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segment.m
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segment.m
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function [F,keep_neurons]=segment(D, bin_size, Fs, keep_neurons, name_var, maxTime)
%SEGMENT remove low firing rate neurons and segments in non-overlapping
% windows
% namevar: is the name of the field in the structure D which is to be
% segmented.
% Fs: sampling frequency
% keep_neurons: a vector indicating neurons to include '1' and exclude '0'
% bin_size: size of the segmentation bin
% maxTime: maximum segmenting time s
%
%Ruben Pinzon@2015
data = eval(['[D.' name_var ']']);
if length(keep_neurons)==1
min_firing = keep_neurons;
firing_thr = min_firing; % Minimum firing rate find which neurons should be kept
m = mean([data],2) * Fs;
keep_neurons = m >= firing_thr;
else
disp('Vector of neurons to remove provided')
firing_thr = NaN;
end
fprintf('%d neurons remained with firing rate above %2.2f Hz\n',...
sum(keep_neurons),firing_thr)
% Remove low firing rate neurons
for itrial = 1:length(D)
Temp(itrial).data = eval(['D(itrial).' name_var '(keep_neurons,:);']);
end
yDim = sum(keep_neurons);
useSqrt = 1; % square root tranform for pre-processing?
bin_width = ceil(bin_size * Fs); % bin size (Seconds * Fs) = samples
%Extrat bins for one trial, since all the trials
%are of the same duration
for ilap = 1 : length(Temp)
seq = [];
T = floor(size(Temp(ilap).data, 2) / bin_width);
if maxTime ~= 0
T_requested = floor(maxTime * Fs /bin_width);
if T_requested > T
disp('ERROR: Requested time larger than lenght of trial')
return
end
T = T_requested;
end
seq.y = nan(yDim, T);
for t = 1:T
iStart = bin_width * (t-1) + 1;
iEnd = bin_width * t;
seq.y(:,t) = sum(Temp(ilap).data(:, iStart:iEnd), 2)./bin_size;
end
%normalization with square root transform
if useSqrt
seq.y = sqrt(seq.y);
end
F(ilap).trialId = D(ilap).trialId;
if isfield(D, 'type')
F(ilap).type = D(ilap).type;
else
F(ilap).type = D(ilap).trialType;
end
F(ilap).y = seq.y;
F(ilap).T = T;
end