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perceive.m
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function perceive(files,subjectIDs,datafields)
% https://github.com/neuromodulation/perceive
% v0.1 Contributors Wolf-Julian Neumann, Tomas Sieger, Gerd Tinkhauser
% This is an open research tool that is not intended for clinical purposes.
%% INPUT
% files:
% All input is optional, you can specify files as cell or character array
% (e.g. files = 'Report_Json_Session_Report_20200115T123657.json')
% if files isn't specified or remains empty, it will automatically include
% all files in the current working directory
% if no files in the current working directory are found, a you can choose
% files via the MATLAB uigetdir window.
%
% subjectIDs:
% you can specify a subject ID for each file in case you want to follow an
% IRB approved naming scheme for file export
% (e.g. run
% perceive('Report_Json_Session_Report_20200115T123657.json','Charite_sub-001')
% if unspecified or left empy, the subjectID will be created from
% ImplantDate, first letter of disease type and target (e.g. sub-2020110DGpi)
%% OUTPUT
% The script generates BIDS inspired subject and session folders with the
% ieeg format specifier. All time series data are being exported as
% FieldTrip .mat files, as these require no additional dependencies for creation.
% You can reformat with FieldTrip and SPM to MNE
% python and other formats (e.g. using fieldtrip2fiff([fullname '.fif'],data))
%% Recording type output naming
% Each of the FieldTrip data files correspond to a specific aspect of the
% Recording session:
% LMTD = LFP Montage Time Domain - BrainSenseSurvey
% IS = Indefinite Streaming - BrainSenseStreaming
% CT = Calibration Testing - Calibration Tests
% BSL = BrainSense LFP (2 Hz power average + stimulation settings)
% BSTD = BrainSense Time Domain (250 Hz raw data corresponding to the BSL
% file)
%% TODO:
% ADD DEIDENTIFICATION OF COPIED JSON
% BUG FIX UTC?
% ADD BATTERY DRAIN
% ADD BSL data to BSTD ephys file
% ADD PATIENT SNAPSHOT EVENT READINGS
% IMPROVE CHRONIC DIAGNOSTIC READINGS
% ADD Lead DBS Integration for electrode location
if exist('datafields') && ischar(datafields)
datafields = {datafields};
end
if ~exist('files','var') || isempty(files)
try
files=perceive_ffind('*.json');
catch
files = [];
end
if isempty(files)
[files,path] = uigetfile('*.json','Select .json file','MultiSelect','on');
if isempty(files)
return
end
files = strcat(path,files);
end
end
if ischar(files)
files = {files};
end
if exist('subjectIDs','var') && ischar(subjectIDs)
subjectIDs={subjectIDs};
end
for a = 1:length(files)
filename = files{a};
disp(['RUNNING ' filename])
js = jsondecode(fileread(filename));
try
js.PatientInformation.Initial.PatientFirstName ='';
js.PatientInformation.Initial.PatientLastName ='';
js.PatientInformation.Initial.PatientDateOfBirth ='';
js.PatientInformation.Final.PatientFirstName ='';
js.PatientInformation.Final.PatientLastName ='';
js.PatientInformation.Final.PatientDateOfBirth ='';
catch
js = rmfield(js,'PatientInformation');
js.PatientInformation.Initial.PatientFirstName ='';
js.PatientInformation.Initial.PatientLastName ='';
js.PatientInformation.Initial.PatientDateOfBirth ='';
js.PatientInformation.Initial.Diagnosis ='';
js.PatientInformation.Final.PatientFirstName ='';
js.PatientInformation.Final.PatientLastName ='';
js.PatientInformation.Final.PatientDateOfBirth ='';
js.PatientInformation.Final.Diagnosis = '';
end
infofields = {'SessionDate','SessionEndDate','PatientInformation','DeviceInformation','BatteryInformation','LeadConfiguration','Stimulation','Groups','Stimulation','Impedance','PatientEvents','EventSummary','DiagnosticData'};
for b = 1:length(infofields)
if isfield(js,infofields{b})
hdr.(infofields{b})=js.(infofields{b});
end
end
disp(js.DeviceInformation.Final.NeurostimulatorLocation)
hdr.SessionEndDate = datetime(strrep(js.SessionEndDate(1:end-1),'T',' '));
hdr.SessionDate = datetime(strrep(js.SessionDate(1:end-1),'T',' '));
if ~isempty(js.PatientInformation.Final.Diagnosis)
hdr.Diagnosis = strsplit(js.PatientInformation.Final.Diagnosis,'.');hdr.Diagnosis=hdr.Diagnosis{2};
else
hdr.Diagnosis = '';
end
hdr.OriginalFile = filename;
hdr.ImplantDate = strrep(strrep(js.DeviceInformation.Final.ImplantDate(1:end-1),'T','_'),':','-');
hdr.BatteryPercentage = js.BatteryInformation.BatteryPercentage;
hdr.LeadLocation = strsplit(hdr.LeadConfiguration.Final(1).LeadLocation,'.');hdr.LeadLocation=hdr.LeadLocation{2};
if ~exist('subjectIDs','var')
if ~isempty(hdr.ImplantDate) && ~isnan(str2double(hdr.ImplantDate(1)))
hdr.subject = ['sub-' strrep(strtok(hdr.ImplantDate,'_'),'-','') hdr.Diagnosis(1) hdr.LeadLocation];
else
hdr.subject = ['sub-000' hdr.Diagnosis(1) hdr.LeadLocation];
end
elseif iscell(subjectIDs) && length(subjectIDs) == length(files)
hdr.subject = subjectIDs{a};
elseif length(subjectIDs) == 1
hdr.subject = subjectIDs{1};
end
hdr.session = ['ses-' char(datetime(hdr.SessionDate,'format','yyyyMMddhhmmss')) num2str(hdr.BatteryPercentage)];
if ~exist(fullfile(hdr.subject,hdr.session,'ieeg'),'dir')
mkdir(fullfile(hdr.subject,hdr.session,'ieeg'));
end
hdr.fpath = fullfile(hdr.subject,hdr.session,'ieeg');
hdr.fname = [hdr.subject '_' hdr.session];
hdr.chan = ['LFP_' hdr.LeadLocation];
hdr.d0 = datetime(js.SessionDate(1:10));
hdr.js = js;
if ~exist('datafields','var')
datafields = sort({'EventSummary','Impedance','MostRecentInSessionSignalCheck','BrainSenseLfp','BrainSenseTimeDomain','LfpMontageTimeDomain','IndefiniteStreaming','BrainSenseSurvey','CalibrationTests','PatientEvents','DiagnosticData'});
end
alldata = {};
disp(['SUBJECT ' hdr.subject])
for b = 1:length(datafields)
if isfield(js,datafields{b})
data = js.(datafields{b});
if isempty(data)
continue
end
switch datafields{b}
case 'Impedance'
T=table;
save_impedance=1;
for c = 1:length(data.Hemisphere)
tmp=strsplit(data.Hemisphere(c).Hemisphere,'.');
side = tmp{2}(1);
electrodes = unique([{data.Hemisphere(c).SessionImpedance.Monopolar.Electrode2} {data.Hemisphere(c).SessionImpedance.Monopolar.Electrode1}]);
e1 = strrep([{data.Hemisphere(c).SessionImpedance.Monopolar.Electrode1} {data.Hemisphere(c).SessionImpedance.Bipolar.Electrode1}],'ElectrodeDef.','') ;
e2 = [{data.Hemisphere(c).SessionImpedance.Monopolar.Electrode2} {data.Hemisphere(c).SessionImpedance.Bipolar.Electrode2}];
if ~ischar([data.Hemisphere(c).SessionImpedance.Monopolar.ResultValue]) && ~ischar([data.Hemisphere(c).SessionImpedance.Bipolar.ResultValue])
imp = [[data.Hemisphere(c).SessionImpedance.Monopolar.ResultValue] [data.Hemisphere(c).SessionImpedance.Bipolar.ResultValue]];
for e = 1:length(imp)
if strcmp(e1{e},'Case')
T.([hdr.chan '_' side e2{e}(end)]) = imp(e);
else
T.([hdr.chan '_' side e2{e}(end) e1{e}(end)]) = imp(e);
end
end
else
warning('impedance values too high, not being saved...')
save_impedance=0;
end
end
if save_impedance
figure
barh(table2array(T(1,:))')
set(gca,'YTick',1:length(T.Properties.VariableNames),'YTickLabel',strrep(T.Properties.VariableNames,'_',' '))
xlabel('Impedance')
title(strrep({hdr.subject, hdr.session,'Impedances'},'_',' '))
perceive_print(fullfile(hdr.fpath,[hdr.fname '_run-Impedance']))
writetable(T,fullfile(hdr.fpath,[hdr.fname '_run-Impedance.csv']));
end
case 'PatientEvents'
disp(fieldnames(data));
case 'MostRecentInSessionSignalCheck'
if ~isempty(data)
channels={};
pow=[];rpow=[];lfit=[];bad=[];peaks=[];
for c = 1:length(data)
cdata = data(c);
if iscell(cdata)
cdata=cdata{1};
end
tmp=strsplit(cdata.Channel,'_');
side=tmp{3}(1);
tmp=strsplit(cdata.Channel,'.');tmp=strrep(tmp{2},'_AND_','');tmp=strsplit(tmp,'_');
ch = strrep(strrep(strrep(strrep(strcat(tmp{1},tmp{2}),'ZERO','0'),'ONE','1'),'TWO','2'),'THREE','3');
channels{c} = [hdr.chan '_' side '_' ch];
freq = cdata.SignalFrequencies;
pow(c,:) = cdata.SignalPsdValues;
rpow(c,:) = perceive_power_normalization(pow(c,:),freq);
lfit(c,:) = perceive_fftlogfitter(freq,pow(c,:));
bad(c,1) = strcmp('IFACT_PRESENT',cdata.ArtifactStatus(end-12:end));
try
peaks(c,1) = cdata.PeakFrequencyInHertz;
peaks(c,2) = cdata.PeakMagnitudeInMicroVolt;
catch
peaks(c,:)=zeros(1,2);
end
end
T=array2table([freq';pow;rpow;lfit]','VariableNames',[{'Frequency'};strcat({'POW'},channels');strcat({'RPOW'},channels');strcat({'LFIT'},channels')]);
writetable(T,fullfile(hdr.fpath,[hdr.fname '_run-MostRecentSignalCheckPowerSpectra.csv']));
T=array2table(peaks','VariableNames',channels,'RowNames',{'PeakFrequency','PeakPower'});
writetable(T,fullfile(hdr.fpath,[hdr.fname '_run-MostRecentSignalCheck_Peaks.csv']));
figure('Units','centimeters','PaperUnits','centimeters','Position',[1 1 40 20])
ir = perceive_ci([hdr.chan '_R'],channels);
subplot(1,2,2)
p=plot(freq,pow(ir,:));
set(p(find(bad(ir))),'linestyle','--')
hold on
plot(freq,nanmean(pow(ir,:)),'color','k','linewidth',2)
xlim([1 35])
plot(peaks(ir,1),peaks(ir,2),'LineStyle','none','Marker','.','MarkerSize',12)
for c = 1:length(ir)
if peaks(ir(c),1)>0
text(peaks(ir(c),1),peaks(ir(c),2),[' ' num2str(peaks(ir(c),1),3) ' Hz'])
end
end
xlabel('Frequency [Hz]')
ylabel('Power spectral density [uV�/Hz]')
title(strrep({hdr.subject,char(hdr.SessionDate),'RIGHT'},'_',' '))
legend(strrep(channels(ir),'_',' '))
il = perceive_ci([hdr.chan '_L'],channels);
subplot(1,2,1)
p=plot(freq,pow(il,:));
set(p(find(bad(il))),'linestyle','--')
hold on
plot(freq,nanmean(pow(il,:)),'color','k','linewidth',2)
xlim([1 35])
title(strrep({'MostRecentSignalCheck',hdr.subject,char(hdr.SessionDate),'LEFT'},'_',' '))
plot(peaks(il,1),peaks(il,2),'LineStyle','none','Marker','.','MarkerSize',12)
xlabel('Frequency [Hz]')
ylabel('Power spectral density [uV�/Hz]')
for c = 1:length(il)
if peaks(il(c),1)>0
text(peaks(il(c),1),peaks(il(c),2),[' ' num2str(peaks(il(c),1),3) ' Hz'])
end
end
legend(strrep(channels(il),'_',' '))
savefig(fullfile(hdr.fpath,[hdr.fname '_run-MostRecentSignalCheck.fig']))
perceive_print(fullfile(hdr.fpath,[hdr.fname '_run-MostRecentSignalCheck']))
end
case 'DiagnosticData'
if isfield(data,'LFPTrendLogs')
LFPL=[];STIML=[];DTL=datetime([],[],[]);
LFPR=[];STIMR=[];DTR=datetime([],[],[]);
if isfield(data.LFPTrendLogs,'HemisphereLocationDef_Left')
data.left=data.LFPTrendLogs.HemisphereLocationDef_Left;
runs = fieldnames(data.left);
for c=1:length(runs)
clfp = [data.left.(runs{c}).LFP];
cstim = [data.left.(runs{c}).AmplitudeInMilliAmps];
cdt = datetime({data.left.(runs{c}).DateTime},'InputFormat','yyyy-MM-dd''T''HH:mm:ss''Z''');
[cdt,i] = sort(cdt);
LFPL=[LFPL,clfp(i)];
STIML=[STIML,cstim(i)];
DTL=[DTL,cdt];
d=[];
d.hdr = hdr;d.datatype = 'DiagnosticData.LFPTrends';
d.fsample = 0.00166666666;
d.trial{1} = [clfp(i);cstim(i)];
d.label = {'LFP_LEFT','STIM_LEFT'};
d.time{1} = linspace(seconds(cdt(1)-hdr.d0),seconds(cdt(end)-hdr.d0),size(d.trial{1},2));
d.realtime{1} = cdt;
if length(d.time{1})>1
d.fsample = abs(1/diff(d.time{1}(1:2)));
else
warning('Only one data point recorded, assuming a sampling frequency of 1 / 10 minutes ~ 0.0017 Hz');
d.fsample = 1/600; % 10*60 sec = 10 minutes
end
d.hdr.Fs = d.fsample; d.hdr.label = d.label;
firstsample = d.time{1}(1); lastsample = d.time{1}(end);d.sampleinfo(1,:) = [firstsample lastsample];
d.fname = [hdr.fname '_run-ChronicLeft' char(datetime(cdt(1),'format','yyyyMMddhhmmss'))];
d.keepfig = false; % do not keep figure with this signal open (the number of LFPTrendLogs can be high)
alldata{length(alldata)+1} = d;
end
end
if isfield(data.LFPTrendLogs,'HemisphereLocationDef_Right')
data.right=data.LFPTrendLogs.HemisphereLocationDef_Right;
runs = fieldnames(data.right);
for c=1:length(runs)
clfp = [data.right.(runs{c}).LFP];
cstim = [data.right.(runs{c}).AmplitudeInMilliAmps];
cdt = datetime({data.right.(runs{c}).DateTime},'InputFormat','yyyy-MM-dd''T''HH:mm:ss''Z''');
[cdt,i] = sort(cdt);
LFPR=[LFPR,clfp(i)];
STIMR=[STIMR,cstim(i)];
DTR=[DTR,cdt];
d=[];
d.hdr = hdr;d.datatype = 'DiagnosticData.LFPTrends';
d.trial{1} = [clfp;cstim];
d.label = {'LFP_RIGHT','STIM_RIGHT'};
d.time{1} = linspace(seconds(cdt(1)-hdr.d0),seconds(cdt(end)-hdr.d0),size(d.trial{1},2));
d.realtime{1} = cdt;
if length(d.time{1})>1
d.fsample = abs(1/diff(d.time{1}(1:2)))
else
warning('Only one data point recorded, assuming a sampling frequency of 1 / 10 minutes ~ 0.0017 Hz');
d.fsample = 1/600; % 10*60 sec = 10 minutes
end
;d.hdr.Fs = d.fsample; d.hdr.label = d.label;
firstsample = d.time{1}(1); lastsample = d.time{1}(end);d.sampleinfo(1,:) = [firstsample lastsample];
d.fname = [hdr.fname '_run-ChronicRight' char(datetime(cdt(1),'format','yyyyMMddhhmmss'))];
d.keepfig = false; % do not keep figure with this signal open (the number of LFPTrendLogs can be high)
alldata{length(alldata)+1} = d;
end
end
LFP=[];
STIM=[];
if isempty(DTL)
DT = sort(DTR);
elseif isempty(DTR)
DT = sort(DTL);
else
DT=sort([DTL,setdiff(DTR,DTL)]);
end
for c = 1:length(DT)
if ismember(DT(c),DTL)
i = find(DTL==DT(c));
LFP(1,c) = LFPL(i);
STIM(1,c) = STIML(i);
else
LFP(1,c) = nan;
STIM(1,c) = nan;
end
if ismember(DT(c),DTR)
i = find(DTR==DT(c));
LFP(2,c) = LFPR(i);
STIM(2,c) = STIMR(i);
else
LFP(2,c) = nan;
STIM(2,c) = nan;
end
end
d=[];
d.hdr = hdr;
d.datatype = 'DiagnosticData.LFPTrends';
d.trial{1} = [LFP;STIM];
d.label = {'LFP_LEFT','LFP_RIGHT','STIM_LEFT','STIM_RIGHT'};
d.time{1} = DT;
d.fsample = diff(DT);
firstsample = d.time{1}(1);
lastsample = d.time{1}(end);
d.sampleinfo(1,:) = [firstsample lastsample];
d.fname = [hdr.fname '_run-CHRONIC' char(datetime(DT(1),'format','yyyyMMddhhmmss'))];
% TODO: set if needed::
%d.keepfig = false; % do not keep figure with this signal open
alldata{length(alldata)+1} = d;
figure('Units','centimeters','PaperUnits','centimeters','Position',[1 1 40 20])
subplot(2,1,1)
title({strrep(hdr.fname,'_',' '),'CHRONIC LEFT'})
yyaxis left
scatter(DT,LFP(1,:),20,'filled','Marker','o')
ylabel('LFP Amplitude')
yyaxis right
scatter(DT,STIM(1,:),20,'filled','Marker','s')
ylabel('STIM Amplitude')
xlabel('Time')
subplot(2,1,2)
yyaxis left
scatter(DT,LFP(2,:),20,'filled','Marker','o')
ylabel('LFP Amplitude')
yyaxis right
scatter(DT,STIM(2,:),20,'filled','Marker','s')
title('RIGHT')
xlabel('Time')
ylabel('STIM Amplitude')
savefig(fullfile(hdr.fpath,[hdr.fname '_CHRONIC.fig']))
perceive_print(fullfile(hdr.fpath,[hdr.fname '_CHRONIC']))
end
if isfield(data,'LfpFrequencySnapshotEvents')
cdata= data.LfpFrequencySnapshotEvents;
Tpow=table;Trpow=table;Tlfit=table;pow=[];rpow=[];lfit=[];lfp=struct;
for c = 1:length(cdata)
try
lfp=cdata{c};
catch
lfp=cdata(c);
end
if lfp.LFP && isfield(lfp,'LfpFrequencySnapshotEvents')
ids(c) = lfp.EventID;
DT(c) = datetime(lfp.DateTime(1:end-1),'InputFormat','yyyy-MM-dd''T''HH:mm:ss');
events{c} = lfp.EventName;
if isfield(lfp.LfpFrequencySnapshotEvents,'HemisphereLocationDef_Left')
tmp = strsplit(strrep(lfp.LfpFrequencySnapshotEvents.HemisphereLocationDef_Left.SenseID,'_AND',''),'.');
if isempty(tmp{1}) || length(tmp) == 1
tmp = {'','unknown'};
end
ch1 = strcat(hdr.chan,'_L_',strrep(strrep(strrep(strrep(strrep(tmp{2},'ZERO','0'),'ONE','1'),'TWO','2'),'THREE','3'),'_',''));
else
ch1 = 'n/a';
end
if isfield(lfp.LfpFrequencySnapshotEvents,'HemisphereLocationDef_Right')
tmp = strsplit(strrep(lfp.LfpFrequencySnapshotEvents.HemisphereLocationDef_Right.SenseID,'_AND',''),'.');
if isempty(tmp{1}) || length(tmp) == 1
tmp = {'','unknown'};
end
ch2 = strcat(hdr.chan,'_R_',strrep(strrep(strrep(strrep(strrep(tmp{2},'ZERO','0'),'ONE','1'),'TWO','2'),'THREE','3'),'_',''));
else
ch2 = 'n/a';
end
chanlabels{c} = {ch1 ch2};
if ~isfield(lfp.LfpFrequencySnapshotEvents,'HemisphereLocationDef_Left') && ~isfield(lfp.LfpFrequencySnapshotEvents,'HemisphereLocationDef_Right')
error('none of HemisphereLocationDef_Left / HemisphereLocationDef_Right appear in LfpFrequencySnapshotEvents');
end
if isfield(lfp.LfpFrequencySnapshotEvents,'HemisphereLocationDef_Left')
stimgroups{c} = lfp.LfpFrequencySnapshotEvents.HemisphereLocationDef_Left.GroupId(end);
freq = lfp.LfpFrequencySnapshotEvents.HemisphereLocationDef_Left.Frequency;
else
stimgroups{c} = lfp.LfpFrequencySnapshotEvents.HemisphereLocationDef_Right.GroupId(end);
freq = lfp.LfpFrequencySnapshotEvents.HemisphereLocationDef_Right.Frequency;
end
if isfield(lfp.LfpFrequencySnapshotEvents,'HemisphereLocationDef_Left') && isfield(lfp.LfpFrequencySnapshotEvents,'HemisphereLocationDef_Right')
pow(:,1) = lfp.LfpFrequencySnapshotEvents.HemisphereLocationDef_Left.FFTBinData;
pow(:,2) = lfp.LfpFrequencySnapshotEvents.HemisphereLocationDef_Right.FFTBinData;
else
if isfield(lfp.LfpFrequencySnapshotEvents,'HemisphereLocationDef_Left')
pow(:,1) = lfp.LfpFrequencySnapshotEvents.HemisphereLocationDef_Left.FFTBinData;
pow(:,2) = 0*pow(:,1);
else
pow(:,2) = lfp.LfpFrequencySnapshotEvents.HemisphereLocationDef_Right.FFTBinData;
pow(:,1) = 0*pow(:,2);
end
end
Tpow.Frequency = freq;
Tpow.(strrep([events{c} '_' num2str(c) '_' ch1 '_' char(datetime(DT(c),'Format','yyyMMddHHmmss'))],' ','')) = pow(:,1);
Tpow.(strrep([events{c} '_' num2str(c) '_' ch2 '_' char(datetime(DT(c),'Format','yyyMMddHHmmss'))],' ','')) = pow(:,2);
figure
plot(freq,pow,'linewidth',2)
legend(strrep(chanlabels{c},'_',' '))
title({strrep(hdr.fname,'_',' ');char(DT(c));events{c};['STIM GROUP ' stimgroups{c}]})
xlabel('Frequency [Hz]')
ylabel('Power spectral density [uV�/Hz]')
savefig(fullfile(hdr.fpath,[hdr.fname '_LFPSnapshot_' events{c} '-' num2str(c) '.fig']))
perceive_print(fullfile(hdr.fpath,[hdr.fname '_LFPSnapshot_' events{c} '-' num2str(c)]))
else
% keyboard
warning('LFP Snapshot Event without LFP data present.')
end
end
writetable(Tpow,fullfile(hdr.fpath,[hdr.fname '_LFPSnapshotEvents.csv']))
end
case 'BrainSenseTimeDomain'
FirstPacketDateTime = strrep(strrep({data(:).FirstPacketDateTime},'T',' '),'Z','');
runs = unique(FirstPacketDateTime);
fsample = data.SampleRateInHz;
Pass = {data(:).Pass};
tmp = {data(:).GlobalSequences};
for c = 1:length(tmp)
GlobalSequences{c,:} = str2num(tmp{c});
missingPackages{c,:} = (diff(str2num(tmp{c}))==2);
nummissinPackages(c) = numel(find(diff(str2num(tmp{c}))==2));
end
tmp = {data(:).TicksInMses};
for c = 1:length(tmp)
TicksInMses{c,:}= str2num(tmp{c});
TicksInS{c,:} = (TicksInMses{c,:} - TicksInMses{c,:}(1))/1000;
end
tmp = {data(:).GlobalPacketSizes};
for c = 1:length(tmp)
GlobalPacketSizes{c,:} = str2num(tmp{c});
isDataMissing(c)= logical(TicksInS{c,:}(end) >= sum(GlobalPacketSizes{c,:})/fsample);
time_real{c,:} = TicksInS{c,:}(1):1/fsample:TicksInS{c,:}(end)+(GlobalPacketSizes{c,:}(end)-1)/fsample;
time_real{c,:} = round(time_real{c,:},3);
end
gain=[data(:).Gain]';
[tmp1,tmp2] = strtok(strrep({data(:).Channel}','_AND',''),'_');
ch1 = strrep(strrep(strrep(strrep(tmp1,'ZERO','0'),'ONE','1'),'TWO','2'),'THREE','3');
[tmp1,tmp2] = strtok(tmp2,'_');
ch2 = strrep(strrep(strrep(strrep(tmp1,'ZERO','0'),'ONE','1'),'TWO','2'),'THREE','3');
side = strrep(strrep(strtok(tmp2,'_'),'LEFT','L'),'RIGHT','R');
Channel = strcat(hdr.chan,'_',side,'_', ch1, ch2);
d=[];
for c = 1:length(runs)
i=perceive_ci(runs{c},FirstPacketDateTime);
try
x=find(ismember(i, find(isDataMissing)));
if ~isempty(x)
warning('missing packages detected, will interpolate to replace missing data')
for k=1:numel(x)
isReceived = zeros(size(time_real{i(k),:}, 2), 1);
nPackets = numel(GlobalPacketSizes{i(k),:});
for packetId = 1:nPackets
timeTicksDistance = abs(time_real{i(k),:} - TicksInS{i(k),:}(packetId));
[~, packetIdx] = min(timeTicksDistance);
if isReceived(packetIdx) == 1
packetIdx = packetIdx +1;
end
isReceived(packetIdx:packetIdx+GlobalPacketSizes{i(k),:}(packetId)-1) = isReceived(packetIdx:packetIdx+GlobalPacketSizes{i(k),:}(packetId)-1)+1;
% figure; plot(isReceived, '.'); yticks([0 1]); yticklabels({'not received', 'received'}); ylim([-1 10])
end
data_temp = NaN(size(time_real{i(k),:}, 2), 1);
data_temp(logical(isReceived), :) = data(i(k)).TimeDomainData;
ind_interp=find(diff(isReceived));
if isReceived(ind_interp(1)+1)==1
ind_interp=[1 ind_interp];
data_temp(1)=0;
end
if isReceived(ind_interp(end)+1)==0
ind_interp=[ind_interp size(data_temp,1)-1];
data_temp(end)=0;
end
for mm=1:2:numel(ind_interp/2)
data_temp(ind_interp(mm)+1:ind_interp(mm+1))=...
linspace(data_temp(ind_interp(mm)), data_temp(ind_interp(mm+1)+1), ind_interp(mm+1)-ind_interp(mm));
end
raw_temp(x(k),:)=data_temp';
end
raw=raw_temp;
else
raw=[data(i).TimeDomainData]';
end
catch unmatched_samples
for xi=1:length(i)
sl(xi)=length(data(i(xi)).TimeDomainData);
end
smin=min(sl);
raw=[];
for xi = 1:length(xi)
raw(xi,:) = data(i(xi)).TimeDomainData(1:smin);
end
warning('Sample size differed between channels. Check session affiliation.')
end
d.hdr = hdr;
d.datatype = datafields{b};
d.hdr.CT.Pass=strrep(strrep(unique(strtok(Pass(i),'_')),'FIRST','1'),'SECOND','2');
d.hdr.CT.GlobalSequences=GlobalSequences(i,:);
d.hdr.CT.GlobalPacketSizes=GlobalPacketSizes(i,:);
d.hdr.CT.FirstPacketDateTime = runs{c};
d.label=Channel(i);
d.trial{1} = raw;
d.time{1} = linspace(seconds(datetime(runs{c},'Inputformat','yyyy-MM-dd HH:mm:ss.sss')-hdr.d0),seconds(datetime(runs{c},'Inputformat','yyyy-MM-dd HH:mm:ss.sss')-hdr.d0)+size(d.trial{1},2)/fsample,size(d.trial{1},2));
d.fsample = fsample;
firstsample = 1+round(fsample*seconds(datetime(runs{c},'Inputformat','yyyy-MM-dd HH:mm:ss.sss')-hdr.d0));
lastsample = firstsample+size(d.trial{1},2);
d.sampleinfo(1,:) = [firstsample lastsample];
if firstsample<0
keyboard
end
d.trialinfo(1) = c;
d.fname = [hdr.fname '_run-BSTD' char(datetime(runs{c},'Inputformat','yyyy-MM-dd HH:mm:ss.sss','format','yyyyMMddhhmmss'))];
d.hdr.Fs = d.fsample;
d.hdr.label = d.label;
d.ecg=[];
d.ecg_cleaned=[];
for e = 1:size(raw,1)
d.ecg{e} = perceive_ecg(raw(e,:));
title(strrep(d.label{e},'_',' '))
xlabel(strrep(d.fname,'_',' '))
savefig(fullfile(hdr.fpath,[d.fname '_ECG_' d.label{e} '.fig']))
perceive_print(fullfile(hdr.fpath,[d.fname '_ECG_' d.label{e}]))
d.ecg_cleaned(e,:) = d.ecg{e}.cleandata;
end
% TODO: set if needed:
%d.keepfig = false; % do not keep figure with this signal open
alldata{length(alldata)+1} = d;
end
case 'BrainSenseLfp'
FirstPacketDateTime = strrep(strrep({data(:).FirstPacketDateTime},'T',' '),'Z','');
runs = unique(FirstPacketDateTime);
bsldata=[];bsltime=[];bslchannels=[];
figure('Units','centimeters','PaperUnits','centimeters','Position',[1 1 40 20])
for c=1:length(runs)
cdata = data(c);
tmp = strrep(cdata.Channel,'_AND','');
tmp = strsplit(strrep(strrep(strrep(strrep(strrep(tmp,'ZERO','0'),'ONE','1'),'TWO','2'),'THREE','3'),'_',''),',');
if length(tmp)==2
lfpchannels = {[hdr.chan '_' tmp{1}(3) '_' tmp{1}(1:2) ], ...
[hdr.chan '_' tmp{2}(3) '_' tmp{2}(1:2)]};
else
if length(tmp)==1
lfpchannels = {[hdr.chan '_' tmp{1}(3) '_' tmp{1}(1:2) ]};
else
error(['unsupported number of ' num2str(length(tmp)) 'sides in BrainSenseLfp']);
end
end
d=[];
d.hdr = hdr;
d.hdr.BSL.TherapySnapshot = cdata.TherapySnapshot;
if isfield(d.hdr.BSL.TherapySnapshot,'Left')
tmp = d.hdr.BSL.TherapySnapshot.Left;
lfpsettings{1,1} = ['PEAK' num2str(round(tmp.FrequencyInHertz)) 'Hz_THR' num2str(tmp.LowerLfpThreshold) '-' num2str(tmp.UpperLfpThreshold) '_AVG' num2str(round(tmp.AveragingDurationInMilliSeconds)) 'ms'];
stimchannels = ['STIM_L_' num2str(tmp.RateInHertz) 'Hz_' num2str(tmp.PulseWidthInMicroSecond) 'us'];
else
lfpsettings{1,1}='LFP n/a';
stimchannels = 'STIM n/a';
end
if isfield(d.hdr.BSL.TherapySnapshot,'Right')
tmp = d.hdr.BSL.TherapySnapshot.Right;
lfpsettings{2,1} = ['PEAK' num2str(round(tmp.FrequencyInHertz)) 'Hz_THR' num2str(tmp.LowerLfpThreshold) '-' num2str(tmp.UpperLfpThreshold) '_AVG' num2str(round(tmp.AveragingDurationInMilliSeconds)) 'ms'];
stimchannels = {stimchannels,['STIM_R_' num2str(tmp.RateInHertz) 'Hz_' num2str(tmp.PulseWidthInMicroSecond) 'us']};
else
lfpsettings{2,1} = 'LFP n/a';
stimchannels = {stimchannels,'STIM n/a'};
end
d.label = [strcat(lfpchannels','_',lfpsettings)' stimchannels];
d.hdr.label = d.label;
d.fsample = cdata.SampleRateInHz;
d.hdr.Fs = d.fsample;
tstart = cdata.LfpData(1).TicksInMs/1000;
for e =1:length(cdata.LfpData)
d.trial{1}(1:2,e) = [cdata.LfpData(e).Left.LFP;cdata.LfpData(e).Right.LFP];
d.trial{1}(3:4,e) = [cdata.LfpData(e).Left.mA;cdata.LfpData(e).Right.mA];
d.time{1}(e) = seconds(datetime(runs{c},'InputFormat','yyyy-MM-dd HH:mm:ss.SSS')-hdr.d0)+((cdata.LfpData(e).TicksInMs/1000)-tstart);
d.realtime(e) = datetime(runs{c},'Inputformat','yyyy-MM-dd HH:mm:ss.SSS','Format','yyyy-MM-dd HH:mm:ss.SSS')+seconds((d.time{1}(e)-d.time{1}(1)));
d.hdr.BSL.seq(e)= cdata.LfpData(e).Seq;
end
d.trialinfo(1) = c;
d.hdr.realtime = d.realtime;
d.fname = [hdr.fname '_run-BSL' char(datetime(runs{c},'Inputformat','yyyy-MM-dd HH:mm:ss.sss','format','yyyyMMddhhmmss'))];
subplot(2,1,1)
yyaxis left
lp=plot(d.realtime,d.trial{1}(1,:),'linewidth',2);
ylabel('LFP Amplitude')
yyaxis right
sp=plot(d.realtime,d.trial{1}(3,:),'linewidth',2,'linestyle','--');
title(strrep(strrep(d.fname,'_','-'),'_',' '))
ylabel('Stimulation Amplitude')
legend([lp sp],strrep(d.label([1 3]),'_',' '),'location','northoutside')
xlabel('Time')
xlim([d.realtime(1) d.realtime(end)])
subplot(2,1,2)
yyaxis left
lp=plot(d.realtime,d.trial{1}(2,:),'linewidth',2);
ylabel('LFP Amplitude')
yyaxis right
title('RIGHT')
sp=plot(d.realtime,d.trial{1}(4,:),'linewidth',2,'linestyle','--');
ylabel('Stimulation Amplitude')
legend([lp sp],strrep(d.label([2 4]),'_',' '),'location','northoutside')
xlabel('Time')
xlim([d.realtime(1) d.realtime(end)])
bsldata = [bsldata,d.trial{1}];
bsltime = [bsltime,d.realtime];
bslchannels = d.label;
% TODO: set if needed:
%d.keepfig = false; % do not keep figure with this signal open
alldata{length(alldata)+1} = d;
savefig(fullfile(hdr.fpath,[d.fname '.fig']))
perceive_print(fullfile(hdr.fpath,[d.fname]))
end
T=table;
T.Time = bsltime';
for c = 1:length(bslchannels)
try
T.(bslchannels{c}) = bsldata(c,:)';
catch
T.(strrep(bslchannels{c},'-','_')) = bsldata(c,:)';
end
end
writetable(T,fullfile(hdr.fpath,[hdr.fname '_run-BrainSenseLFP.csv']))
case 'LfpMontageTimeDomain'
FirstPacketDateTime = strrep(strrep({data(:).FirstPacketDateTime},'T',' '),'Z','');
runs = unique(FirstPacketDateTime);
Pass = {data(:).Pass};
tmp = {data(:).GlobalSequences};
for c = 1:length(tmp)
GlobalSequences{c,:} = str2num(tmp{c});
end
tmp = {data(:).GlobalPacketSizes};
for c = 1:length(tmp)
GlobalPacketSizes{c,:} = str2num(tmp{c});
end
fsample = data.SampleRateInHz;
gain=[data(:).Gain]';
[tmp1,tmp2] = strtok(strrep({data(:).Channel}','_AND',''),'_');
[tmp1] = split({data(:).Channel}','_AND_'); % tmp1 is a tuple of first str part before AND and second str part after AND
% ch1 = strrep(strrep(strrep(strrep(tmp1,'ZERO','0'),'ONE','1'),'TWO','2'),'THREE','3');
ch1 = strrep(strrep(strrep(strrep(tmp1(:,1),'ZERO','0'),'ONE','1'),'TWO','2'),'THREE','3'); % ch1 replaces ZERO to int 0 etc of first part before AND (tmp1(:,1))
% [tmp1,tmp2] = strtok(tmp2,'_');
% ch2 = strrep(strrep(strrep(strrep(tmp1,'ZERO','0'),'ONE','1'),'TWO','2'),'THREE','3');
ch2 = strrep(strrep(strrep(strrep(tmp1(:,2),'ZERO','0'),'ONE','1'),'TWO','2'),'THREE','3'); % ch2 replaces ZERO to int 0 etc of second part after AND (tmp1(:,1))
% side = strrep(strrep(strtok(tmp2,'_'),'LEFT','L'),'RIGHT','R');
% Channel = strcat(hdr.chan,'_',side,'_', ch1, ch2);
Channel = strcat(hdr.chan,'_', ch1,'_', ch2); % taken out "side" so RIGHT and LEFT will stay the same, no transformation to R and L
d=[];
for c = 1:length(runs)
i=perceive_ci(runs{c},FirstPacketDateTime);
d=[];
d.hdr = hdr;
d.datatype = datafields{b};
d.hdr.IS.Pass=strrep(strrep(unique(strtok(Pass(i),'_')),'FIRST','1'),'SECOND','2');
d.hdr.IS.GlobalSequences=GlobalSequences(i,:);
d.hdr.IS.GlobalPacketSizes=GlobalPacketSizes(i,:);
d.hdr.IS.FirstPacketDateTime = runs{c};
tmp = [data(i).TimeDomainData]';
d.trial{1} = [tmp];
d.label=Channel(i);
d.time{1} = linspace(seconds(datetime(runs{c},'Inputformat','yyyy-MM-dd HH:mm:ss.sss')-hdr.d0),seconds(datetime(runs{c},'Inputformat','yyyy-MM-dd HH:mm:ss.sss')-hdr.d0)+size(d.trial{1},2)/fsample,size(d.trial{1},2));
d.fsample = fsample;
firstsample = 1+round(fsample*seconds(datetime(runs{c},'Inputformat','yyyy-MM-dd HH:mm:ss.sss')-datetime(FirstPacketDateTime{1},'Inputformat','yyyy-MM-dd HH:mm:ss.sss')));
lastsample = firstsample+size(d.trial{1},2);
d.sampleinfo(1,:) = [firstsample lastsample];
d.trialinfo(1) = c;
d.hdr.label = d.label;
d.hdr.Fs = d.fsample;
d.fname = [hdr.fname '_run-LMTD' char(datetime(runs{c},'Inputformat','yyyy-MM-dd HH:mm:ss.sss','format','yyyyMMddhhmmss')), '_',num2str(c)];
% TODO: set if needed:
%d.keepfig = false; % do not keep figure with this signal open
alldata{length(alldata)+1} = d;
end
case 'BrainSenseSurvey'
channels={};
pow=[];rpow=[];lfit=[];bad=[];
for c = 1:length(data)
cdata = data(c);
if iscell(cdata)
cdata=cdata{1};
end
tmp=strsplit(cdata.Hemisphere,'.');
side=tmp{2}(1);
tmp=strsplit(cdata.SensingElectrodes,'.');tmp=strrep(tmp{2},'_AND_','');
ch = strrep(strrep(strrep(strrep(tmp,'ZERO','0'),'ONE','1'),'TWO','2'),'THREE','3');
channels{c} = [hdr.chan '_' side '_' ch];
freq = cdata.LFPFrequency;
pow(c,:) = cdata.LFPMagnitude;
rpow(c,:) = perceive_power_normalization(pow(c,:),freq);
lfit(c,:) = perceive_fftlogfitter(freq,pow(c,:));
bad(c,1) = strcmp('IFACT_PRESENT',cdata.ArtifactStatus(end-12:end));
try
peaks(c,1) = cdata.PeakFrequencyInHertz;
peaks(c,2) = cdata.PeakMagnitudeInMicroVolt;
catch
peaks(c,:)=nan(1,2);
end
end
T=array2table([freq';pow;rpow;lfit]','VariableNames',[{'Frequency'};strcat({'POW'},channels');strcat({'RPOW'},channels');strcat({'LFIT'},channels')]);
writetable(T,fullfile(hdr.fpath,[hdr.fname '_run-BrainSenseSurveyPowerSpectra.csv']));
T=array2table(peaks','VariableNames',channels,'RowNames',{'PeakFrequency','PeakPower'});
writetable(T,fullfile(hdr.fpath,[hdr.fname '_run-BrainSenseSurvey_Peaks.csv']));
figure('Units','centimeters','PaperUnits','centimeters','Position',[1 1 40 20])
ir = perceive_ci([hdr.chan '_R'],channels);
subplot(1,2,2)
p=plot(freq,pow(ir,:));
set(p(find(bad(ir))),'linestyle','--')
hold on
plot(freq,nanmean(pow(ir,:)),'color','k','linewidth',2)
xlim([1 35])
plot(peaks(ir,1),peaks(ir,2),'LineStyle','none','Marker','.','MarkerSize',12)
for c = 1:length(ir)
if peaks(ir(c),1)>0
text(peaks(ir(c),1),peaks(ir(c),2),[' ' num2str(peaks(ir(c),1),3) ' Hz'])
end
end
xlabel('Frequency [Hz]')
ylabel('Power spectral density [uV�/Hz]')
title(strrep({hdr.subject,char(hdr.SessionDate),'RIGHT'},'_',' '))
legend(strrep(channels(ir),'_',' '))
il = perceive_ci([hdr.chan '_L'],channels);
subplot(1,2,1)
p=plot(freq,pow(il,:));
set(p(find(bad(il))),'linestyle','--')
hold on
plot(freq,nanmean(pow(il,:)),'color','k','linewidth',2)
xlim([1 35])
title(strrep({hdr.subject,char(hdr.SessionDate),'LEFT'},'_',' '))
plot(peaks(il,1),peaks(il,2),'LineStyle','none','Marker','.','MarkerSize',12)
xlabel('Frequency [Hz]')
ylabel('Power spectral density [uV�/Hz]')
for c = 1:length(il)
if peaks(il(c),1)>0
text(peaks(il(c),1),peaks(il(c),2),[' ' num2str(peaks(il(c),1),3) ' Hz'])
end
end
legend(strrep(channels(il),'_',' '))
savefig(fullfile(hdr.fpath,[hdr.fname '_run-BrainSenseSurvey.fig']))
pause(2)
perceive_print(fullfile(hdr.fpath,[hdr.fname '_run-BrainSenseSurvey']))
case 'IndefiniteStreaming'
FirstPacketDateTime = strrep(strrep({data(:).FirstPacketDateTime},'T',' '),'Z','');
runs = unique(FirstPacketDateTime);
fsample = data.SampleRateInHz;
Pass = {data(:).Pass};
tmp = {data(:).GlobalSequences};
for c = 1:length(tmp)
GlobalSequences{c,:} = str2num(tmp{c});
missingPackages{c,:} = (diff(str2num(tmp{c}))==2);
nummissinPackages(c) = numel(find(diff(str2num(tmp{c}))==2));
end
tmp = {data(:).TicksInMses};
for c = 1:length(tmp)
TicksInMses{c,:} = str2num(tmp{c});
TicksInS_temp = (TicksInMses{c,:} - TicksInMses{c,:}(1))/1000;
[TicksInS_temp,~,ci_temp] = unique(TicksInS_temp);
TicksInS{c,:} = TicksInS_temp;
ci{c,:} = ci_temp;
end
tmp = {data(:).GlobalPacketSizes};
for c = 1:length(tmp)
GlobalPacketSizes_temp = str2num(tmp{c});
for kk=1:max(ci{c,:})
GPS_temp(kk)=sum(GlobalPacketSizes_temp(find(ci{c,:}==kk)));
end
GlobalPacketSizes{c,:} = GPS_temp;
isDataMissing(c) = logical(TicksInS{c,:}(end) >= sum(GlobalPacketSizes{c,:})/fsample);
time_real{c,:} = TicksInS{c,:}(1):1/fsample:TicksInS{c,:}(end)+(GlobalPacketSizes{c,:}(end)-1)/fsample;
time_real{c,:} = round(time_real{c,:},3);
end
gain=[data(:).Gain]';
[tmp1,tmp2] = strtok(strrep({data(:).Channel}','_AND',''),'_');
ch1 = strrep(strrep(strrep(strrep(tmp1,'ZERO','0'),'ONE','1'),'TWO','2'),'THREE','3');
[tmp1,tmp2] = strtok(tmp2,'_');
ch2 = strrep(strrep(strrep(strrep(tmp1,'ZERO','0'),'ONE','1'),'TWO','2'),'THREE','3');
side = strrep(strrep(strtok(tmp2,'_'),'LEFT','L'),'RIGHT','R');
Channel = strcat(hdr.chan,'_',side,'_', ch1, ch2);
d=[];
for c = 1:length(runs)
i=perceive_ci(runs{c},FirstPacketDateTime);
d=[];
d.hdr = hdr;
d.datatype = datafields{b};
d.hdr.IS.Pass=strrep(strrep(unique(strtok(Pass(i),'_')),'FIRST','1'),'SECOND','2');
d.hdr.IS.GlobalSequences=GlobalSequences(i,:);
d.hdr.IS.GlobalPacketSizes=GlobalPacketSizes(i,:);
d.hdr.IS.FirstPacketDateTime = runs{c};
x=find(ismember(i, find(isDataMissing)));
if ~isempty(x)
warning('missing packages detected, will interpolate to replace missing data')
for k=1:numel(x)
isReceived = zeros(size(time_real{i(k),:}, 2), 1);
nPackets = numel(GlobalPacketSizes{i(k),:});
for packetId = 1:nPackets
timeTicksDistance = abs(time_real{i(k),:} - TicksInS{i(k),:}(packetId));
[~, packetIdx] = min(timeTicksDistance);
if isReceived(packetIdx) == 1
packetIdx = packetIdx +1;
end
% if packetIdx+GlobalPacketSizes{i(k),:}(packetId)-1>size(isReceived,1)
% cut_sampl=size(isReceived,1)-packetIdx+GlobalPacketSizes{i(k),:}(packetId);
% isReceived(packetIdx:packetIdx+GlobalPacketSizes{i(k),:}(packetId)-cut_sampl) = isReceived(packetIdx:packetIdx+GlobalPacketSizes{i(k),:}(packetId)-cut_sampl)+1;
% else
isReceived(packetIdx:packetIdx+GlobalPacketSizes{i(k),:}(packetId)-1) = isReceived(packetIdx:packetIdx+GlobalPacketSizes{i(k),:}(packetId)-1)+1;
% figure; plot(isReceived, '.'); yticks([0 1]); yticklabels({'not received', 'received'}); ylim([-1 10])
% end
end
%If there are pseudo double-received samples, compensate non-received samples
% numel(find(logical(isReceived)))+nDoubles
doublesIdx = find(isReceived == 2);
nDoubles = numel(doublesIdx);
for doubleId = 1:nDoubles
missingIdx = find(isReceived == 0);
[~, idxOfidx] = min(abs(missingIdx - doublesIdx(doubleId)));
isReceived(missingIdx(idxOfidx)) = 1;
isReceived(doublesIdx(doubleId)) = 1;
end
data_temp = NaN(size(time_real{i(k),:}, 2), 1);
data_temp(logical(isReceived), :) = data(i(k)).TimeDomainData;
ind_interp=find(diff(isReceived));
if isReceived(ind_interp(1)+1)==1
ind_interp=[1 ind_interp];
data_temp(1)=0;
end
if isReceived(ind_interp(end)+1)==0
ind_interp=[ind_interp size(data_temp,1)-1];
data_temp(end)=0;
end
for mm=1:2:numel(ind_interp/2)
data_temp(ind_interp(mm)+1:ind_interp(mm+1))=...
linspace(data_temp(ind_interp(mm)), data_temp(ind_interp(mm+1)+1), ind_interp(mm+1)-ind_interp(mm));
end
raw_temp(x(k),:)=data_temp';
end
tmp=raw_temp;
else
tmp=[data(i).TimeDomainData]';
end
xchans = perceive_ci({'L_03','L_13','L_02','R_03','R_13','R_02'},Channel(i));
nchans = {'L_01','L_12','L_23','R_01','R_12','R_23'};
refraw = [tmp(xchans(1),:)-tmp(xchans(2),:);(tmp(xchans(1),:)-tmp(xchans(2),:))-tmp(xchans(3),:);tmp(xchans(3),:)-tmp(xchans(1),:);
tmp(xchans(4),:)-tmp(xchans(5),:);(tmp(xchans(4),:)-tmp(xchans(5),:))-tmp(xchans(6),:);tmp(xchans(6),:)-tmp(xchans(4),:)];
d.trial{1} = [tmp;-refraw;];
d.label=[Channel(i);strcat(hdr.chan,'_',nchans')];
d.time{1} = linspace(seconds(datetime(runs{c},'Inputformat','yyyy-MM-dd HH:mm:ss.sss')-hdr.d0),seconds(datetime(runs{c},'Inputformat','yyyy-MM-dd HH:mm:ss.sss')-hdr.d0)+size(d.trial{1},2)/fsample,size(d.trial{1},2));
d.fsample = fsample;
firstsample = 1+round(fsample*seconds(datetime(runs{c},'Inputformat','yyyy-MM-dd HH:mm:ss.sss')-datetime(FirstPacketDateTime{1},'Inputformat','yyyy-MM-dd HH:mm:ss.sss')));
lastsample = firstsample+size(d.trial{1},2);
d.sampleinfo(1,:) = [firstsample lastsample];
d.trialinfo(1) = c;
d.hdr.label=d.label;
d.hdr.Fs = d.fsample;
d.fname = [hdr.fname '_run-IS' char(datetime(runs{c},'Inputformat','yyyy-MM-dd HH:mm:ss.sss','format','yyyyMMddhhmmss'))];
% TODO: set if needed:
%d.keepfig = false; % do not keep figure with this signal open
alldata{length(alldata)+1} = d;
end
case 'CalibrationTests'
FirstPacketDateTime = strrep(strrep({data(:).FirstPacketDateTime},'T',' '),'Z','');
runs = unique(FirstPacketDateTime);
Pass = {data(:).Pass};
tmp = {data(:).GlobalSequences};
for c = 1:length(tmp)
GlobalSequences{c,:} = str2num(tmp{c});
end
tmp = {data(:).GlobalPacketSizes};
for c = 1:length(tmp)
GlobalPacketSizes{c,:} = str2num(tmp{c});
end
figure
for c = 1:length(data)