Aims
The aim of Ledalab is to provide a decomposition of skin
conductance (SC) data into its tonic and phasic components. The decomposition
results in the extraction of unsuperposed response components and thus allows
for an unbisaed quantification of SCR characteristics (e.g., SCR amplitude).
Both methods assume that there is a certain intraindividual stability of the general shape of the SCR.
A physiologial model of twocompartment sweat diffusion suggests that the Bateman function (biexponetial function)
may be adequate for describing this general response shape. The shape parameters of this response are estimated interindividually.
The current version of Ledalab (V3.x) features two EDA analysis strategies:
(1) Continuous Decomposition Analysis (CDA):
This method extracts the phasic (driver) information underlying EDA, and aims at retrieving the signal characteristics of the
underlying sudomotor nerve activity (SNA). SC data is deconvolved by the general response shape which results in a large increase of temporal precision.
Then data is decomposed into continuous phasic and tonic components (Benedek & Kaernbach, 2010b).
It is the method we generally recommend for the analysis of skin conductance data. It features the computation of several standard measures of phasic EDA.
Moreover, straightforward measures such as the average (or integrated) phasic driver activity are provided.
(2) Discrete Decomposition Analysis (DDA):
This method decomposses SC data into distinct phasic components and a tonic component by means of Nonnegative Deconvolution.
The method captures and explores all intraindividual deviations of the general response shape and computes a detailed full model
of all components in the entire data set (Benedek & Kaernbach, 2010a). This method is especially suited for studies of physiological models of the SCR but may be slow for large data.
Standard phasic and tonic EDA measures are computed as well.
Restrictions and recommendations
Ledalab requires Matlab. It is currently tested for Matlab version R2011b but should also work for earlier versions.
The decomposition analysis relies on raw, unfiltered, DC recorded SC data.
The analysis (especially DDA) is time consuming and computation time (above all) corresponds
to the total number of samples of the data. You can speed up the analysis by downsampling your data (samplingrates of about 10 Hz appear sufficient)
or cutting the data and keeping only relevant periods (see preprocessing functions). Moreover, you can use the batchmode to automatically
analyze all data files within a specified directory.
For the employment of DDA, artifacts have to be removed before analysis (see preprocessing functions).
Installation
Installation is easy. Simply extract the provided zipfile to any directory.
Open Matlab and move to the Ledalab directory, where Ledalab.m should be located. Start Ledalab by typing Ledalab in the command window.
All relevant directories will be added to the Matlab path automatically.
Opening/Importing/Saving data
Ledalab imports various data formats. It imports native file format of Varioport, Cassy Lab, and BioPac.
Ledalab also imports Ascii export formats of BioTrace, Vitaport, PortiLab, PsychLab, and the Matlab export of BioPac or Vision Analyzer.
Finally, Ledalab also imports text and Matlab data files which are organized in specific formats. Here you find an example
text file (Type 1) and an example Matlab file,
which illustrate the required data organization in these formats.
The Ledalab import functionality shall be continuously extended. If you have Matlabfunctions at hand which import file formats currently not included in Ledalab, your are invited to share them.
These functions may then be added to the Ledalab import functionality and will become accessable via the Ledalab menu in upcoming versions.
For BioPac, you can use either a single event/markerchannel which features different markertypes, or you can use multiple event/markerchannels
each representing one eventtype. The channels can be selected automatically (if labelled GSR/EDA/SC or EVENT) or manually.
Preprocessing
Preprocessing functions involve lowpass filtering, downsampling, cutting, smoothing, and artifactcorrection.
Decomposition Analysis
To analyze the data select Analysis – Continuous Decomposition Analysis or – Discrete Decomposition Analysis.
It is recommended to optimize the analysis by clicking
the Optimize button. You are also given the opportunity to manually optimize
the analysis by altering the parameters in the decomposition window
and clicking analyze again. But standard settings usually are ok. Click on Apply to accept the analysis
and have the decomposition plotted.
Continuous Decomposition Analysis decomposes
SC data into continuous tonic and phasic activity. This method
is based on Standard Deconvolution, is comparatively fast and quite
robust (artifacts).
Discrete Decomposition Analysis decomposes the SC data
into a tonic component and discrete phasic components. The latter are
further segregated into diffusion components and optional overshoot
(or PO) components.
Results
(1) Eventrelated Analysis:
Select Results  Export eventrelated activation.
You can define a response window (e.g., 1 to 4 seconds after the
event) and a minimum amplitude threshold (e.g., 0.01 muS). All SCRs meeting
these criteria are considered when calculating eventrelated phasic
parameters for each single event.
This analysis can be exported to Matlab, Text, or Excel.
Description of eventrelated variables:
Variable
(Labels used in exported files) 
Description 
Event data 

Event.nr 
Sequence number of event/marker 
Event.nid 
Numerical ID of event 
event.name 
Optional name or decription of event 
Event.ud 
Optional userdata associated with event 
Continuous Decomposition Analysis (CDA)
(Extraction of Continuous Phasic/Tonic Activity based on Standard Deconvolution) 
CDA.nSCR 
Number of significant (= abovethreshold) SCRs within response window (wrw) 
CDA.Latency 
Response latency of first significant SCR wrw [s] 
CDA.AmpSum 
Sum of SCRamplitudes of significant SCRs wrw (reconvolved from corresponding phasic driverpeaks) [muS] 
CDA.SCR 
Average phasic driver wrw. This score represents phasic activity wrw most accurately, but does not fall back on classic SCR amplitudes [muS] 
CDA.ISCR 
Area (i.e. time integral) of phasic driver wrw. It equals SCR multiplied by size of response window [muS*s] 
CDA.PhasicMax 
Maximum value of phasic activity wrw [muS] 
CDA.Tonic 
Mean tonic activity wrw (of decomposed tonic component) 
Discrete Decomposition Analysis (DDA)
(Extraction of Discrete Phasic/Tonic Components based on Nonnegative Deconvolution) 
DDA.nSCR 
Number of significant (= abovethreshold) SCRs within response window (wrw) 
DDA.Latency 
Response latency of first significant SCR wrw [s] 
DDA.AmpSum 
Sum of SCRamplitudes of significant SCRs wrw (recomposed from corresponding phasic driverpeaks/overshoot) [muS] 
DDA.AreaSum 
Sum of SCRarea of significant SCRs wrw (recomposed from corresponding phasic driverpeaks/overshoot) [muS] 
DDATonic 
Mean tonic activity wrw (of decomposed tonic component) 
Standard troughtopeak (TTP) or minmax analysis 
TTP.nSCR 
Number of significant (= abovethreshold) SCRs within response window (wrw) 
TTP.AmpSum 
Sum of SCRamplitudes of significant SCRs wrw [muS] 
TTP.Latency 
Response latency of first significant SCR wrw [s] 
Global Measures 

Global.Mean 
Mean SC value within response window (wrw) 
Global.MaxDeflection 
Maximum positive deflection wrw 
Mind: The eventrelated analysis will either include CDA or DDA measures (dependent on your analysis);
but in any case classic TTP and Global measures are included so that they can be compared with CDA/DDA measures
(2) Export SCRList:
Select Results  Export SCRList.
in order to export all SCRonsets and amplitudes of abovethreshold SCRs
The results can be exported to Matlab, Text, or Excel. Export to mat or xls is recommended as it includes results of CDA (DDA) and TTP.
Batchmode
The batchmode is intended to analyze larger amounts
of data automatically. It will simply be run from the Matlab command
window. The basic idea is to apply Ledalab to a directory of data files
and just handing over some settings. All data files in directory will
then be processed one after the other, which can be run unattendedly.
Finally, the batchmode also generates a protocol.mat containing the main settings and outcomes of the batch analysis.
In the batchmode you just have to define a target directory;
further settings are optional. All settings that can not be specified
explicitly will be set the way they were set the last time when using
Ledalab (saved in personal settings).
After processing all files will be saved (i.e., overwritten) to the
target directory. So you may want to have a copy of you raw data before
analyzing them in batchmode.
Overview of settings and respective options:
setting 
options 
description 
open 
biotrace, biopac, biopacmat, cassylab, leda (default), varioport,
visionanalyzer, vitaport, portilab, psychlab, mat, text (Type 1), text2 (Type 2) 
Define what file type to open 
filter 
[filter_order, lowercutoff frequency]; default = no filter applied 
Apply lowpass Butterworth filter with given settings, e.g. [1 5] for a 1rst order lowpass filter with 5Hz cutoff 
downsample 
0 (default) , 2+ 
Downsampling of data by a given factor 
smooth 
{type, width}
type: mean (moving average), hann (hanning window), gauss (gauss window), adapt (adaptive smoothing using gauss); width: width of smoothing window in samples (does not apply for adapt) 
Perform data smoothing 
analyze 
'CDA' (Discrete Decomposition Analysis), 'DDA' (Continuous Decomposition Analysis) 
Type of Decomposition Analysis 
optimize 
2 (default), 1  6 
Number of different sets of initial values to be considered in optimization 
export_era 
[respwin_start respwin_end amp_threshold (filetype)]
filetype: 1 (Matlab file = default), 2 (Text file), 3 (Excel file)

Export eventrelated activation
E.g., [1 4 .01 1] will use a responsewindow of
1 to 4 sec after the event, an minimum amplitude threshold criterion of
0.01 muS and export the results to a Matlab file 
export_scrlist 
[amp_threshold (filetype)]
filetype: 1 (Matlab file = default), 2 (Text file), 3 (Excel file)

Export eventrelated activation
E.g., [.05 1] will export a list of detected SCRs using a minimum amplitude criterion of
0.05 muS to a Matlab file. 
overview 
0 (default), 1 
Save decomposition result to jpg for easy control
(boolean option) 
Example 1:
Ledalab('P:\EDA_Data\', 'open', 'biotrace', 'downsample', 2, 'analyze','CDA', 'optimize',4, 'export_era', [1 4 .01 3])
... imports all Biotrace files from directory
P:\EDA_Data\, downsamples them by factor 2 (e.g. from 32 Hz to 16
Hz), analyzes them by means of CDA (performing optimization of four initial values),
exports the eventrelated data (for a response window of 1 to 4 sec after each marker, with an SCR amplitude threshold of .01 muS) to Excelresult files
and saves them to Ledalab files (including data and analysis).
Example 2:
Ledalab('P:\EDA_Data\', 'open','leda', 'smooth',{'gauss',16}, 'analyze','DDA', 'optimize',3, 'overview',1)
... opens all Ledalab files from directory P:\EDA_DATA\, performes data smoothing using the gaussmethod and a window width of 16 samples
analyzes them by means of DDA (performing optimization of 3 different initial values),
saves the Ledalab files (now including analysis), and saves an analysis overview to an image file.
Accessing analysis variables