% REJTREND - detect linear trends in EEG activity and reject the
% epoched trials based on the accuracy of the linear
% fit.
% Usage:
% >> [rej rejE] = rejtrend( signal, winsize, maxslope, minR, step);
%
% Inputs:
% signal - 3 dimensional signal (channels x frames x trials)
% winsize - integer determining the number of consecutive points
% for the detection of linear patterns
% maxslope - maximum acceptable absolute slope of the linear trend. If the slope
% of the line fitted to a data epoch is greater than or equal to
% maxslope, that epoch is rejected (assuming a sufficient R^2 value,
% see minR below).
% minR - minimal R^2 (coefficient of determination between
% 0 and 1). The R^2 value reflects how well the data epoch is
% approximated by a line. An epoch is not rejected unless its R^2
% value is greater than minR.
% step - step for the window. Default is 1 point (2 points
% will divide by two the computation time)
%
% Outputs:
% rej - rejected trials. Array with 0 or 1 for each trial.
% rejE - rejected rows of the rejected trials
%
% Algorithm:
% Looked for all possible windows of size 'winsize' of each trial if
% the linear fit have minimum slope and R^2
%
% Author: Arnaud Delorme, CNL / Salk Institute, 2001
%
% See also: EEGLAB
% Copyright (C) 2001 Arnaud Delorme, Salk Institute, arno@salk.edu
%
% This file is part of EEGLAB, see http://www.eeglab.org
% for the documentation and details.
%
% Redistribution and use in source and binary forms, with or without
% modification, are permitted provided that the following conditions are met:
%
% 1. Redistributions of source code must retain the above copyright notice,
% this list of conditions and the following disclaimer.
%
% 2. Redistributions in binary form must reproduce the above copyright notice,
% this list of conditions and the following disclaimer in the documentation
% and/or other materials provided with the distribution.
%
% THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
% AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
% IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
% ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE
% LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
% CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
% SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
% INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
% CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
% ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF
% THE POSSIBILITY OF SUCH DAMAGE.
function [rej, rejE] = rejtrend( signal, pointrange, maxslope, minstd, step)
% This is to avoid divide-by-zero and machine errors.
SST_TOLERANCE = 1000*pointrange*1.1921e-07;
if nargin < 3
help rejtrend
return;
end
if nargin < 5
step = pointrange;
end
[chans, pnts, trials] = size(signal);
rejE = zeros( chans, trials);
x = linspace( 1/pointrange, 1, pointrange );
%waitbarhandle = waitbar(0,'rejtrend.m Please wait...');
for c = 1:chans
for t = 1:trials
for w = 1:step:(pnts-pointrange+1)
y = signal(c, [w:w+pointrange-1], t);
coef = polyfit(x,y,1);
if abs(coef(1)) >= maxslope
ypred = polyval(coef,x); % predictions
dev = y - mean(y); % deviations - measure of spread
SST = sum(dev.^2); % total variation to be accounted for
if SST < SST_TOLERANCE % make sure SST is not too close to zero
SST = SST_TOLERANCE;
end
resid = y - ypred; % residuals - measure of mismatch
SSE = sum(resid.^2); % variation NOT accounted for
Rsq = 1 - SSE/SST; % percent of error explained
if Rsq > minstd
rejE( c, t ) = 1;
end
% see the page http://www.facstaff.bucknell.edu/maneval/help211/fitting.html
end
end
end
%waitbar(c/chans);
end
%close(waitbarhandle);
rej = max( rejE, [], 1);
return;
Datasets
Models
