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best_fieldtrip.m
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classdef best_fieldtrip <handle
properties
best_toolbox
fieldtrip
end
methods
function obj = best_fieldtrip(best_toolbox)
obj.best_toolbox=best_toolbox;
end
function irasa(obj,EEGData,InputDevice)
%% Just for testing purposes
% load S5_raw_segmented.mat
% EEGData=data;
% cfg=[];
% cfg.channel =data.label(1:64);
% EEGData=ft_selectdata(cfg,EEGData);
% obj.best_toolbox.inputs.results.RawEEGData=EEGData;
%% Stop Check Point
if obj.best_toolbox.inputs.stop_event==1, return, end
%% Modifying IRASA
EEGChannelsIndex=find(strcmp(obj.best_toolbox.app.par.hardware_settings.(InputDevice).NeurOneProtocolChannelSignalTypes,'EEG'));
EEGChanelsLabels=obj.best_toolbox.app.par.hardware_settings.(InputDevice).NeurOneProtocolChannelLabels(EEGChannelsIndex);
%% Creating RawEEGData
obj.best_toolbox.inputs.results.RawEEGData.label=EEGChanelsLabels';
obj.best_toolbox.inputs.results.RawEEGData.fsample=500;
obj.best_toolbox.inputs.results.RawEEGData.trial={EEGData.Data};
obj.best_toolbox.inputs.results.RawEEGData.time={EEGData.Time};
%% PreProcessing RawEEGData
cfg=[];
cfg.demean=[];
cfg.detrend=[];
if ~isempty(obj.best_toolbox.inputs.HighPassFrequency)
cfg.hpfilter = 'yes'; % high-pass in order to get rid of low-freq trends
cfg.hpfiltord = obj.best_toolbox.inputs.HighPassFilterOrder;
cfg.hpfreq = obj.best_toolbox.inputs.HighPassFrequency;
end
if ~isempty(obj.best_toolbox.inputs.BandStopFrequency)
cfg.bsfilter = 'yes'; % band-stop filter, to take out 50 Hz and its harmonics
cfg.bsfiltord = obj.best_toolbox.inputs.BandStopFilterOrder;
cfg.bsfreq = obj.best_toolbox.inputs.BandStopFrequency;
end
if ~isempty(obj.best_toolbox.inputs.ReferenceChannels)
cfg.reref = 'yes';
cfg.refchannel = obj.best_toolbox.inputs.ReferenceChannels;
end
if ~isempty(obj.best_toolbox.inputs.RecordingReference)
cfg.implicitref = obj.best_toolbox.inputs.RecordingReference;
end
Data = ft_preprocessing(cfg,obj.best_toolbox.inputs.results.RawEEGData);
%% Applying Montage on Preprocessed Data, Creating Montage Channels
SelectedData={};
for channel=1:numel(obj.best_toolbox.inputs.MontageChannels)
SelectedData{channel}=obj.best_toolbox.inputs.MontageChannels{channel};
if iscell(obj.best_toolbox.inputs.MontageChannels{channel})
cfg=[];
cfg.labelold =obj.best_toolbox.inputs.MontageChannels{channel};
cfg.labelnew ={erase(char(join(obj.best_toolbox.inputs.MontageChannels{channel})),' ')};
cfg.tra =cell2mat(obj.best_toolbox.inputs.MontageWeights{channel});
Montage=ft_apply_montage(Data,cfg);
Data=ft_appenddata([], Data, Montage);
SelectedData{channel}=char(cfg.labelnew);
end
end
%% Selected Data
cfg=[];
cfg.channel =SelectedData;
Data=ft_selectdata(cfg,Data);
%% Segmented Data
cfg = [];
cfg.length = obj.best_toolbox.inputs.EEGEpochPeriod;
cfg.overlap = 0;
Data = ft_redefinetrial(cfg, Data);
for itrialinfo=1:numel(Data.trial)
Data.trialinfo(itrialinfo,1)=itrialinfo;
end
%% Overlapped Data etc
w = Data.time{1}(end)-Data.time{1}(1); % window length
cfg = [];
cfg.length = w*.9;
cfg.overlap = 1-(((w-cfg.length)/cfg.length)/(10-1));
Data = ft_redefinetrial(cfg, Data);
%% Stop Check Point
if obj.best_toolbox.inputs.stop_event==1, return, end
%% Orignial, Fractal and Oscillation Components Spectral Analysis
cfg = [];
cfg.foi = [1:1/obj.best_toolbox.inputs.EEGEpochPeriod:45];
cfg.taper = 'hanning';
cfg.pad = 'nextpow2';
cfg.keeptrials = 'yes';
cfg.method = 'irasa';
frac_r = ft_freqanalysis(cfg,Data); % Frac
cfg.method = 'mtmfft';
orig_r = ft_freqanalysis(cfg, Data); %Raw
% average across the sub-segments
frac_s = {};
orig_s = {};
for rpt = unique(frac_r.trialinfo(:,end))'
cfg = [];
cfg.trials = find(frac_r.trialinfo(:,end)==rpt);
cfg.avgoverrpt = 'yes';
frac_s{end+1} = ft_selectdata(cfg, frac_r);
orig_s{end+1} = ft_selectdata(cfg, orig_r);
end
frac_a = ft_appendfreq([], frac_s{:}); %Frac
orig_a = ft_appendfreq([], orig_s{:}); %Raw
% average across trials
cfg = [];
cfg.trials = 'all';
cfg.avgoverrpt = 'yes';
obj.best_toolbox.inputs.results.FractalComponents = ft_selectdata(cfg, frac_a); %Frac
obj.best_toolbox.inputs.results.OriginalComponents = ft_selectdata(cfg, orig_a); %Raw
% subtract the fractal component from the power spectrum
cfg = [];
cfg.parameter = 'powspctrm';
cfg.operation = 'x2-x1';
obj.best_toolbox.inputs.results.OscillationComponents = ft_math(cfg, obj.best_toolbox.inputs.results.FractalComponents, obj.best_toolbox.inputs.results.OriginalComponents); %Osci
%% Percentage Difference
obj.best_toolbox.inputs.results.percentageOscillationOverFractalComponent.powspctrm=100*(obj.best_toolbox.inputs.results.OscillationComponents.powspctrm./obj.best_toolbox.inputs.results.FractalComponents.powspctrm);
obj.best_toolbox.inputs.results.percentageOscillationOverFractalComponent.freq=obj.best_toolbox.inputs.results.FractalComponents.freq;
%% DB Scaling
obj.best_toolbox.inputs.results.dbOscillationOverFractalComponent.powspctrm=real(log10(100*(obj.best_toolbox.inputs.results.percentageOscillationOverFractalComponent.powspctrm)));
obj.best_toolbox.inputs.results.dbOscillationOverFractalComponent.freq=obj.best_toolbox.inputs.results.FractalComponents.freq;
%% Stop Check Point
if obj.best_toolbox.inputs.stop_event==1, return, end
%% Plotting , Annotating and Saving Peak Frequency
[~,iTargetFrequencyRange1]=min(abs(obj.best_toolbox.inputs.results.OriginalComponents.freq - obj.best_toolbox.inputs.TargetFrequencyRange(1)));
[~,iTargetFrequencyRange2]=min(abs(obj.best_toolbox.inputs.results.OriginalComponents.freq - obj.best_toolbox.inputs.TargetFrequencyRange(2)));
TargetFrequencyRange=iTargetFrequencyRange1:iTargetFrequencyRange2;
for channel=1:numel(obj.best_toolbox.inputs.results.OriginalComponents.label)
ax1=['ax' num2str(channel*4-3)];
axes(obj.best_toolbox.app.pr.ax.(ax1))
plot(obj.best_toolbox.inputs.results.FractalComponents.freq, obj.best_toolbox.inputs.results.FractalComponents.powspctrm(channel,:),'linewidth', 3, 'color', [0 0 0],'DisplayName','Fractal'); hold on; legend;
plot(obj.best_toolbox.inputs.results.OriginalComponents.freq, obj.best_toolbox.inputs.results.OriginalComponents.powspctrm(channel,:),'linewidth', 3, 'color', [0.6 0.6 0.6],'DisplayName','Original')
grid=gridxy((obj.best_toolbox.inputs.TargetFrequencyRange(1)):0.25:(obj.best_toolbox.inputs.TargetFrequencyRange(2)),'Color',[219/255 246/255 255/255],'linewidth',4) ;
grid.Annotation.LegendInformation.IconDisplayStyle = 'off';
xlabel('Frequency (Hz)'), ylabel('Power'); xticks(unique((sort([obj.best_toolbox.app.pr.ax.(ax1).XTick obj.best_toolbox.inputs.TargetFrequencyRange(1) obj.best_toolbox.inputs.TargetFrequencyRange(2)]))));
ax2=['ax' num2str(channel*4-2)];
axes(obj.best_toolbox.app.pr.ax.(ax2))
plot(obj.best_toolbox.inputs.results.OscillationComponents.freq, obj.best_toolbox.inputs.results.OscillationComponents.powspctrm(channel,:),'linewidth', 3, 'color', [0 0 0]); hold on;
gridxy((obj.best_toolbox.inputs.TargetFrequencyRange(1)):0.25:(obj.best_toolbox.inputs.TargetFrequencyRange(2)),'Color',[219/255 246/255 255/255],'linewidth',4) ;
%Find Peak Frequency, store it and annotate it
% 15.02.2021 Find APeriodic/Periodic ratio,store,annotate
[~,PeakPowerIndex] = find(obj.best_toolbox.inputs.results.OscillationComponents.powspctrm(channel,:)==max(obj.best_toolbox.inputs.results.OscillationComponents.powspctrm(channel,TargetFrequencyRange)));
obj.best_toolbox.inputs.results.PeakFrequency.(obj.best_toolbox.inputs.results.OscillationComponents.label{channel})=obj.best_toolbox.inputs.results.OscillationComponents.freq(1,PeakPowerIndex);
[RawPeakPowerValue,FractalPeakPowerValue,APeriodicPercentile,APeriodicSNRLimitExceeded]=getAPeriodicRatio(PeakPowerIndex);
obj.best_toolbox.app.pr.ax.(ax2).UserData.TextAnnotationPeakFrequency=text(obj.best_toolbox.app.pr.ax.(ax2),1,1,{'';['Peak Freq (Hz):' num2str(obj.best_toolbox.inputs.results.PeakFrequency.(obj.best_toolbox.inputs.results.OscillationComponents.label{channel}))];['APeriodic Percentile (%):' num2str(APeriodicPercentile)];['Raw Peak (Hz):' num2str(RawPeakPowerValue)];['Fractal Peak (Hz):' num2str(FractalPeakPowerValue)];['APeriodic SNR Limit Exceeded:' APeriodicSNRLimitExceeded]},'units','normalized','HorizontalAlignment','right','VerticalAlignment','cap','color',[0.45 0.45 0.45]);
obj.best_toolbox.inputs.results.APeriodicComponentPercentile.(obj.best_toolbox.inputs.results.OscillationComponents.label{channel})=APeriodicPercentile;
grid.Annotation.LegendInformation.IconDisplayStyle = 'off';
xlabel('Frequency (Hz)'), ylabel('Power'); xticks(unique((sort([obj.best_toolbox.app.pr.ax.(ax2).XTick obj.best_toolbox.inputs.TargetFrequencyRange(1) obj.best_toolbox.inputs.TargetFrequencyRange(2)]))));
ax3=['ax' num2str(channel*4-1)];
axes(obj.best_toolbox.app.pr.ax.(ax3))
plot(obj.best_toolbox.inputs.results.percentageOscillationOverFractalComponent.freq, obj.best_toolbox.inputs.results.percentageOscillationOverFractalComponent.powspctrm(channel,:),'linewidth', 3, 'color', [0 0 0]); hold on;
gridxy((obj.best_toolbox.inputs.TargetFrequencyRange(1)):0.25:(obj.best_toolbox.inputs.TargetFrequencyRange(2)),'Color',[219/255 246/255 255/255],'linewidth',4) ;
grid.Annotation.LegendInformation.IconDisplayStyle = 'off';
xlabel('Frequency (Hz)'), ylabel('Power'); xticks(unique((sort([obj.best_toolbox.app.pr.ax.(ax3).XTick obj.best_toolbox.inputs.TargetFrequencyRange(1) obj.best_toolbox.inputs.TargetFrequencyRange(2)]))));
ax4=['ax' num2str(channel*4)];
axes(obj.best_toolbox.app.pr.ax.(ax4))
plot(obj.best_toolbox.inputs.results.dbOscillationOverFractalComponent.freq, obj.best_toolbox.inputs.results.dbOscillationOverFractalComponent.powspctrm(channel,:),'linewidth', 3, 'color', [0 0 0]); hold on;
gridxy((obj.best_toolbox.inputs.TargetFrequencyRange(1)):0.25:(obj.best_toolbox.inputs.TargetFrequencyRange(2)),'Color',[219/255 246/255 255/255],'linewidth',4) ;
grid.Annotation.LegendInformation.IconDisplayStyle = 'off';
xlabel('Frequency (Hz)'), ylabel('Power'); xticks(unique((sort([obj.best_toolbox.app.pr.ax.(ax4).XTick obj.best_toolbox.inputs.TargetFrequencyRange(1) obj.best_toolbox.inputs.TargetFrequencyRange(2)]))));
end
%% Showing Results on Table
function [RawPeakPowerValue,FractalPeakPowerValue,APeriodicPercentile,APeriodicSNRLimitExceeded]= getAPeriodicRatio(PeakPowerIndex)
RawPeakPowerValue=obj.best_toolbox.inputs.results.OriginalComponents.powspctrm(1,PeakPowerIndex);
FractalPeakPowerValue=obj.best_toolbox.inputs.results.FractalComponents.powspctrm(1,PeakPowerIndex);
APeriodicPercentile=round((FractalPeakPowerValue/RawPeakPowerValue)*100);
APeriodicSNRLimitExceeded='No';
if APeriodicPercentile>50, APeriodicSNRLimitExceeded='Yes'; end
end
end
function fft(obj,EEGData,InputDevice)
%% Just for testing purposes
% load S5_raw_segmented.mat
% EEGData=data;
% cfg=[];
% cfg.channel =data.label(1:64);
% EEGData=ft_selectdata(cfg,EEGData);
% obj.best_toolbox.inputs.results.RawEEGData=EEGData;
%% Stop Check Point
if obj.best_toolbox.inputs.stop_event==1, return, end
%% Getting ChannelIndex and Labels
EEGChannelsIndex=find(strcmp(obj.best_toolbox.app.par.hardware_settings.(InputDevice).NeurOneProtocolChannelSignalTypes,'EEG'));
EEGChanelsLabels=obj.best_toolbox.app.par.hardware_settings.(InputDevice).NeurOneProtocolChannelLabels(EEGChannelsIndex);
%% Creating RawEEGData
obj.best_toolbox.inputs.results.RawEEGData.label=EEGChanelsLabels';
obj.best_toolbox.inputs.results.RawEEGData.fsample=5000;
obj.best_toolbox.inputs.results.RawEEGData.trial={EEGData.Data};
obj.best_toolbox.inputs.results.RawEEGData.time={EEGData.Time};
%% PreProcessing RawEEGData
cfg=[];
cfg.demean=[];
cfg.detrend=[];
if ~isempty(obj.best_toolbox.inputs.HighPassFrequency)
cfg.hpfilter = 'yes'; % high-pass in order to get rid of low-freq trends
cfg.hpfiltord = obj.best_toolbox.inputs.HighPassFilterOrder;
cfg.hpfreq = obj.best_toolbox.inputs.HighPassFrequency;
end
if ~isempty(obj.best_toolbox.inputs.BandStopFrequency)
cfg.bsfilter = 'yes'; % band-stop filter, to take out 50 Hz and its harmonics
cfg.bsfiltord = obj.best_toolbox.inputs.BandStopFilterOrder;
cfg.bsfreq = obj.best_toolbox.inputs.BandStopFrequency;
end
if ~isempty(obj.best_toolbox.inputs.ReferenceChannels)
cfg.reref = 'yes'; % band-stop filter, to take out 50 Hz and its harmonics
cfg.refchannel = obj.best_toolbox.inputs.ReferenceChannels;
end
if ~isempty(obj.best_toolbox.inputs.ReferenceChannels)
cfg.reref = 'yes';
cfg.refchannel = obj.best_toolbox.inputs.ReferenceChannels;
cfg.implicitref = obj.best_toolbox.inputs.RecordingReference;
end
Data = ft_preprocessing(cfg,obj.best_toolbox.inputs.results.RawEEGData);
%% Applying Montage on Preprocessed Data, Creating Montage Channels
SelectedData={};
for channel=1:numel(obj.best_toolbox.inputs.MontageChannels)
SelectedData{channel}=obj.best_toolbox.inputs.MontageChannels{channel};
if iscell(obj.best_toolbox.inputs.MontageChannels{channel})
cfg=[];
cfg.labelold =obj.best_toolbox.inputs.MontageChannels{channel};
cfg.labelnew ={erase(char(join(obj.best_toolbox.inputs.MontageChannels{channel})),' ')};
cfg.tra =cell2mat(obj.best_toolbox.inputs.MontageWeights{channel});
Montage=ft_apply_montage(Data,cfg);
Data=ft_appenddata([], Data, Montage);
SelectedData{channel}=char(cfg.labelnew);
end
end
%% Selected Data
cfg=[];
cfg.channel =SelectedData;
Data=ft_selectdata(cfg,Data);
%% Segmented Data
cfg = [];
cfg.length = obj.best_toolbox.inputs.EEGEpochPeriod;
cfg.overlap = 0;
Data = ft_redefinetrial(cfg, Data);
%% Overlapped Data
w = Data.time{1}(end)-Data.time{1}(1); % window length
cfg = [];
cfg.length = w*.9;
cfg.overlap = 1-(((w-cfg.length)/cfg.length)/(10-1));
Data = ft_redefinetrial(cfg, Data);
%% Stop Check Point
if obj.best_toolbox.inputs.stop_event==1, return, end
%% Orignial, Fractal and Oscillation Components Spectral Analysis
cfg = [];
cfg.foi = [1:1/obj.best_toolbox.inputs.EEGEpochPeriod:45];
cfg.taper = 'hanning';
cfg.pad = 'nextpow2';
cfg.method = 'mtmfft';
obj.best_toolbox.inputs.results.OriginalComponents = ft_freqanalysis(cfg, Data); %Raw
%% Stop Check Point
if obj.best_toolbox.inputs.stop_event==1, return, end
%% Plotting , Annotating and Saving Peak Frequency
[~,iTargetFrequencyRange1]=min(abs(obj.best_toolbox.inputs.results.OriginalComponents.freq - obj.best_toolbox.inputs.TargetFrequencyRange(1)));
[~,iTargetFrequencyRange2]=min(abs(obj.best_toolbox.inputs.results.OriginalComponents.freq - obj.best_toolbox.inputs.TargetFrequencyRange(2)));
TargetFrequencyRange=iTargetFrequencyRange1:iTargetFrequencyRange2;
for channel=1:numel(obj.best_toolbox.inputs.results.OriginalComponents.label)
ax2=['ax' num2str(channel)];
axes(obj.best_toolbox.app.pr.ax.(ax2))
plot(obj.best_toolbox.inputs.results.OriginalComponents.freq, obj.best_toolbox.inputs.results.OriginalComponents.powspctrm(channel,:),'linewidth', 3, 'color', [0 0 0]); hold on;
gridxy((obj.best_toolbox.inputs.TargetFrequencyRange(1)):0.25:(obj.best_toolbox.inputs.TargetFrequencyRange(2)),'Color',[219/255 246/255 255/255],'linewidth',4) ;
%Find Peak Frequency, store it and annotate it
[~,PeakPowerIndex] = find(obj.best_toolbox.inputs.results.OriginalComponents.powspctrm(channel,:)==max(obj.best_toolbox.inputs.results.OriginalComponents.powspctrm(channel,TargetFrequencyRange)));
obj.best_toolbox.inputs.results.PeakFrequency.(obj.best_toolbox.inputs.results.OriginalComponents.label{channel})=obj.best_toolbox.inputs.results.OriginalComponents.freq(1,PeakPowerIndex);
obj.best_toolbox.app.pr.ax.(ax2).UserData.TextAnnotationPeakFrequency=text(obj.best_toolbox.app.pr.ax.(ax2),1,1,{['Peak Freq (Hz):' num2str(obj.best_toolbox.inputs.results.PeakFrequency.(obj.best_toolbox.inputs.results.OriginalComponents.label{channel}))]},'units','normalized','HorizontalAlignment','right','VerticalAlignment','cap','color',[0.45 0.45 0.45]);
xlabel('Frequency (Hz)'), ylabel('Power'); xticks(unique((sort([obj.best_toolbox.app.pr.ax.(ax2).XTick obj.best_toolbox.inputs.TargetFrequencyRange(1) obj.best_toolbox.inputs.TargetFrequencyRange(2)]))));
end
%% Showing Results on Table
end
function fft_old(obj,EEGData,InputDevice)
%% Stop Check Point
if obj.best_toolbox.inputs.stop_event==1, return, end
%% Just for testing purposes
load S5_raw_segmented.mat
EEGData=data;
cfg=[];
cfg.channel =data.label(1:64);
EEGData=ft_selectdata(cfg,EEGData);
EEGChannelsIndex=find(strcmp(obj.best_toolbox.app.par.hardware_settings.(InputDevice).NeurOneProtocolChannelSignalTypes,'EEG'));
EEGChanelsLabels=obj.best_toolbox.app.par.hardware_settings.(InputDevice).NeurOneProtocolChannelLabels(EEGChannelsIndex);
%% Stop Check Point
if obj.best_toolbox.inputs.stop_event==1, return, end
%% Creating RawEEGData
obj.best_toolbox.inputs.results.RawEEGData.label=EEGChanelsLabels';
obj.best_toolbox.inputs.results.RawEEGData.fsample=5000;
obj.best_toolbox.inputs.results.RawEEGData.trial={EEGData.Data};
obj.best_toolbox.inputs.results.RawEEGData.time={EEGData.Time};
%% Referencing RawEEGData
if ~isempty(obj.best_toolbox.inputs.TargetChannels)
if ~isempty(obj.best_toolbox.inputs.ReferenceChannels)
cfg=[];
cfg.reref = 'yes';
cfg.refchannel = obj.best_toolbox.inputs.ReferenceChannels;
if ~isempty(obj.best_toolbox.inputs.RecordingReference)
cfg.implicitref =obj.best_toolbox.inputs.RecordingReference;
end
obj.best_toolbox.inputs.results.ReReferencedData = ft_preprocessing(cfg, obj.best_toolbox.inputs.results.RawEEGData);
end
end
%% Montage Creation
if ~isempty(obj.best_toolbox.inputs.MontageChannels)
montage=[];
montage.tra = obj.best_toolbox.inputs.MontageWeights;
montage.labelold = obj.best_toolbox.inputs.MontageChannels;
montage.labelnew = {'Montage1'};
obj.best_toolbox.inputs.results.MontageData = ft_apply_montage(obj.best_toolbox.inputs.results.RawEEGData,montage);
end
%% Selected Data
if ~isempty(obj.best_toolbox.inputs.TargetChannels)
cfg=[];
cfg.channel = obj.best_toolbox.inputs.TargetChannels;
if ~isempty(obj.best_toolbox.inputs.ReferenceChannels)
SelectedEEGData=ft_selectdata(cfg,obj.best_toolbox.inputs.results.ReReferenced);
else
SelectedEEGData=ft_selectdata(cfg,obj.best_toolbox.inputs.results.RawEEGData) ;
end
end
if ~isempty(obj.best_toolbox.inputs.TargetChannels) && ~isempty(obj.best_toolbox.inputs.MontageChannels)
cfg=[];
cfg.keepsampleinfo ='no';
obj.best_toolbox.inputs.results.SelectedData=ft_appenddata(cfg , SelectedEEGData , obj.best_toolbox.inputs.results.MontageData); %% Check if this works properly
elseif ~isempty(obj.best_toolbox.inputs.TargetChannels) && isempty(obj.best_toolbox.inputs.MontageChannels)
obj.best_toolbox.inputs.results.SelectedChannels=SelectedEEGData;
elseif isempty(obj.best_toolbox.inputs.TargetChannels)>0 && ~isempty(obj.best_toolbox.inputs.MontageChannels)
obj.best_toolbox.inputs.results.SelectedData=obj.best_toolbox.inputs.results.MontageData;
end
%% Filtered Data
cfg=[];
if ~isempty(obj.best_toolbox.inputs.HighPassFrequency)
cfg.hpfilter = 'yes'; % high-pass in order to get rid of low-freq trends
cfg.hpfiltord = obj.best_toolbox.inputs.HighPassFilterOrder;
cfg.hpfreq = obj.best_toolbox.inputs.HighPassFrequency;
end
if ~isempty(obj.best_toolbox.inputs.BandStopFrequency)
cfg.bsfilter = 'yes'; % band-stop filter, to take out 50 Hz and its harmonics
cfg.bsfiltord = obj.best_toolbox.inputs.BandPassFilterOrder;
cfg.bsfreq = obj.best_toolbox.inputs.BandPassFrequency;
end
if ~isempty(cfg)
obj.best_toolbox.inputs.results.FilteredData = ft_preprocessing(cfg, obj.best_toolbox.inputs.results.SelectedData); %%
end
%% Segmented Data
cfg = [];
cfg.length = obj.best_toolbox.inputs.EEGEpochPeriod;
cfg.overlap = 0;
if isfield(obj.best_toolbox.inputs.results,'FilteredData')
obj.best_toolbox.inputs.results.SegmentedData = ft_redefinetrial(cfg, obj.best_toolbox.inputs.results.FilteredData);
else
obj.best_toolbox.inputs.results.SegmentedData = ft_redefinetrial(cfg, obj.best_toolbox.inputs.results.SelectedData);
end
%% Stop Check Point
if obj.best_toolbox.inputs.stop_event==1, return, end
%% Orignial, Fractal and Oscillation Components Spectral Analysis
cfg = [];
cfg.foilim = [1:1/obj.best_toolbox.inputs.EEGEpochPeriod:45];
cfg.taper = 'hanning';
cfg.pad = 'nextpow2';
cfg.method = 'mtmfft';
obj.best_toolbox.inputs.results.OriginalComponents = ft_freqanalysis(cfg, obj.best_toolbox.inputs.results.SegmentedData); %Raw
%% Stop Check Point
if obj.best_toolbox.inputs.stop_event==1, return, end
%% Plotting, Annotating and Saving Peak Frequency
TargetFrequencyRange=(find(obj.best_toolbox.inputs.results.OriginalComponents.freq == obj.best_toolbox.inputs.TargetFrequencyRange(1)):find(obj.best_toolbox.inputs.results.OriginalComponents.freq == obj.best_toolbox.inputs.TargetFrequencyRange(2)));
for channel=1:numel(obj.best_toolbox.inputs.results.OriginalComponents.label)
ax1=['ax' num2str(channel)];
axes(obj.best_toolbox.app.pr.ax.(ax1))
plot(obj.best_toolbox.inputs.results.OriginalComponents.freq, obj.best_toolbox.inputs.results.OriginalComponents.powspctrm(chanel,:),'linewidth', 3, 'color', [0 0 0]); hold on;
gridxy((obj.best_toolbox.inputs.TargetFrequencyRange(1)):0.25:(obj.best_toolbox.inputs.TargetFrequencyRange(2)),'Color',[219/255 246/255 255/255],'linewidth',4) ;
[~,PeakPowerIndex] = find(obj.best_toolbox.inputs.results.OriginalComponents.powspctrm(channel,:)==max(obj.best_toolbox.inputs.results.OriginalComponents.powspctrm(channel,TargetFrequencyRange)));%.*[8:0.5:14]);
obj.best_toolbox.inputs.results.PeakFrequency.(obj.best_toolbox.inputs.results.OriginalComponents.label(channel))=obj.best_toolbox.inputs.results.OriginalComponents.freq(1,PeakPowerIndex);
obj.best_toolbox.app.pr.ax.(ax1).UserData.TextAnnotationPeakFrequency=text(obj.best_toolbox.app.pr.ax.(ax1),1,1,{['Peak Freq (Hz):' num2str(obj.best_toolbox.inputs.results.PeakFrequency.(obj.best_toolbox.inputs.results.OriginalComponents.label(channel)))]},'units','normalized','HorizontalAlignment','right','VerticalAlignment','cap','color',[0.45 0.45 0.45]);
xlabel('Frequency (Hz)'), ylabel('Power'); xticks(unique((sort([obj.best_toolbox.app.pr.ax.(ax2).XTick obj.best_toolbox.inputs.TargetFrequencyRange(1) obj.best_toolbox.inputs.TargetFrequencyRange(2)]))));
end
end
function best2ftdata(obj,rawData,trial,InputDevice)
EEGChannelsIndex=find(strcmp(obj.best_toolbox.app.par.hardware_settings.(InputDevice).NeurOneProtocolChannelSignalTypes,'EEG'));
EEGChanelsLabels=obj.best_toolbox.app.par.hardware_settings.(InputDevice).NeurOneProtocolChannelLabels(EEGChannelsIndex);
obj.best_toolbox.inputs.rawData.ftdata.label=EEGChanelsLabels';
obj.best_toolbox.inputs.rawData.ftdata.fsample=5000;
obj.best_toolbox.inputs.rawData.ftdata.trial{trial}=rawData.data{trial};
obj.best_toolbox.inputs.rawData.ftdata.time{trial}=rawData.time{trial};
end
function preprocess(obj,Configuration,Data,Trial)
cfg=[];
cfg=Configuration;
cfg.trials=Trial;
obj.best_toolbox.inputs.PreProcessedData=ft_preprocessing(cfg,Data);
end
function [cfg] = databrowser(obj,cfg,data,Parent)
% FT_DATABROWSER can be used for visual inspection of data. Artifacts that were
% detected by artifact functions (see FT_ARTIFACT_xxx functions where xxx is the type
% of artifact) are marked. Additionally data pieces can be marked and unmarked as
% artifact by manual selection. The output cfg contains the updated specification of
% the artifacts.
%
% Use as
% cfg = ft_databrowser(cfg)
% cfg = ft_databrowser(cfg, data)
% If you only specify the configuration structure, it should contain the name of the
% dataset on your hard disk (see below). If you specify input data, it should be a
% data structure as obtained from FT_PREPROCESSING or from FT_COMPONENTANALYSIS.
%
% If you want to browse data that is on disk, you have to specify
% cfg.dataset = string with the filename
% Instead of specifying the dataset, you can also explicitely specify the name of the
% file containing the header information and the name of the file containing the
% data, using
% cfg.datafile = string with the filename
% cfg.headerfile = string with the filename
%
% The following configuration options are supported:
% cfg.ylim = vertical scaling, can be 'maxmin', 'maxabs' or [ymin ymax] (default = 'maxabs')
% cfg.zlim = color scaling to apply to component topographies, 'minmax', 'maxabs' (default = 'maxmin')
% cfg.blocksize = duration in seconds for cutting the data up
% cfg.trl = structure that defines the data segments of interest, only applicable for trial-based data
% cfg.continuous = 'yes' or 'no', whether the data should be interpreted as continuous or trial-based
% cfg.allowoverlap = 'yes' or 'no', whether data that is overlapping in multiple trials is allowed (default = 'no')
% cfg.channel = cell-array with channel labels, see FT_CHANNELSELECTION
% cfg.channelclamped = cell-array with channel labels, that (when using the 'vertical' viewmode) will always be shown at the bottom. This is useful for showing ECG/EOG channels along with the other channels
% cfg.compscale = string, 'global' or 'local', defines whether the colormap for the topographic scaling is applied per topography or on all visualized components (default 'global')
% cfg.plotlabels = 'yes', 'no' or 'some', whether to plot channel labels in vertical viewmode. The option 'some' plots one label for every ten channels, which is useful if there are many channels. (default = 'yes')
% cfg.ploteventlabels = 'type=value', 'colorvalue' (default = 'type=value');
% cfg.plotevents = 'no' or 'yes', whether to plot event markers. (default is 'yes')
% cfg.viewmode = string, 'butterfly', 'vertical', 'component' for visualizing ICA/PCA components (default is 'butterfly')
% cfg.artfctdef.xxx.artifact = Nx2 matrix with artifact segments see FT_ARTIFACT_xxx functions
% cfg.selectfeature = string, name of feature to be selected/added (default = 'visual')
% cfg.selectmode = 'markartifact', 'markpeakevent', 'marktroughevent' (default = 'markartifact')
% cfg.colorgroups = 'sequential', 'allblack', 'labelcharN' (N = Nth character in label), 'chantype' or a vector with the length of the number of channels defining the groups (default = 'sequential')
% cfg.linecolor = string with line colors or Nx3 color map (default = customized lines map with 15 colors)
% cfg.linewidth = linewidth in points (default = 0.5)
% cfg.linestyle = linestyle/marker type, see options of the PLOT function (default = '-')
% cfg.verticalpadding = number or 'auto', padding to be added to top and bottom of plot to avoid channels largely dissappearing when viewmode = 'vertical'/'component' (default = 'auto'). The padding is expressed as a proportion of the total height added to the top and bottom. The setting 'auto' determines the padding depending on the number of channels that are being plotted.
% cfg.selfun = string, name of function that is evaluated using the right-click context menu. The selected data and cfg.selcfg are passed on to this function.
% cfg.selcfg = configuration options for function in cfg.selfun
% cfg.seldat = 'selected' or 'all', specifies whether only the currently selected or all channels will be passed to the selfun (default = 'selected')
% cfg.renderer = string, 'opengl', 'zbuffer', 'painters', see MATLAB Figure Properties. If this function crashes, you should try 'painters'.
% cfg.position = location and size of the figure, specified as a vector of the form [left bottom width height].
%
% The following options for the scaling of the EEG, EOG, ECG, EMG, MEG and NIRS channels
% is optional and can be used to bring the absolute numbers of the different
% channel types in the same range (e.g. fT and uV). The channel types are determined
% from the input data using FT_CHANNELSELECTION.
% cfg.eegscale = number, scaling to apply to the EEG channels prior to display
% cfg.eogscale = number, scaling to apply to the EOG channels prior to display
% cfg.ecgscale = number, scaling to apply to the ECG channels prior to display
% cfg.emgscale = number, scaling to apply to the EMG channels prior to display
% cfg.megscale = number, scaling to apply to the MEG channels prior to display
% cfg.gradscale = number, scaling to apply to the MEG gradiometer channels prior to display (in addition to the cfg.megscale factor)
% cfg.magscale = number, scaling to apply to the MEG magnetometer channels prior to display (in addition to the cfg.megscale factor)
% cfg.nirsscale = number, scaling to apply to the NIRS channels prior to display
% cfg.mychanscale = number, scaling to apply to the channels specified in cfg.mychan
% cfg.mychan = Nx1 cell-array with selection of channels
% cfg.chanscale = Nx1 vector with scaling factors, one per channel specified in cfg.channel
%
% You can specify preprocessing options that are to be applied to the data prior to
% display. Most options from FT_PREPROCESSING are supported. They should be specified
% in the sub-structure cfg.preproc like these examples
% cfg.preproc.lpfilter = 'no' or 'yes' lowpass filter (default = 'no')
% cfg.preproc.lpfreq = lowpass frequency in Hz
% cfg.preproc.demean = 'no' or 'yes', whether to apply baseline correction (default = 'no')
% cfg.preproc.detrend = 'no' or 'yes', remove linear trend from the data (done per trial) (default = 'no')
% cfg.preproc.baselinewindow = [begin end] in seconds, the default is the complete trial (default = 'all')
%
% In case of component viewmode, a layout is required. If no layout is specified, an
% attempt is made to construct one from the sensor definition that is present in the
% data or specified in the configuration.
% cfg.layout = filename of the layout, see FT_PREPARE_LAYOUT
% cfg.elec = structure with electrode positions or filename, see FT_READ_SENS
% cfg.grad = structure with gradiometer definition or filename, see FT_READ_SENS
%
% The default font size might be too small or too large, depending on the number of
% channels. You can use the following options to change the size of text inside the
% figure and along the axes.
% cfg.fontsize = number, fontsize inside the figure (default = 0.03)
% cfg.fontunits = string, can be 'normalized', 'points', 'pixels', 'inches' or 'centimeters' (default = 'normalized')
% cfg.axisfontsize = number, fontsize along the axes (default = 10)
% cfg.axisfontunits = string, can be 'normalized', 'points', 'pixels', 'inches' or 'centimeters' (default = 'points')
%
% When visually selection data, a right-click will bring up a context-menu containing
% functions to be executed on the selected data. You can use your own function using
% cfg.selfun and cfg.selcfg. You can use multiple functions by giving the names/cfgs
% as a cell-array.
%
% In butterfly and vertical mode, you can use the "identify" button to reveal the name of a
% channel. Please be aware that it searches only vertically. This means that it will
% return the channel with the amplitude closest to the point you have clicked at the
% specific time point. This might be counterintuitive at first.
%
% The "cfg.artfctdef" structure in the output cfg is comparable to the configuration
% used by the artifact detection functions like FT_ARTIFACT_ZVALUE and in
% FT_REJECTARTIFACT. It contains for each artifact type an Nx2 matrix in which the
% first column corresponds to the begin samples of an artifact period, the second
% column contains the end samples of the artifact periods.
%
% Note for debugging: in case the databrowser crashes, use delete(gcf) to kill the
% figure.
%
% See also FT_PREPROCESSING, FT_REJECTARTIFACT, FT_ARTIFACT_EOG, FT_ARTIFACT_MUSCLE,
% FT_ARTIFACT_JUMP, FT_ARTIFACT_MANUAL, FT_ARTIFACT_THRESHOLD, FT_ARTIFACT_CLIP,
% FT_ARTIFACT_ECG, FT_COMPONENTANALYSIS
% Copyright (C) 2009-2015, Robert Oostenveld, Ingrid Nieuwenhuis
%
% 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/>.
%
% $Id$
% FIXME these should be removed or documented
% cfg.preproc
% these are used by the ft_preamble/ft_postamble function and scripts
ft_revision = '$Id$';
ft_nargin = nargin;
ft_nargout = nargout;
% do the general setup of the function
ft_defaults
ft_preamble init
ft_preamble debug
ft_preamble loadvar data
ft_preamble provenance data
ft_preamble trackconfig
% the ft_abort variable is set to true or false in ft_preamble_init
if ft_abort
return
end
% the data can be passed as input arguments or can be read from disk
hasdata = exist('data', 'var');
hascomp = hasdata && ft_datatype(data, 'comp'); % can be 'raw+comp' or 'timelock+comp'
% check if the input cfg is valid for this function
cfg = ft_checkconfig(cfg, 'unused', {'comps', 'inputfile', 'outputfile'});
cfg = ft_checkconfig(cfg, 'renamed', {'zscale', 'ylim'});
cfg = ft_checkconfig(cfg, 'renamedval', {'ylim', 'auto', 'maxabs'});
cfg = ft_checkconfig(cfg, 'renamedval', {'selectmode', 'mark', 'markartifact'});
cfg = ft_checkconfig(cfg, 'renamed', {'elecfile', 'elec'});
cfg = ft_checkconfig(cfg, 'renamed', {'gradfile', 'grad'});
cfg = ft_checkconfig(cfg, 'renamed', {'optofile', 'opto'});
cfg = ft_checkconfig(cfg, 'renamed', {'channelcolormap', 'linecolor'});
% ensure that the preproc specific options are located in the cfg.preproc substructure
cfg = ft_checkconfig(cfg, 'createsubcfg', {'preproc'});
% set the defaults
cfg.ylim = ft_getopt(cfg, 'ylim', 'maxabs');
cfg.artfctdef = ft_getopt(cfg, 'artfctdef', struct);
cfg.selectfeature = ft_getopt(cfg, 'selectfeature', 'visual'); % string or cell-array
cfg.selectmode = ft_getopt(cfg, 'selectmode', 'markartifact');
cfg.blocksize = ft_getopt(cfg, 'blocksize'); % now used for both continuous and non-continuous data, defaulting done below
cfg.preproc = ft_getopt(cfg, 'preproc'); % see preproc for options
cfg.selfun = ft_getopt(cfg, 'selfun'); % default functions: 'simpleFFT', 'multiplotER', 'topoplotER', 'topoplotVAR', 'movieplotER'
cfg.selcfg = ft_getopt(cfg, 'selcfg'); % defaulting done below, requires layouts/etc to be processed
cfg.seldat = ft_getopt(cfg, 'seldat', 'current');
cfg.colorgroups = ft_getopt(cfg, 'colorgroups', 'sequential');
cfg.linecolor = ft_getopt(cfg, 'linecolor', [0.75 0 0; 0 0 1; 0 1 0; 0.44 0.19 0.63; 0 0.13 0.38;0.5 0.5 0.5;1 0.75 0; 1 0 0; 0.89 0.42 0.04; 0.85 0.59 0.58; 0.57 0.82 0.31; 0 0.69 0.94; 1 0 0.4; 0 0.69 0.31; 0 0.44 0.75]);
cfg.linestyle = ft_getopt(cfg, 'linestyle', '-');
cfg.linewidth = ft_getopt(cfg, 'linewidth', 0.5);
cfg.eegscale = ft_getopt(cfg, 'eegscale');
cfg.eogscale = ft_getopt(cfg, 'eogscale');
cfg.ecgscale = ft_getopt(cfg, 'ecgscale');
cfg.emgscale = ft_getopt(cfg, 'emgscale');
cfg.megscale = ft_getopt(cfg, 'megscale');
cfg.magscale = ft_getopt(cfg, 'magscale');
cfg.gradscale = ft_getopt(cfg, 'gradscale');
cfg.chanscale = ft_getopt(cfg, 'chanscale');
cfg.mychanscale = ft_getopt(cfg, 'mychanscale');
cfg.mychan = ft_getopt(cfg, 'mychan');
cfg.layout = ft_getopt(cfg, 'layout');
cfg.plotlabels = ft_getopt(cfg, 'plotlabels', 'some');
cfg.event = ft_getopt(cfg, 'event'); % this only exists for backward compatibility and should not be documented
cfg.continuous = ft_getopt(cfg, 'continuous'); % the default is set further down in the code, conditional on the input data
cfg.ploteventlabels = ft_getopt(cfg, 'ploteventlabels', 'type=value');
cfg.plotevents = ft_getopt(cfg, 'plotevents', 'yes');
cfg.precision = ft_getopt(cfg, 'precision', 'double');
cfg.zlim = ft_getopt(cfg, 'zlim', 'maxmin');
cfg.compscale = ft_getopt(cfg, 'compscale', 'global');
cfg.renderer = ft_getopt(cfg, 'renderer');
cfg.fontsize = ft_getopt(cfg, 'fontsize', 12);
cfg.fontunits = ft_getopt(cfg, 'fontunits', 'points'); % inches, centimeters, normalized, points, pixels
cfg.editfontsize = ft_getopt(cfg, 'editfontsize', 12);
cfg.editfontunits = ft_getopt(cfg, 'editfontunits', 'points'); % inches, centimeters, normalized, points, pixels
cfg.axisfontsize = ft_getopt(cfg, 'axisfontsize', 10);
cfg.axisfontunits = ft_getopt(cfg, 'axisfontunits', 'points'); % inches, centimeters, normalized, points, pixels
cfg.verticalpadding = ft_getopt(cfg, 'verticalpadding', 'auto');
cfg.artifactalpha = ft_getopt(cfg, 'artifactalpha', 0.2); % for the opacity of marked artifacts
cfg.allowoverlap = ft_getopt(cfg, 'allowoverlap', 'no'); % for ft_fetch_data
cfg.contournum = ft_getopt(cfg, 'contournum', 0); % topoplot contour lines
cfg.trl = ft_getopt(cfg, 'trl');
if ~isfield(cfg, 'viewmode')
% butterfly, vertical, component
if hascomp
cfg.viewmode = 'component';
else
cfg.viewmode = 'butterfly';
end
end
if ~isfield(cfg, 'colorgroups')
% 'sequential', 'allblack', 'labelcharx' (x = xth character in label), 'chantype' or a vector with the length of the number of channels defining the groups (default = 'sequential')
if hascomp
cfg.viewmode = 'allblack';
else
cfg.viewmode = 'sequential';
end
end
if ~isempty(cfg.chanscale)
if ~isfield(cfg, 'channel')
ft_warning('ignoring cfg.chanscale; this should only be used when an explicit channel selection is being made');
cfg.chanscale = [];
elseif numel(cfg.channel) ~= numel(cfg.chanscale)
ft_error('cfg.chanscale should have the same number of elements as cfg.channel');
end
% make sure chanscale is a column vector, not a row vector
cfg.chanscale = cfg.chanscale(:);
end
if ~isempty(cfg.mychanscale) && ~isfield(cfg, 'mychan')
ft_warning('ignoring cfg.mychanscale; no channels specified in cfg.mychan');
cfg.mychanscale = [];
end
if ~isfield(cfg, 'channel')
if hascomp
if size(data.topo,2)>9
cfg.channel = 1:10;
else
cfg.channel = 1:size(data.topo,2);
end
else
cfg.channel = 'all';
end
end
if strcmp(cfg.viewmode, 'component')
% read or create the layout that will be used for the topoplots
tmpcfg = keepfields(cfg, {'layout', 'rows', 'columns', 'commentpos', 'scalepos', 'projection', 'viewpoint', 'rotate', 'width', 'height', 'elec', 'grad', 'opto', 'showcallinfo'});
if hasdata
cfg.layout = ft_prepare_layout(tmpcfg, data);
else
cfg.layout = ft_prepare_layout(tmpcfg);
end
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% set the defaults and do some preparation
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
if hasdata
% save whether data came from a timelock structure
istimelock = strcmp(ft_datatype(data), 'timelock');
if ~isempty(cfg.trl)
% reduce the input data to the requested trial segments
tmpcfg = keepfields(cfg, {'trl'});
data = ft_redefinetrial(tmpcfg, data);
[cfg, data] = rollback_provenance(cfg, data);
end
% check if the input data is valid for this function
data = ft_checkdata(data, 'datatype', {'raw+comp', 'raw'}, 'feedback', 'yes', 'hassampleinfo', 'yes');
% fetch the header from the data structure in memory
hdr = ft_fetch_header(data);
if isfield(data, 'cfg') && ~isempty(ft_findcfg(data.cfg, 'origfs'))
% don't use the events in case the data has been resampled
ft_warning('the data has been resampled, not showing the events');
event = [];
elseif isfield(data, 'cfg') && isfield(data.cfg, 'event')
% use the event structure from the data as per bug #2501
event = data.cfg.event;
elseif ~isempty(cfg.event)
% use the events that the user passed in the configuration
event = cfg.event;
else
% fetch the events from the data structure in memory
%event = ft_fetch_event(data);
event = [];
end
cfg.channel = ft_channelselection(cfg.channel, hdr.label);
chansel = match_str(data.label, cfg.channel);
Nchans = length(chansel);
if isempty(cfg.continuous)
if numel(data.trial) == 1 && ~istimelock
cfg.continuous = 'yes';
else
cfg.continuous = 'no';
end
else
if strcmp(cfg.continuous, 'yes') && (numel(data.trial) > 1)
ft_warning('interpreting trial-based data as continuous, time-axis is no longer appropriate. t(0) now corresponds to the first sample of the first trial, and t(end) to the last sample of the last trial')
end
end
% this is how the input data is segmented
trlorg = sampleinfo2trl(data);
else
% check if the input cfg is valid for this function
cfg = ft_checkconfig(cfg, 'dataset2files', 'yes');
cfg = ft_checkconfig(cfg, 'required', {'headerfile', 'datafile'});
cfg = ft_checkconfig(cfg, 'renamed', {'datatype', 'continuous'});
cfg = ft_checkconfig(cfg, 'renamedval', {'continuous', 'continuous', 'yes'});
% read the header from file
hdr = ft_read_header(cfg.headerfile, 'headerformat', cfg.headerformat);
if isempty(cfg.continuous)
if hdr.nTrials==1
cfg.continuous = 'yes';
else
cfg.continuous = 'no';
end
end
if ~isempty(cfg.event)
% use the events that the user passed in the configuration
event = cfg.event;
else
% read the events from file
event = ft_read_event(cfg.dataset);
end
cfg.channel = ft_channelselection(cfg.channel, hdr.label);
chansel = match_str(hdr.label, cfg.channel);
Nchans = length(chansel);
if ~isfield(cfg, 'trl') || isempty(cfg.trl)
% treat the data as continuous if possible, otherwise define all trials as indicated in the header
if strcmp(cfg.continuous, 'yes')
trlorg = zeros(1, 3);
trlorg(1,1) = 1;
trlorg(1,2) = hdr.nSamples*hdr.nTrials;
trlorg(1,3) = -hdr.nSamplesPre;
else
trlorg = zeros(hdr.nTrials, 3);
for i=1:hdr.nTrials
trlorg(i,1) = (i-1)*hdr.nSamples + 1;
trlorg(i,2) = (i )*hdr.nSamples ;
trlorg(i,3) = -hdr.nSamplesPre;
end
end
elseif ischar(cfg.trl)
% load the trial information from file
trlorg = loadvar(cfg.trl, 'trl');
else
trlorg = cfg.trl;
end
end % if hasdata
Ntrials = size(trlorg, 1);
if strcmp(cfg.continuous, 'no') && isempty(cfg.blocksize)
cfg.blocksize = (trlorg(1,2) - trlorg(1,1)+1) ./ hdr.Fs;
elseif strcmp(cfg.continuous, 'yes') && isempty(cfg.blocksize)
cfg.blocksize = 1;
end
if cfg.blocksize<round(10*1/hdr.Fs)
ft_warning('the blocksize is very small given the samping rate, increasing blocksize to 10 samples');
cfg.blocksize = round(10*1/hdr.Fs);
end
% FIXME make a check for the consistency of cfg.continuous, cfg.blocksize, cfg.trl and the data header
if Nchans == 0
ft_error('no channels to display');
end
if Ntrials == 0
ft_error('no trials to display');
end
if ischar(cfg.selectfeature)
% ensure that it is a cell-array
cfg.selectfeature = {cfg.selectfeature};
end
if ~isempty(cfg.selectfeature)
for i=1:length(cfg.selectfeature)
if ~isfield(cfg.artfctdef, cfg.selectfeature{i})
cfg.artfctdef.(cfg.selectfeature{i}) = [];
cfg.artfctdef.(cfg.selectfeature{i}).artifact = zeros(0,2);
end
end
end
% determine the vertical scaling
if ischar(cfg.ylim)
if hasdata
sel = 1;
while all(isnan(reshape(data.trial{sel}(chansel,:),[],1)))
sel = sel+1;
end
% the first trial is used to determine the vertical scaling
dat = data.trial{sel}(chansel,:);
else
% one second of data is read from file to determine the vertical scaling
dat = ft_read_data(cfg.datafile, 'header', hdr, 'begsample', 1, 'endsample', round(hdr.Fs), 'chanindx', chansel, 'checkboundary', strcmp(cfg.continuous, 'no'), 'dataformat', cfg.dataformat, 'headerformat', cfg.headerformat);
end % if hasdata
% convert the data to another numeric precision, i.e. double, single or int32
if ~isempty(cfg.precision)
dat = cast(dat, cfg.precision);
end
minval = min(dat(:));
maxval = max(dat(:));
switch cfg.ylim
case 'maxabs'
maxabs = max(abs([minval maxval]));
scalefac = 10^(fix(log10(maxabs)));
if scalefac==0
% this happens if the data is all zeros
scalefac=1;
end
maxabs = (round(maxabs / scalefac * 100) / 100) * scalefac;
cfg.ylim = [-maxabs maxabs];
case 'maxmin'
if minval==maxval
% this happens if the data is constant, e.g. all zero or clipping
minval = minval - eps;
maxval = maxval + eps;
end
cfg.ylim = [minval maxval];
otherwise
ft_error('unsupported value for cfg.ylim');
end % switch ylim
% zoom in a bit when viemode is vertical
if strcmp(cfg.viewmode, 'vertical')
cfg.ylim = cfg.ylim/10;
end
else
if (numel(cfg.ylim) ~= 2) || ~isnumeric(cfg.ylim)
ft_error('cfg.ylim needs to be a 1x2 vector [ymin ymax], describing the upper and lower limits')
end
end
% determine the coloring of channels
if hasdata
linecolor = linecolor_common(cfg, data);
else
linecolor = linecolor_common(cfg, hdr);
end
% collect the artifacts that have been detected from cfg.artfctdef.xxx.artifact
artlabel = fieldnames(cfg.artfctdef);
sel = zeros(size(artlabel));
artifact = cell(size(artlabel));
for i=1:length(artlabel)
sel(i) = isfield(cfg.artfctdef.(artlabel{i}), 'artifact');
if sel(i)
artifact{i} = cfg.artfctdef.(artlabel{i}).artifact;
ft_info('detected %3d %s artifacts\n', size(artifact{i}, 1), artlabel{i});
end
end
% get the subset of the artfctdef fields that seem to contain artifacts
artifact = artifact(sel==1);
artlabel = artlabel(sel==1);
if length(artlabel) > 9
ft_error('only up to 9 artifacts groups supported')
end
% make artdata representing all artifacts in a "raw data" format
datendsample = max(trlorg(:,2));
artdata = [];
artdata.trial{1} = convert_event(artifact, 'boolvec', 'endsample', datendsample); % every artifact is a "channel"
artdata.time{1} = offset2time(0, hdr.Fs, datendsample);
artdata.label = artlabel;
artdata.fsample = hdr.Fs;
artdata.cfg.trl = [1 datendsample 0];
% determine amount of unique event types (for cfg.ploteventlabels)
if ~isempty(event) && isstruct(event)
eventtypes = unique({event.type});
else
eventtypes = [];
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% set up default functions to be available in the right-click menu
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% cfg.selfun - labels that are presented in rightclick menu, and is appended using ft_getuserfun(..., 'browse') later on to create a function handle
% cfg.selcfg - cfgs for functions to be executed
if ~isempty(cfg.selfun) || ~isempty(cfg.selcfg)
if ischar(cfg.selfun)
cfg.selfun = {cfg.selfun};
end
if isstruct(cfg.selcfg)
cfg.selcfg = {cfg.selcfg};
end
elseif isempty(cfg.selfun) && isempty(cfg.selcfg)
% simplefft
cfg.selcfg{1} = [];
cfg.selcfg{1}.linecolor = linecolor;
cfg.selfun{1} = 'simpleFFT';
% multiplotER
cfg.selcfg{2} = [];
cfg.selcfg{2}.linecolor = linecolor;
cfg.selcfg{2}.layout = cfg.layout;
cfg.selfun{2} = 'multiplotER';
% topoplotER
cfg.selcfg{3} = [];
cfg.selcfg{3}.linecolor = linecolor;
cfg.selcfg{3}.layout = cfg.layout;
cfg.selfun{3} = 'topoplotER';
% topoplotVAR
cfg.selcfg{4} = [];
cfg.selcfg{4}.layout = cfg.layout;
cfg.selfun{4} = 'topoplotVAR';
% movieplotER
cfg.selcfg{5} = [];
cfg.selcfg{5}.layout = cfg.layout;
cfg.selcfg{5}.interactive = 'yes';
cfg.selfun{5} = 'movieplotER';
% audiovideo
cfg.selcfg{6} = [];
cfg.selcfg{6}.audiofile = ft_getopt(cfg, 'audiofile');
cfg.selcfg{6}.videofile = ft_getopt(cfg, 'videofile');
cfg.selcfg{6}.anonimize = ft_getopt(cfg, 'anonimize');
cfg.selfun{6} = 'audiovideo';
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% set up the data structures used in the GUI
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% opt represents the global data/settings, it should contain
% - the original data, epoched or continuous
% - the artifacts represented as continuous data
% - the redraw_cb settings
% - the preproc settings
% - the select_range_cb settings (also used in keyboard_cb)
% these elements are stored inside the figure, so that the callback routines can modify them
opt = [];
if hasdata
opt.orgdata = data;
else
opt.orgdata = []; % this means that it will read from cfg.dataset
end
if strcmp(cfg.continuous, 'yes')
opt.trialviewtype = 'segment';
else
opt.trialviewtype = 'trial';
end
opt.artdata = artdata;
opt.hdr = hdr;
opt.event = event;
opt.trlop = 1; % the active trial being displayed
opt.ftsel = find(strcmp(artlabel, cfg.selectfeature)); % current artifact/feature being selected
opt.trlorg = trlorg;
opt.fsample = hdr.Fs;
opt.artifactcolors = [0.9686 0.7608 0.7686; 0.7529 0.7098 0.9647; 0.7373 0.9725 0.6824; 0.8118 0.8118 0.8118; 0.9725 0.6745 0.4784; 0.9765 0.9176 0.5686; 0.6863 1 1; 1 0.6863 1; 0 1 0.6000];
opt.linecolor = linecolor;