minor bug fixes

jkosciessa · Mar 3, 2021 · 8985e3b · 8985e3b
1 parent 65afe06
commit 8985e3b
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Showing 3 changed files with 14 additions and 12 deletions.
diff --git a/eBOSC_example_empirical.m b/eBOSC_example_empirical.m
@@ -34,7 +34,7 @@
 cfg.eBOSC.postproc.effSignal= 'PT';         % Power deconvolution on whole signal or signal above power threshold? (default = 'PT')
 
 % general processing settings
-cfg.eBOSC.channel = [58:60]; % select posterior channels (default: all)
+cfg.eBOSC.channel = [59]; % select posterior channels (default: all)
 cfg.eBOSC.trial = []; % select trials (default: all)
 cfg.eBOSC.trial_background = []; % select trials for background (default: all)
 
@@ -106,17 +106,18 @@
     % Mark all periods with episodes falling into the alpha range
     alphaDetected(eBOSC.episodes.ColID{idx_alpha(indEp)}(1):eBOSC.episodes.ColID{idx_alpha(indEp)}(end)) = 1;
 end; clear idx_alpha;
-h = figure('units','normalized','position',[.1 .1 .7 .3]); hold on; 
-scatter(idx_onset, repmat(100,1,numel(idx_onset)), 75, [.5 .5 .5], 'filled')
+
+h = figure('units','normalized','position',[.1 .1 .3 .2]); hold on; 
+scatter(idx_onsetTime, repmat(100,1,numel(idx_onset)), 75, [.5 .5 .5], 'filled')
 OnsetLine = squeeze(origData);
-OnsetLine(idx_onset) = 100; clear idx_onset idx_onsetTime;
-plot(OnsetLine, 'Color', [.5 .5 .5]); clear OnsetLine;
-[orig]=plot(squeeze(origData), 'k');
-[rhythm]=plot(squeeze(origData).*alphaDetected, 'r');
-xlim([7.2, 7.9]*10^4)
+OnsetLine(idx_onsetTime) = 100; clear idx_onset idx_onsetTime;
+plot(cfg.tmp.finalTime,OnsetLine, 'Color', [.5 .5 .5]); clear OnsetLine;
+[orig]=plot(cfg.tmp.finalTime,squeeze(origData), 'k');
+[rhythm]=plot(cfg.tmp.finalTime,squeeze(origData).*alphaDetected, 'r');
+xlim([162, 167])
 xlabel('Time (s)'); ylabel('Power [µV]');
 legend([orig, rhythm], {'Original signal'; 'Rhythmic signal'}, ...
-    'orientation', 'horizontal', 'location', 'south'); legend('boxoff')
+    'orientation', 'horizontal', 'location', 'NorthEast'); legend('boxoff')
 set(findall(gcf,'-property','FontSize'),'FontSize',26)
 
 %% optional: delete unnecessary fields prior to saving

diff --git a/external/BOSC/BOSC_tf.m b/external/BOSC/BOSC_tf.m
@@ -1 +1 @@
-%    This file is part of the Better OSCillation detection (BOSC) library.%%    The BOSC library 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.%%    The BOSC library 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 Foobar.  If not, see <http://www.gnu.org/licenses/>.%%    Copyright 2010 Jeremy B. Caplan, Adam M. Hughes, Tara A. Whitten%    and Clayton T. Dickson.function [B,T,F]=BOSC_tf(eegsignal,F,Fsample,wavenumber);% [B,T,F]=BOSC_tf(eegsignal,F,Fsample,wavenumber);%% This function computes a continuous wavelet (Morlet) transform on% a segment of EEG signal; this can be used to estimate the% background spectrum (BOSC_bgfit) or to apply the BOSC method to% detect oscillatory episodes in signal of interest (BOSC_detect).%% parameters:% eegsignal - a row vector containing a segment of EEG signal to be%             transformed% F - a set of frequencies to sample (Hz)% Fsample - sampling rate of the time-domain signal (Hz)% wavenumber is the size of the wavelet (typically, width=6)%	% returns:% B - time-frequency spectrogram: power as a function of frequency%     (rows) and time (columns)% T - vector of time values (based on sampling rate, Fsample)st=1./(2*pi*(F/wavenumber));A=1./sqrt(st*sqrt(pi));B = zeros(length(F),length(eegsignal)); % initialize the time-frequency matrixfor f=1:length(F) % loop through sampled frequencies  t=-3.6*st(f):(1/Fsample):3.6*st(f);  m=A(f)*exp(-t.^2/(2*st(f)^2)).*exp(i*2*pi*F(f).*t); % Morlet wavelet  y=conv(eegsignal,m); y=abs(y).^2;  B(f,:)=y(ceil(length(m)/2):length(y)-floor(length(m)/2));endT=(1:size(eegsignal,2))/Fsample;
+%    This file is part of the Better OSCillation detection (BOSC) library.%%    The BOSC library 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.%%    The BOSC library 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 Foobar.  If not, see <http://www.gnu.org/licenses/>.%%    Copyright 2010 Jeremy B. Caplan, Adam M. Hughes, Tara A. Whitten%    and Clayton T. Dickson.function [B,T,F]=BOSC_tf(eegsignal,F,Fsample,wavenumber)% [B,T,F]=BOSC_tf(eegsignal,F,Fsample,wavenumber);%% This function computes a continuous wavelet (Morlet) transform on% a segment of EEG signal; this can be used to estimate the% background spectrum (BOSC_bgfit) or to apply the BOSC method to% detect oscillatory episodes in signal of interest (BOSC_detect).%% parameters:% eegsignal - a row vector containing a segment of EEG signal to be%             transformed% F - a set of frequencies to sample (Hz)% Fsample - sampling rate of the time-domain signal (Hz)% wavenumber is the size of the wavelet (typically, width=6)%	% returns:% B - time-frequency spectrogram: power as a function of frequency%     (rows) and time (columns)% T - vector of time values (based on sampling rate, Fsample)st=1./(2*pi*(F/wavenumber));A=1./sqrt(st*sqrt(pi));B = zeros(length(F),length(eegsignal)); % initialize the time-frequency matrixfor f=1:length(F) % loop through sampled frequencies  t=-3.6*st(f):(1/Fsample):3.6*st(f);  m=A(f)*exp(-t.^2/(2*st(f)^2)).*exp(i*2*pi*F(f).*t); % Morlet wavelet  y=conv(eegsignal,m); y=abs(y).^2;  B(f,:)=y(ceil(length(m)/2):length(y)-floor(length(m)/2));endT=(1:size(eegsignal,2))/Fsample;

diff --git a/internal/eBOSC_getThresholds.m b/internal/eBOSC_getThresholds.m
@@ -51,8 +51,9 @@
             % find empirical peak in specified range
             freqInd1 = find(cfg.eBOSC.F >= cfg.eBOSC.threshold.excludePeak(indExFreq,1), 1, 'first');
             freqInd2 = find(cfg.eBOSC.F <= cfg.eBOSC.threshold.excludePeak(indExFreq,2), 1, 'last');
-            [~, indPos] = max(mean(BG(freqInd1:freqInd2,:),2));
-            indPos = freqInd1+indPos;
+            freqidx = freqInd1:freqInd2;
+            [~, indPos] = max(mean(BG(freqidx,:),2));
+            indPos = freqidx(indPos);
             % approximate wavelet extension in frequency domain
             % note: we do not remove the specified range, but the FWHM
             % around the empirical peak
Original file line number	Diff line number	Diff line change
		@@ -1 +1 @@
		% This file is part of the Better OSCillation detection (BOSC) library.%% The BOSC library 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.%% The BOSC library 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 Foobar. If not, see <http://www.gnu.org/licenses/>.%% Copyright 2010 Jeremy B. Caplan, Adam M. Hughes, Tara A. Whitten% and Clayton T. Dickson.function [B,T,F]=BOSC_tf(eegsignal,F,Fsample,wavenumber);% [B,T,F]=BOSC_tf(eegsignal,F,Fsample,wavenumber);%% This function computes a continuous wavelet (Morlet) transform on% a segment of EEG signal; this can be used to estimate the% background spectrum (BOSC_bgfit) or to apply the BOSC method to% detect oscillatory episodes in signal of interest (BOSC_detect).%% parameters:% eegsignal - a row vector containing a segment of EEG signal to be% transformed% F - a set of frequencies to sample (Hz)% Fsample - sampling rate of the time-domain signal (Hz)% wavenumber is the size of the wavelet (typically, width=6)% % returns:% B - time-frequency spectrogram: power as a function of frequency% (rows) and time (columns)% T - vector of time values (based on sampling rate, Fsample)st=1./(2pi(F/wavenumber));A=1./sqrt(stsqrt(pi));B = zeros(length(F),length(eegsignal)); % initialize the time-frequency matrixfor f=1:length(F) % loop through sampled frequencies t=-3.6st(f):(1/Fsample):3.6st(f); m=A(f)exp(-t.^2/(2st(f)^2)).exp(i2piF(f).t); % Morlet wavelet y=conv(eegsignal,m); y=abs(y).^2; B(f,:)=y(ceil(length(m)/2):length(y)-floor(length(m)/2));endT=(1:size(eegsignal,2))/Fsample;
		% This file is part of the Better OSCillation detection (BOSC) library.%% The BOSC library 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.%% The BOSC library 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 Foobar. If not, see <http://www.gnu.org/licenses/>.%% Copyright 2010 Jeremy B. Caplan, Adam M. Hughes, Tara A. Whitten% and Clayton T. Dickson.function [B,T,F]=BOSC_tf(eegsignal,F,Fsample,wavenumber)% [B,T,F]=BOSC_tf(eegsignal,F,Fsample,wavenumber);%% This function computes a continuous wavelet (Morlet) transform on% a segment of EEG signal; this can be used to estimate the% background spectrum (BOSC_bgfit) or to apply the BOSC method to% detect oscillatory episodes in signal of interest (BOSC_detect).%% parameters:% eegsignal - a row vector containing a segment of EEG signal to be% transformed% F - a set of frequencies to sample (Hz)% Fsample - sampling rate of the time-domain signal (Hz)% wavenumber is the size of the wavelet (typically, width=6)% % returns:% B - time-frequency spectrogram: power as a function of frequency% (rows) and time (columns)% T - vector of time values (based on sampling rate, Fsample)st=1./(2pi(F/wavenumber));A=1./sqrt(stsqrt(pi));B = zeros(length(F),length(eegsignal)); % initialize the time-frequency matrixfor f=1:length(F) % loop through sampled frequencies t=-3.6st(f):(1/Fsample):3.6st(f); m=A(f)exp(-t.^2/(2st(f)^2)).exp(i2piF(f).t); % Morlet wavelet y=conv(eegsignal,m); y=abs(y).^2; B(f,:)=y(ceil(length(m)/2):length(y)-floor(length(m)/2));endT=(1:size(eegsignal,2))/Fsample;
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