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function ft_realtime_average(cfg)
% FT_REALTIME_AVERAGE is an example realtime application for online
% averaging of the data. It should work both for EEG and MEG.
% Use as
% ft_realtime_average(cfg)
% with the following configuration options
% = cell-array, see FT_CHANNELSELECTION (default = 'all')
% cfg.trialfun = string with the trial function
% The source of the data is configured as
% cfg.dataset = string
% or alternatively to obtain more low-level control as
% cfg.datafile = string
% cfg.headerfile = string
% cfg.eventfile = string
% cfg.dataformat = string, default is determined automatic
% cfg.headerformat = string, default is determined automatic
% cfg.eventformat = string, default is determined automatic
% To stop the realtime function, you have to press Ctrl-C
% Copyright (C) 2009, Robert Oostenveld
% This file is part of FieldTrip, see
% 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
% 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 <>.
% $Id$
% set the default configuration options
if ~isfield(cfg, 'dataformat'), cfg.dataformat = []; end % default is detected automatically
if ~isfield(cfg, 'headerformat'), cfg.headerformat = []; end % default is detected automatically
if ~isfield(cfg, 'eventformat'), cfg.eventformat = []; end % default is detected automatically
if ~isfield(cfg, 'channel'), = 'all'; end
if ~isfield(cfg, 'bufferdata'), cfg.bufferdata = 'last'; end % first or last
% translate dataset into datafile+headerfile
cfg = ft_checkconfig(cfg, 'dataset2files', 'yes');
cfg = ft_checkconfig(cfg, 'required', {'datafile' 'headerfile'});
% ensure that the persistent variables related to caching are cleared
clear ft_read_header
% start by reading the header from the realtime buffer
hdr = ft_read_header(cfg.headerfile, 'cache', true);
% define a subset of channels for reading = ft_channelselection(, hdr.label);
chanindx = match_str(hdr.label,;
nchan = length(chanindx);
if nchan==0
ft_error('no channels were selected');
prevSample = 0;
count = 0;
% initialize the average, it will be filled on the first iteration
avgsum = [];
avgnum = [];
% open a figure in which the average will be plotted
% this is the general BCI loop where realtime incoming data is handled
while true
% determine latest header and event information
event = ft_read_event(cfg.dataset, 'minsample', prevSample+1); % only consider events that are later than the data processed sofar
hdr = ft_read_header(cfg.dataset, 'cache', true); % the trialfun might want to use this, but it is not required
cfg.event = event; % store it in the configuration, so that it can be passed on to the trialfun
cfg.hdr = hdr; % store it in the configuration, so that it can be passed on to the trialfun
% evaluate the trialfun, note that the trialfun should not re-read the events and header
fprintf('evaluating ''%s'' based on %d events\n', cfg.trialfun, length(event));
trl = feval(cfg.trialfun, cfg);
fprintf('processing %d trials\n', size(trl,1));
for trllop=1:size(trl,1)
begsample = trl(trllop,1);
endsample = trl(trllop,2);
offset = trl(trllop,3);
% remember up to where the data was read
prevSample = endsample;
count = count + 1;
fprintf('processing segment %d from sample %d to %d\n', count, begsample, endsample);
% read data segment from buffer
dat = ft_read_data(cfg.datafile, 'header', hdr, 'begsample', begsample, 'endsample', endsample, 'chanindx', chanindx, 'checkboundary', false);
% from here onward it is specific to the processing of the data
% apply some preprocessing options
dat = ft_preproc_baselinecorrect(dat);
if isempty(average)
% initialize the accumulating variables on the first call
avgsum = dat;
avgnum = 1;
avgsum = avgsum + dat;
avgnum = avgnum + 1;
% compute the average
avg = avgsum ./ avgnum;
% create a time-axis and plot the average
time = offset2time(offset, hdr.Fs, endsample-begsample+1);
plot(time, avg);
% force matlab to redraw the figure
end % looping over new trials
end % while true
function [time] = offset2time(offset, fsample, nsamples)
offset = double(offset);
nsamples = double(nsamples);
time = (offset + (0:(nsamples-1)))/fsample;