AcqKnowledge offers two methods for computing phasic skin conductance from tonic. Both methods are designed to isolate relatively quick changes in the signal. In one method, the raw skin conductance data are passed through a high pass filter with a cutoff frequency of 0.05 Hz. In the other method, the data are passed through a median value smoothing filter, and then that filtered waveform is subtracted from the original. Since median value smoothing discards areas of rapid change, subtracting this smoothed waveform leaves behind only those sections where the data are changing rapidly.
The median value smoothing filter is computationally intensive. Processing time required by this filter depends on the smoothing factor. Lower values of this factor entail more computations and thus take longer to complete. When the value is too small, the software may seem to hang indefinitely. Therefore, from a computational standpoint, it is better to use a relatively large smoothing factor if this method is to be used for deriving phasic skin conductance. In some older versions of AcqKnowledge, the default smoothing factor was 0.25 seconds. A factor of four seconds or eight seconds is probably more appropriate and will produce results much more quickly.
The alternative method, high pass filtering the data, is generally much faster than median value smoothing. In versions of AcqKnowledge earlier than 4.2, however, this method introduced an artifact that makes the first 30 seconds or so of the data unusable. To apply the same filter without the artifact in AcqKnowledge 4.0 or 4.1, the phasic skin conductance can be derived this way:
In AcqKnowledge version 4.2 and above, these steps are built into the “Derive Phasic EDA from Tonic” analysis routine when the high pass filter option is selected. Hence in these versions, the 0.05 Hz filter does not introduce an artifact.
Options for deriving phasic skin conductance—which of the two methods to use, and what smoothing factor (Baseline estimation window) to use in the case that “Smoothing Baseline Removal” is chosen—are accessible via “Analysis > Electrodermal Activity > Preferences…”
Last Modified 24Mar2015
Many studies use hand dynamometry to objectively quantify exerted effort during experiments most commonly related to the study of motivation.
We’ll focus on this topic and go over everything you need to know to record dynamometry data in the MRI or in the lab. Topics include
– Calibrating for maximum voluntary contraction (MVC)
– Real-time access to the dynamometer signal by third-party applications
– How researchers have used this equipment
– Creating a visual task that gives feedback on exerted effort as well as rewards to the participant
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