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perceive.m
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function perceive(files, sub, sesMedOffOn01, extended, gui, datafields, localsettings)
% https://github.com/neuromodulation/perceive
% Toolbox by Wolf-Julian Neumann
% v1.0 update by J Vanhoecke
% Merge requests from Jennifer Behnke and Mansoureh Fahimi
% Contributors Wolf-Julian Neumann, Tomas Sieger, Gerd Tinkhauser
% This is an open research tool that is not intended for clinical purposes.
%
% INPUT:
% file ["", 'Report_Json_Session_Report_20200115T123657.json', {'Report_Json_Session_Report_20200115T123657.json','Report_Json_Session_Report_20200115T123658.json'}, ...]
% sub ["", 7, 21 , "021", ... ]
% sesMedOffOn01 ["","MedOff01","MedOn01","MedOff02","MedOn02","MedOff03","MedOn03","MedOffOn01"]
% extended ["","yes"] %% gives an extensive output of chronic, calibration, lastsignalcheck, diagnostic, impedance and snapshot data
% gui ["","yes"] %% gives option to skip gui by default settings
%% INPUT
arguments
files {mustBeA(files,["char","cell"])} = '';
% 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.
sub {mustBeA(sub,["char","cell","numeric"])} = '';
% sub:
% 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)
sesMedOffOn01 {mustBeMember(sesMedOffOn01,["","MedOff","MedOn","MedDaily","MedOff01","MedOn01","MedOff02","MedOn02","MedOff03","MedOn03","MedOffOn01","MedOffOn02","MedOffOn03"])} = '';
%task = 'TASK'; %All types of tasks: Rest, RestTap, FingerTapL, FingerTapR, UPDRS, MovArtArms,MovArtStand,MovArtHead,MovArtWalk
%acq = ''; %StimOff, StimOnL, StimOnR, StimOnB, Burst
%mod = ''; %BrainSense, IS, LMTD, Chronic + Bip Ring RingL RingR SegmIntraL SegmInterL SegmIntraR SegmInterR
%run = ''; %numeric
extended {mustBeMember(extended,["","yes"])} = '';
% '' means not extended, 'yes' means extended (default no)
gui {mustBeMember(gui,["","yes"])} = 'yes';
% '' means no gui, 'yes' means gui (default yes)
datafields {mustBeMember(datafields,["","BrainSenseLfp","BrainSenseSurvey","BrainSenseTimeDomain","CalibrationTests","DiagnosticData","EventSummary","Impedance","IndefiniteStreaming","LfpMontageTimeDomain","MostRecentInSessionSignalCheck","PatientEvents"])} ='';
localsettings ='';
end
%% 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)
% BrainSenseBip = combination of BSL and BSTD into Brainsense with LFP signal/stim settings.
% for modalities see white paper: https://www.medtronic.com/content/dam/medtronic-wide/public/western-europe/products/neurological/percept-pc-neurostimulator-whitepaper.pdf
% Jimenez-Shahed, J. (2021). Expert Review of Medical Devices, 18(4), 319–332. https://doi.org/10.1080/17434440.2021.1909471
% Yohann Thenaisie et al (2021) J. Neural Eng. 18 042002 DOI https://doi.org/10.1088/1741-2552/ac1d5b
%% TODO:
% ADD DEIDENTIFICATION OF COPIED JSON -> remove copied json, OK
% BUG FIX UTC? -> yes
% 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
%ubersichtzeit = table('Size',[1 8],'VariableNames',{'fname','FirstPackagetime','TicksMSecStart','TicksMSecEnd','TDTimeStart','TDTimeEnd','SumGlobalPackages','Triallength'},'VariableTypes',{'string','string','double','double','double','double','double','double'})
if exist('datafields','var') && ischar(datafields) && ~isempty(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
%% load local settings
check_followup_time=false;
check_gui_tasks=false;
check_gui_med=false;
if isfield(localsettings,'name')
if strcmp(localsettings.name, 'Charite')
check_followup_time=true;
check_gui_tasks=true;
check_gui_med=true;
datafields = {"IndefiniteStreaming","LfpMontageTimeDomain","BrainSenseTimeDomain"}; %delete this section
end
end
%% disable ecg cleaning
ecg_cleaning = false;
%% create subject
if exist('sub','var')
if isnumeric(sub)
sub=num2str(sub);
end
if ischar(sub) && ~isempty(sub)
if length(sub) == sum(isstrprop(sub,'digit'))
sub=pad(sub,3,'left','0');
sub=['sub-' sub];
end
sub={sub};
end
end
%% create task
task = 'task-Rest';
%% create run / mod / acq
run = 0;
mod = '';
acq = 'acq-StimOff';
%% iterate over files
for a = 1:length(files)
filename = files{a};
disp(['RUNNING ' filename])
js = jsondecode(fileread(filename));
js = pseudonymize(js);
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
hdr.SessionEndDate = datetime(strrep(js.SessionEndDate(1:end-1),'T',' ')); %To Do
hdr.SessionEndDate = datetime(strrep(js.SessionDate(1:end-1),'T',' ')); %To Do
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','_'),':','-'); %To Do
hdr.BatteryPercentage = js.BatteryInformation.BatteryPercentage;
hdr.LeadLocation = strsplit(hdr.LeadConfiguration.Final(1).LeadLocation,'.');hdr.LeadLocation=hdr.LeadLocation{2};
%% preset subject
if isempty(sub)
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
sub = {hdr.subject};
elseif iscell(sub) && length(sub) == length(files)
hdr.subject = sub{a};
elseif isscalar(sub)
hdr.subject = sub{1};
end
% determine session
if isempty(sesMedOffOn01)
ses = ['ses-' char(datetime(hdr.SessionEndDate,'format','yyyyMMddhhmmss')) num2str(hdr.BatteryPercentage)];
hdr.session = ses;
else
%% preset session
if ~ischar(sesMedOffOn01)
sesMedOffOn01=char(sesMedOffOn01);
end
diffmonths=between(datetime(hdr.SessionEndDate,'format','yyyyMMdd') , datetime(strrep(strtok(hdr.ImplantDate,'_'),'-',''),'format','yyyyMMdd'));
diffmonths=abs(calmonths(diffmonths));
presetmonths=[0,1,2,3,6,12,18,24,30,36,42,48,60,72,84,96,108,120];
diffmonths = interp1(presetmonths,presetmonths,diffmonths,'nearest');
diffmonths=num2str(diffmonths);
if check_followup_time
loc_diffmonths=localsettings.followup{1}(3:end-1);
assert(~contains(loc_diffmonths,lettersPattern, 'IgnoreCase',true))
if ~strcmp(loc_diffmonths,diffmonths)
warning('needed to update the Fu time')
fid = fopen('update_followup_time.txt', 'a+');
[~, jsfile, ~] = fileparts(filename);
fprintf(fid, [hdr.subject ' ' jsfile ' ' diffmonths ' ' loc_diffmonths '\n']);
fclose(fid);
diffmonths= loc_diffmonths;
else
diffmonths= loc_diffmonths; % always take the FU input
end
end
%% create session
ses = ['ses-', 'Fu' pad(diffmonths,2,'left','0'), 'm' , sesMedOffOn01];
hdr.session = ses;
end
%% create metatable %determine
MetaT = cell2table(cell(0,10),'VariableNames', {'report','perceiveFilename','session','condition','task','contacts','run','part','acq','remove'});
%% create dirs and path
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 '_' task '_' acq mod]; % do not add extra '_'
hdr.chan = ['LFP_' hdr.LeadLocation];
AbnormalEnd = js.AbnormalEnd;
if ~AbnormalEnd
hdr.d0 = datetime(js.SessionEndDate(1:10));
else
warning('This recording had an abnormal end')
hdr.d0 = datetime(js.DeviceInformation.Final.DeviceDateTime(1:10));
end
if isfield(js, 'DataVersion')
assert(strcmp(js.DataVersion, '1.2'), 'Version implentation until 1.2, contact Jojo Vanhoecke for update')
DataVersion = 1.2;
else
DataVersion = 0;
end
hdr.js = js;
if isempty(datafields)
datafields = sort({'EventSummary','Impedance','MostRecentInSessionSignalCheck','BrainSenseLfp','BrainSenseTimeDomain','LfpMontageTimeDomain','IndefiniteStreaming','BrainSenseSurvey','CalibrationTests','PatientEvents','DiagnosticData','BrainSenseSurveysTimeDomain','BrainSenseSurveys'});
%if DataVersion
% datafields = sort({'BrainSenseSurveysTimeDomain','BrainSenseSurveys'});
%end
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
mod='';
run=1;
counterBSL=0;
switch datafields{b}
%% add csv files by default
case 'Impedance'
if extended
perceive_impedance(data, hdr)
end
case 'PatientEvents'
disp(fieldnames(data));
case 'MostRecentInSessionSignalCheck'
if extended
mod = 'mod-MostRecentSignalCheck';
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 '_' mod 'PowerSpectra.csv']));
T=array2table(peaks','VariableNames',channels,'RowNames',{'PeakFrequency','PeakPower'});
writetable(T,fullfile(hdr.fpath,[hdr.fname '_' mod '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^2/Hz]')
title(strrep({hdr.subject,char(hdr.SessionEndDate),'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.SessionEndDate),'LEFT'},'_',' '))
plot(peaks(il,1),peaks(il,2),'LineStyle','none','Marker','.','MarkerSize',12)
xlabel('Frequency [Hz]')
ylabel('Power spectral density [uV^2/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 '_' mod]))
end
end
case 'DiagnosticData'
if extended
hdr.fname = strrep(hdr.fname,'StimOff','StimX');
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); warning('firstsample is not exactly computed for chronic recordings')
lastsample = d.time{1}(end);d.sampleinfo(1,:) = [firstsample lastsample];
mod= 'mod-ChronicLeft';
hdr.fname = strrep(hdr.fname, 'task-Rest', 'task-None');
d.fname = [hdr.fname '_' mod];
d.fnamedate = [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); warning('firstsample is not exactly computed for chronic recordings')
lastsample = d.time{1}(end);d.sampleinfo(1,:) = [firstsample lastsample];
mod = 'mod-ChronicRight';
hdr.fname = strrep(hdr.fname, 'task-Rest', 'task-None');
d.fname = [hdr.fname '_' mod];
d.fnamedate = [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); warning('firstsample is not exactly computed for chronic recordings')
lastsample = d.time{1}(end);
d.sampleinfo(1,:) = [firstsample lastsample];
mod = 'mod-Chronic';
hdr.fname = strrep(hdr.fname, 'task-Rest', 'task-None');
d.fname = [hdr.fname '_' mod];
d.fnamedate = [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}) || isscalar(tmp)
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}) || isscalar(tmp)
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^2/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
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) %missing
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) %missing
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)
% time correction Jeroen Habets
%
%perceive time correction
% '''
i=perceive_ci(runs{c},FirstPacketDateTime);
% try
% x=find(ismember(i, find(isDataMissing))); %missing
% if ~isempty(x)
% warning('missing packages detected, will interpolate to replace missing data') %missing
% 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
raw1=[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
% % % raw=[];
% % % for ii=1:length(i)
% % % raw(ii,:)=check_and_correct_lfp_missingData_in_json(data,ii, hdr);
% % % end
% % %
% % % assert(size(raw1,1)==size(raw,1));
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} = raw1;
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.time_real = time_real{i,1}; %insert real time here
d.fsample = fsample;
%firstsample = 1+round(fsample*seconds(datetime(runs{c},'Inputformat','yyyy-MM-dd HH:mm:ss.SSS')-hdr.d0));
firstsample = set_firstsample(data(i(1)).TicksInMses);
assert(firstsample==set_firstsample(data(i(end)).TicksInMses))
lastsample = firstsample+size(d.trial{1},2);
d.sampleinfo(1,:) = [firstsample lastsample];
if firstsample<0
keyboard
end
d.BrainSenseDateTime = [datetime(runs{c},'Inputformat','yyyy-MM-dd HH:mm:ss.SSS','Format','yyyy-MM-dd HH:mm:ss.SSS'), datetime(runs{c},'Inputformat','yyyy-MM-dd HH:mm:ss.SSS','Format','yyyy-MM-dd HH:mm:ss.SSS') + seconds(size(d.trial{1},2)/fsample)];
d.trialinfo(1) = c;
mod = 'mod-BSTD';
d.fname = [hdr.fname '_' mod];
d.fnamedate = [char(datetime(runs{c},'Inputformat','yyyy-MM-dd HH:mm:ss.SSS','format','yyyyMMddhhmmss'))];
d.hdr.Fs = d.fsample;
d.hdr.label = d.label;
if ecg_cleaning
d=call_ecg_cleaning(d,hdr,raw);
end
% TODO: set if needed:
%d.keepfig = false; % do not keep figure with this signal open
%%%Gaetanon pseudocode
tmp = strsplit(data(i(1),:).TicksInMses, ',');
tmp(end)=[];
TicksInMses= cellfun(@(x)str2double(x), tmp);
tmp = strsplit(data(i(1),:).GlobalPacketSizes, ',');
tmp(end)=[];
GlobalPacketSize= cellfun(@(x)str2double(x), tmp);
TDtime = (TicksInMses(end)- (GlobalPacketSize(end)-1)/fsample) : 1/fsample : TicksInMses(end);
for m=length(GlobalPacketSize):-1:2
if TicksInMses(m)-TicksInMses(m-1) > (1 + GlobalPacketSize(m))/ fsample
Prev_packet = (TicksInMses(m-1)- (GlobalPacketSize(m-1)-1)/ fsample) : 1/fsample : TicksInMses(m-1);
TDtime = [Prev_packet,TDtime];
else
Prev_packet = (TDtime(1)- GlobalPacketSize(m-1)/ fsample): 1/fsample : TDtime(1) - 1/fsample;
TDtime = [Prev_packet,TDtime];
end
end
d.TDtime = TDtime;
d.sampleinfo(1,:) = [firstsample lastsample];
%%% track time
%ubersichtzeit.fname(end+1)=d.fname;
%ubersichtzeit.FirstPackagetime(end)=FirstPacketDateTime(i(1));
%ubersichtzeit.TicksMSecStart(end) =TicksInMses(1);
%ubersichtzeit.TicksMSecEnd(end) = TicksInMses(end);
%ubersichtzeit.TDTimeStart(end) = TDtime(1);
%ubersichtzeit.TDTimeEnd(end) =TDtime(end);
%ubersichtzeit.SumGlobalPackages(end) =sum(GlobalPacketSize);
%ubersichtzeit.Triallength(end) = length(d.trial{1});
%%%
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;
%% set the name for BSL and STIM
counterBSL= counterBSL+1;
mod = 'mod-BSL';
d.fname = [hdr.fname '_' mod ];
d.fname = strrep(d.fname,'task-Rest',['task-TASK' num2str(counterBSL)]);
if contains(d.label(3),'STIM_L')
LAmp=d.trial{1}(3,:);
elseif contains(d.label(4),'STIM_L')
LAmp=d.trial{1}(4,:);
else
LAmp=0;
end
if contains(d.label(3),'STIM_R')
RAmp=d.trial{1}(3,:);
elseif contains(d.label(4),'STIM_R')
RAmp=d.trial{1}(4,:);
else
RAmp=0;
end
% d.hdr.SessionDate
% d.hdr.Groups
% d.hdr.Groups.Initial(1).GroupSettings.Cycling
% d.hdr.Groups.Initial(2).GroupSettings.Cycling
% d.hdr.Groups.Initial(3).GroupSettings.Cycling
% d.hdr.Groups.Initial(4).GroupSettings.Cycling
% %d.hdr.Groups.Initial(5).GroupSettings.Cycling
acq=check_stim(LAmp, RAmp, d.hdr);
d.fname = strrep(d.fname,'StimOff',acq);
d.fnamedate = [char(datetime(runs{c},'Inputformat','yyyy-MM-dd HH:mm:ss.SSS','format','yyyyMMddhhmmss'))];
%% plot integrated BrainSense plot
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','--');
sgtitle(strrep(strrep(strrep(d.fname,'_','-'),'_',' '),'BSL','BrainSenseBIP'))
title('LEFT')
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,[strrep(d.fname, 'BSL','BrainSenseBip')]))
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
mod = 'mod-BrainsenseLFP';
writetable(T,fullfile(hdr.fpath,[hdr.fname '_' mod '.csv']))
case 'LfpMontageTimeDomain'
%% add perceive ecg add figures cleaning
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]';
%if contains()
[tmp1]=split({data(:).Channel}', regexpPattern("(_AND_)|((?<!.*_.*)_(?!.*_AND_.*))"));
%[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')));
firstsample = set_firstsample(data(i(1)).TicksInMses);
lastsample = firstsample+size(d.trial{1},2);
%%fix later%d.sampleinfo(1,:) = [firstsample lastsample];
d.trialinfo(1) = c;
d.hdr.label = d.label;
d.hdr.Fs = d.fsample;
mod = 'mod-LMTD';
d.fname = [hdr.fname '_' mod];
d.fnamedate = [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
if ecg_cleaning
d=call_ecg_cleaning(d,hdr,d.trial{1});
end
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')]);
mod = 'mod-BrainSenseSurveyBip';
writetable(T,fullfile(hdr.fpath,[hdr.fname '_' mod 'PowerSpectra.csv']));
T=array2table(peaks','VariableNames',channels,'RowNames',{'PeakFrequency','PeakPower'});
writetable(T,fullfile(hdr.fpath,[hdr.fname '_' mod '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^2/Hz]')