG.pnts]);
if strcmpi(lower(hdr.commoninfos.dataorientation), 'vectorized')
count = 1;
fseek(IN, 0, 'bof');
len = inf;
for chan = 1:hdr.commoninfos.numberofchannels
if chanlabels, tmpchan = fscanf(IN, '%s', 1); end;
tmpdata = fscanf(IN, '%f', len); len = length(tmpdata);
if ismember(chan, chans)
EEG.data(count, :) = tmpdata(srange(1):srange(2))';
count = count + 1;
end;
end;
elseif strcmpi(lower(hdr.commoninfos.dataorientation), 'multiplexed')
% fclose(IN);
% error('ASCII multiplexed reading not implemeted yet; export as a different format');
if EEG.nbchan == hdr.commoninfos.numberofchannels % Read all channels
tmpchan= fgetl(IN);
count = 1;
while ~feof(IN)
tmpstr = fgetl(IN);
if ~isempty(tmpstr)
temp_ind = tmpstr==',';
tmpstr(temp_ind) = '.';
tmpdata = strread(tmpstr);
EEG.data(:,count) = tmpdata';
count = count + 1;
end;
end;
EEG.pnts = count - 1;
else
end;
end;
end;
fclose(IN);
EEG.trials = 1;
EEG.xmin = 0;
EEG.xmax = (EEG.pnts - 1) / EEG.srate;
% Convert to EEG.data to double for MATLAB < R14
if str2double(version('-release')) < 14
EEG.data = double(EEG.data);
end
% Scale data
if exist('chanlocs', 'var') && isfield(chanlocs, 'scale')
disp('pop_loadbv(): scaling EEG data');
for chan = 1:EEG.nbchan
if ~isnan(chanlocs(chan).scale)
EEG.data(chan, :) = EEG.data(chan, :) * chanlocs(chan).scale;
end;
end
end
% Marker file
if isfield(hdr.commoninfos, 'markerfile')
disp('pop_loadbv(): reading marker file');
MRK = readbvconf(path, hdr.commoninfos.markerfile);
if hdr.commoninfos.datafile ~= MRK.commoninfos.datafile
disp('pop_loadbv() warning: data files in header and marker files inconsistent.');
end
% Marker infos
if isfield(MRK, 'markerinfos')
EEG.event = parsebvmrk(MRK);
% Correct event latencies by first sample offset
tmpevent = EEG.event;
for index = 1:length(EEG.event)
tmpevent(index).latency = tmpevent(index).latency - srange(1) + 1;
end;
EEG.event = tmpevent;
% Remove unreferenced events
EEG.event = EEG.event([tmpevent.latency] >= 1 & [tmpevent.latency] <= EEG.pnts);
% Copy event structure to urevent structure
EEG.urevent = rmfield(EEG.event, 'urevent');
% find if boundaries at homogenous intervals
% ------------------------------------------
tmpevent = EEG.event;
boundaries = strmatch('boundary', {tmpevent.type});
boundlats = unique([tmpevent(boundaries).latency]);
if (isfield(hdr.commoninfos, 'segmentationtype') && (strcmpi(hdr.commoninfos.segmentationtype, 'markerbased') || strcmpi(hdr.commoninfos.segmentationtype, 'fixtime'))) && length(boundaries) > 1 && length(unique(diff([boundlats EEG.pnts + 1]))) == 1
EEG.trials = length(boundlats);
EEG.pnts = EEG.pnts / EEG.trials;
EEG.event(boundaries) = [];
% adding epoch field
% ------------------
tmpevent = EEG.event;
for index = 1:length(EEG.event)
EEG.event(index).epoch = ceil(tmpevent(index).latency / EEG.pnts);
end
% finding minimum time
% --------------------
tles = strmatch('time 0', lower({tmpevent.code}))';
if ~isempty(tles)
for iTLE = tles(:)'
EEG.event(iTLE).type ='TLE';
end
EEG.xmin = -(tmpevent(tles(1)).latency - 1) / EEG.srate;
end
end
end
end
EEG.ref = 'common';
try
EEG = eeg_checkset(EEG);
catch
end
if nargout == 2
com = sprintf('EEG = pop_loadbv(''%s'', ''%s'', %s, %s);', path, hdrfile, mat2str(srange), mat2str(chans));
end