TSTagger - Revision history

Chris: /* Use Case: Tagging Time Series Data from External Experiment */

2020-11-20T16:48:51Z

Use Case: Tagging Time Series Data from External Experiment

← Older revision		Revision as of 12:48, 20 November 2020
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	==Use Case: Tagging Time Series Data from External Experiment ==		==Use Case: Tagging Time Series Data from External Experiment ==
	For experiments run elsewhere, we cannot assume a standard format for the experiment meta-data (e.g., timestamps, condition codes, etc.). However, if we have gotten this far in the process, I will assume you've figured out how to create a .par file for the first level BOLD analysis on these data. A MATLAB script called <code>PARParser.m</code> can be found in ubfs/Scripts/Matlab that will translate a series of .par files into a data structure compatible with that generated by PTBParser().~~expinfo=PARParser()~~		For experiments run elsewhere, we cannot assume a standard format for the experiment meta-data (e.g., timestamps, condition codes, etc.). However, if we have gotten this far in the process, I will assume you've figured out how to create a .par file for the first level BOLD analysis on these data. A MATLAB script called <code>PARParser.m</code> can be found in ubfs/Scripts/Matlab that will translate a series of .par files into a data structure compatible with that generated by PTBParser().

	%The following lines tagged 20 runs of data from the Horikawa experiment using 20 .par files (001.par to 020.par)		%The following lines tagged 20 runs of data from the Horikawa experiment using 20 .par files (001.par to 020.par)
	expinfo=PARParser('subject', 'FS_01');		expinfo=PARParser('subject', 'FS_01'); %if no subject is given, default name is "no_name"
	[M, hemis]=loadFSTS		[M, hemis]=loadFSTS
	%'''Important:''' steps omitted detailing the transformation of the raw timeseries matrices M into SCALED		%'''Important:''' steps omitted detailing the transformation of the raw timeseries matrices M into SCALED
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	TR=3; %horikawa TR was 3 seconds; this can be found by examining the .nii header		TR=3; %horikawa TR was 3 seconds; this can be found by examining the .nii header
	condition=[1,2]; %horikawa par files were configured for living vs. nonliving		condition=[1,2]; %horikawa par files were configured for living vs. nonliving
	TSTagger('tr', TR, 'condition', condition, 'dat', DAT, 'duration', 15)		duration=15; %each tagged time window is 15 seconds long; corresponds to horikawa imagery block duration
			%default duration is 5 seconds
			TSTagger('tr', TR, 'condition', condition, 'dat', DAT, 'duration', duration)
			%you will now have a series of ''nruns'' files labeled FS_01_nnn.csv

Chris: /* Use Case: Tagging Time Series Data from CCN Lab Experiments */

2020-11-20T16:44:28Z

Use Case: Tagging Time Series Data from CCN Lab Experiments

@@ Line 48: / Line 48: @@
   %use PTBParser to extract the runtime data from the .mat files for the participant
    eio=PTBParser();
   %Use loadFSTS() to import associated time series from *.wav.txt files
   M=loadFSTS(); %No parameters given ... see the wiki entry for '''[[Importing_Time_Series_Into_MATLAB]]'''
@@ Line 65: / Line 65: @@
    TSTagger('tr', TR, 'volumes_dropped', DROPVOLS, 'condition', ...
         hifam, 'dat', DAT);

Chris at 16:12, 20 November 2020

2020-11-20T16:12:11Z

← Older revision		Revision as of 12:12, 20 November 2020
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	median)		median)

	==~~Sample Usage~~==		==Use Case: Tagging Time Series Data from CCN Lab Experiments==
	%use PTBParser to extract the runtime data from the .mat files for the participant		%CCN Lab experiments save runtime data in a series of .mat files (1 for each run)
			%use PTBParser to extract the runtime data from the .mat files for the participant
	eio=PTBParser();		eio=PTBParser();

			%Use loadFSTS() to import associated time series from *.wav.txt files
			M=loadFSTS(); %No parameters given ... see the wiki entry for '''[[Importing_Time_Series_Into_MATLAB]]'''

			%'''Important:''' steps omitted detailing the transformation of the raw timeseries matrices M into SCALED

	nruns=6		nruns=6
	for i=1:nruns		for i=1:nruns

Chris at 18:56, 18 November 2020

2020-11-18T18:56:31Z

← Older revision		Revision as of 14:56, 18 November 2020
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	'''Optional arguments:'''		'''Optional arguments:'''
	duration: a scalar indicating the number of ~~volumes for~~ each event		duration: a scalar indicating the number of seconds of data to include
	(default: 1)		in each event pattern (default: 5)

	volumes_dropped: number of INITIAL volumes dropped (i.e., volumes from		volumes_dropped: number of INITIAL volumes dropped (i.e., volumes from

Chris at 18:03, 18 November 2020

2020-11-18T18:03:33Z

← Older revision		Revision as of 14:03, 18 November 2020
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	tr: scan interval (E.g. 2.047)		tr: scan interval (E.g. 2.047)

	dat: a cell array of structs: {dat.expinfo, dat.mat}, where expinfo comes		dat: a cell array of structs: {dat.expinfo, dat.mat}, where expinfo comes
	from the PsychToolBox runtime .mat files, and mat is a (usually) normalized		from the PsychToolBox runtime .mat files, and mat is a (usually) normalized

Chris at 18:03, 18 November 2020

2020-11-18T18:03:17Z

← Older revision		Revision as of 14:03, 18 November 2020
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	condition column		condition column

	tr: scan interval (E.g. 2.047)		tr: scan interval (E.g. 2.047)

	dat: a cell array of structs: {dat.expinfo, dat.mat}, where expinfo comes		dat: a cell array of structs: {dat.expinfo, dat.mat}, where expinfo comes
	from the PsychToolBox runtime .mat files, and mat is a (usually) normalized		from the PsychToolBox runtime .mat files, and mat is a (usually) normalized

Chris at 18:00, 18 November 2020

2020-11-18T18:00:15Z

← Older revision		Revision as of 14:00, 18 November 2020
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	of numbers. If a single value is provided, it will be applied to		of numbers. If a single value is provided, it will be applied to
	all paired data files, otherwise the values will be applied to the		all paired data files, otherwise the values will be applied to the
	corresponding dat argument cell. '''Important: Do not use this parameter		corresponding dat argument cell. '''Important: Do not use this parameter'''
	if your event timestamps have already been adjusted for the volumes dropped'''		'''if your event timestamps have already been adjusted for the volumes dropped'''

	precision: input values will be rounded to PRECISION decimal places		precision: input values will be rounded to PRECISION decimal places

Chris at 17:59, 18 November 2020

2020-11-18T17:59:38Z

← Older revision		Revision as of 13:59, 18 November 2020
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	of numbers. If a single value is provided, it will be applied to		of numbers. If a single value is provided, it will be applied to
	all paired data files, otherwise the values will be applied to the		all paired data files, otherwise the values will be applied to the
	corresponding dat argument cell		corresponding dat argument cell. '''Important: Do not use this parameter
			if your event timestamps have already been adjusted for the volumes dropped'''

	precision: input values will be rounded to PRECISION decimal places		precision: input values will be rounded to PRECISION decimal places

Chris: /* Sample Usage */

2019-06-28T15:22:43Z

Sample Usage

← Older revision		Revision as of 11:22, 28 June 2019
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	==Sample Usage==		==Sample Usage==
			%use PTBParser to extract the runtime data from the .mat files for the participant
			eio=PTBParser();

	nruns=6		nruns=6
	for i=1:nruns		for i=1:nruns

Chris at 15:16, 28 June 2019

2019-06-28T15:16:08Z

← Older revision		Revision as of 11:16, 28 June 2019
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	2, meaning that each jittered value is weighted 2:1 in favour of the		2, meaning that each jittered value is weighted 2:1 in favour of the
	median)		median)

			==Sample Usage==
			nruns=6
			for i=1:nruns
			DAT{i}.expinfo=eio{i};
			DAT{i}.mat=SCALED{i};
			end
			hifam=[11 21 31]; %Condition codes for hi familiar Semcat experiment
			lofam=[12 22 32];
			TR=2.047; %TR in the semcat experiment
			DROPVOLS=4; %We dropped the first 4 volumes when processing the data
			TSTagger('tr', TR, 'volumes_dropped', DROPVOLS, 'condition', ...
			hifam, 'dat', DAT);