function ft_realtime_selectiveaverage(cfg)

% FT_REALTIME_SELECTIVEAVERAGE is an example realtime application for online

% averaging of the data. It should work both for EEG and MEG.

%

% Use as

% ft_realtime_selectiveaverage(cfg)

% with the following configuration options

% cfg.channel = 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) 2008, 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/>.

%

% $Id$

% 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'), cfg.channel = 'all'; end

if ~isfield(cfg, 'bufferdata'), cfg.bufferdata = 'last'; end % first or last

if ~isfield(cfg, 'jumptoeof'), cfg.jumptoeof = 'no'; end % jump to end of file at initialization

% 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

cfg.channel = ft_channelselection(cfg.channel, hdr.label);

chanindx = match_str(hdr.label, cfg.channel);

nchan = length(chanindx);

if nchan==0

ft_error('no channels were selected');

end

if strcmp(cfg.jumptoeof, 'yes')

prevSample = hdr.nSamples * hdr.nTrials;

else

prevSample = 0;

end

count = 0;

% initialize the timelock cell-array, each cell will hold the average in one condition

timelock = {};

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

% 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);

% the code below assumes that the 4th column of the trl matrix contains the condition index

% set the default condition to one if no condition index was given

if size(trl,1)>0 && size(trl,2)<4

trl(:,4) = 1;

end

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);

condition = trl(trllop,4);

% remember up to where the data was read

prevSample = endsample;

count = count + 1;

fprintf('processing segment %d from sample %d to %d, condition = %d\n', count, begsample, endsample, condition);

% 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

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

% put the data in a fieldtrip-like raw structure

data.trial{1} = dat;

data.time{1} = offset2time(offset, hdr.Fs, endsample-begsample+1);

data.label = hdr.label(chanindx);

data.hdr = hdr;

data.fsample = hdr.Fs;

% apply some preprocessing options

data.trial{1} = ft_preproc_baselinecorrect(data.trial{1});

if length(timelock)<condition || isempty(timelock{condition})

% this is the first occurence of this condition, initialize an empty timelock structure

timelock{condition}.label = data.label;

timelock{condition}.time = data.time{1};

timelock{condition}.avg = [];

timelock{condition}.var = [];

timelock{condition}.dimord = 'chan_time';

nchans = size(data.trial{1}, 1);

nsamples = size(data.trial{1}, 2);

% the following elements are for the cumulative computation

timelock{condition}.n = 0; % number of trials

timelock{condition}.s = zeros(nchans, nsamples); % sum

timelock{condition}.ss = zeros(nchans, nsamples); % sum of squares

end

% add the new data to the accumulated data

timelock{condition}.n = timelock{condition}.n + 1;

timelock{condition}.s = timelock{condition}.s + data.trial{1};

timelock{condition}.ss = timelock{condition}.ss + data.trial{1}.^2;

% compute the average and variance on the fly

timelock{condition}.avg = timelock{condition}.s ./ timelock{condition}.n;

timelock{condition}.var = (timelock{condition}.ss - (timelock{condition}.s.^2)./timelock{condition}.n) ./ (timelock{condition}.n-1);

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

% from here onward additional processing of the selective averages could be done

% as an example here the ERP of each condition is plotted in its own figure

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

% compute the t-score versus zero by dividing the average by the standard error of mean

tscore = timelock{condition}.avg ./ (sqrt(timelock{condition}.var)./(timelock{condition}.n - 1));

figure(condition)

plot(timelock{condition}.time, tscore);

title(sprintf('condition %d, ntrials = %d', condition, timelock{condition}.n));

% force matlab to redraw the figure

drawnow

end % looping over new trials

end % while true

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

% SUBFUNCTION

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

function [time] = offset2time(offset, fsample, nsamples)

offset = double(offset);

nsamples = double(nsamples);

time = (offset + (0:(nsamples-1)))/fsample;