What Does This Channel
Measure?
EDA measures the subject's skin conductance (sweat gland activity,
sometimes called galvanic skin response). anslab identifies
significant changes in skin conductance level defined as drops or rises
greater than .02 microSiemens above a zero slope baseline within a
defined time interval. These are called SCRs (skin conductance
responses) and depending on the experimental context can be specific or
non-specific (without known stimulus). SCRs can be mapped to
experimental events to measure subject reactivity to various
stimuli. Non-specific SCRs can be counted per unit
of time (e.g., 1 min), and are called
non-specific fluctuations (NSFs).
If you're interested in evoked SCRs, you first scan the entire file
for artifacts and correct them, if they are found in a sensitive
position. Then, stimulus-related segments are extracted and amplitude
changes can either be scored trial by trial or averaged for every
sample point across experimental conditions. For nonspecific
fluctuations, the procedure is different: after editing artifacts,
fluctuations are recognized automatically and displayed for you to
check for correctness. Both types of processing are described
separately in the following sections.
1. evoked skin conductance responses
(SCR)
If you are analyzing an event related design, the object of
scl-editing is not to extract certain calculated traces but
to identify and edit artifactual data segments. The 'clean' signal
can then be used for automatic extraction of event related
epochs (trials) as described on the 
segmentation page. Therefore
analyzing involves only 'resuming' the analysis once after
artifact editing in order to save the clean signal. You can load
optional channels (e.g. accelerometer or marker), to give you more
information about data segments you wish to edit.
Artifacts are removed by using the buttons in the editing
section of the command window, especially the connect-function
to
remove and interpolate. The most common artifacts are technical errors
in hookup or data collection. These artifacts will appear as
large spikes or sudden niveau changes in the EDA graph.
Another irregularity that must be noted but cannot be edited is the nonresponder or person with little electrodermal lability. A small percentage of the general population do not respond electrodermally and will produce a near flat line in figure 2 of the editing windows. A flatliner can be recognized by restricted range on the vertical axis and the lack of identified changes >.02 uS. One needs to decide a-priori whether to exclude the data from these analyses (typically data is included).
2. nonspecific fluctuations (NSF)
Extracting nonspecific fluctuation parameters of the skin conductance level also involves a first step for artifact editing.The accelerometer signal is loaded automatically to help identify movement artifacts.
When you're done with this, hitting the resume button will start the
response identifaction algorithm. Before running the fluctuation
detection, anslab filters the edited signal with the given cut-off
frequency in the eda-options, to avoid false positives due to high
frequent noise. This is not done directly after loading the data, as
artefacts than are smoothed out and are nearly impossible to identify.
Detected SCR onsets are then
displayed in the main axis and qualifying onsets (baseline points) are
labelled with small numbered red dots. These dots are only plotted when
zoomed in sufficiently, to avoid long drawing times when browsing large
data files. Two additional axes display bivariate distributions of
SCR pattern characteristics. Outliers typically identify SCRs that are
due to technical or movement artifact. These need to be inspected and
excluded, if necessary. You can automatically jump to a specific
response by clicking on the corresponding number on the two bivariate
plots on the right.
The following variables are extracted from the electrodermal activity channel: SCL - skin conductance level, SRR - SCR rate (in count/min), SRA - SCR amplitude, SRT - SCR rise time & SHR - SCR half-recovery time. You can display them by switching from 'raw' to 'event'-mode:
You can set artifactual intervals to NaN (missing data) by using the
'exclusion box'-button or define a global artifact using the
'define artifact'-button. When you are done, push the 'Resume'-button and
then select "Save reduced data"
if you want to save your changes.
SCR-rise time and SCR-half recovery time of the skin conductance leve; red dots with numbers (16, 17 in the upper case) represent qualifying onsets (baseline points).
SCR option window with standard parameters.
What Kinds of Artifacts are
Common in this Channel?
anslab almost always correctly identifies significant SCRs, so this
channel does not require heavy editing. Anslab will occasionally fail
to identify significant changes in SCL or will misidentify
non-significant changes. Scan the graph and note on the vertical
axis the height of all rises and dips. If you suspect that anslab
has made a mistake, follow the instructions below to add or remove
dots.
How Are Artifacts Removed?
You mainly need to look out for movement artifacts that become apparent
in the bivariate plots, or for instances when the program has missed or
misdetected an SCR. Before every wave (>0.02 uS) in the signal
there should be a red dot marking that the program has detected this
event. If there is none, it might be that the rise was too slowly
or because the amplitude was less than 0.02 uS, or the signal was very
noisy at this point. If you don't agree, you need to push the 'insert'-button on the
command window. Then, you can mark a click on a point where you wish to
insert a SCR. You can deleted events with the 'delete'-button and
clicking near the event you wish to delete. The bivariate plots are
updated to include your changes, allowing you to check your editing
results. You can also use 'delete box'-button, to delete multiple
events at a time.
What Qualities Must Be
Preserved In Editing?
The most important editing goal is to remove obvious technical
artifacts that distort a mean. The second goal is to identify
electrodermally stable individuals so that they can be considered
separately or in some cases excluded from data analysis.