Other Sections include
A: A good discussion and differentiation of these signals can be found in Handbook of Psychophysiology. John T. Cacioppo, Gary Berntson, Louis G. Tassinary and Psychophysiology: Human Behavior and Physiological Response by John L. Andreassi.
A: The webinar covers everything from subject, hardware and software setup, to recording great data and analyzing the signals.
A: The presentation provides a good overview of the recommended steps for preparation of data and examples of good vs. bad data. The recorded EDA Analysis and Scripting Webinar shows how these functions can be automated using BIOPAC scripting. These functions will also be addressed in the EDA Analysis Essentials Webinar. Finally, we recommend you consult the EDA resources in our Knowledge Base and Application Notes.
A: The webinar demonstrates two automated techniques for identifying and scoring Skin Conductance Responses. AcqKnowledge includes fully-automated routines for EDA analysis.
A: The presentation shows how to acquire both the Tonic and Phasic signals and walks through a variety of analysis options.
A: The Focus Areas are a great tool for comparing segments of data. I demonstrated the use of the Focus Areas in the webinar and they were also covered in the previous webinar. We also have a screencast that provides additional information. https://www.biopac.com/events/acqknowledge-webinar/ , https://www.youtube.com/watch?v=CBYGYklB9Yo
A: The following link will take you to a webinar that covers analysis and scripting. The BIOPAC basic scripting tool allows you to batch process analysis and automate much of the analysis that were included in this webinar: https://www.biopac.com/events/eda-webinar-analysis-scripting/
A: We refrain from advising on the best measure. We recommend that you consult the reference literature that was included in the presentation as well as the literature on the topic of EDA in general. The analysis routines in AcqKnowledge will provide you with a wide range of options to choose from and all of the frequently used and recommended measures.
A: It draws a line from the first selected sample point to the last selected sample point and interpolates the values on this line to replace the original data.
A: If the signal is clean, it can be used as is. But you can resample to 50 Hz (no less than 50 Hz) in order to speed up the performance of the analysis algorithms.
A: Median smoothing rejects outliers while mean smoothing averages them into the result. It will eliminate rap transient spikes from a slow moving signal such as electrodermal activity. 50 samples were selected because the sample rate was 50 Hz. Usually, a smoothing window equal to the number of samples per second will remove most artifacts while not disturbing the physiological trends in the data. Remember to apply a 1 Hz FIR LP afterwards, to smooth out the result. You can perform testing with these and other settings on clean data and compare to the original to see that the data are not significantly altered by the transformations. We recommend such testing steps before transforming data in general. See also Methods for Computing Phasic EDA.
A: No, median smoothing will not impact the signal if it is applied correctly and it is easy to determine whether you have altered the signal by overlapping the raw and smoothed waveforms. If the filter becomes too aggressive (if you use too many samples for the window), then it will also transform the underlying trends in the signal. For data sampled at N samples per second, a median smoothing filter with window size N will result in a slight reduction of observed P-P changes in responses:
You have to define the limits for an acceptable transformation. In this example, the peak-to-peak measurement after smoothing and low-pass filtering at 1 Hz differs by about 3% from the raw data (0.337 vs 0.347). But a filter this aggressive will eliminate most fast motion artifacts. If this trade-off is acceptable it’s the researcher’s decision and we recommend to always perform testing on both clean and noisy data before choosing a strategy to remove artifacts.
A: Smoothing baseline removal was used during the webinar as a method to obtain the phasic EDA signal, the signal that represents changes in EDA. If you want to compare the participant’s responses during two different blocks of the experiment (such as when sound stimuli are presented vs baseline), please refer to the section of the webinar that covered the block analysis.
A: We would like to refrain from making recommendations on how to run statistical tests.
A: Yes, and this will be covered in the follow-up webinar.
A: Reducing the sample rate lessens the computational load for the analysis.
A: A separate troubleshooting guide is being prepared and it will contain actual data, not just pictures. We should focus on good data—show what the signal should look like and not worry about what bad data looks like. I do not like this approach…there are just too many potential issues.
A: It is best to send over a data sample to firstname.lastname@example.org. We would need to see the data to provide useful advice. Please send the raw .acq file. However, I would start by looking at the electrode to subject connection and make sure that everything is good there. Check the quality of the electrodes and ensure they are making good contact with the subject. When you send a file make sure that you include a full description of your equipment and participant setup including any tasks the subject is performing.
A: The AcqKnowledge cycle detector can take measurements around specific stimulus events, both before the event and after the event, to automate the extraction of measurements. If you use the automated Event-related EDA Analysis option, the software will identify specific and nonspecific skin conductance responses within the recording. You can then use the Find cycle detector to measure SCL and count SCRs during the anticipatory phase of the recording. We have several Find Cycle screencasts that demonstrate how to use the Find Cycle detector. I would also refer to the guidelines to see what is recommended for measurement timing intervals.
A: This will be similar to the Block demonstration I gave in the presentation, but instead of blocks, you can look at responses over defined time intervals – e.g., every 2 minutes. AcqKnowledge has a fully automated Epoch analysis routine that will allow you to automatically measure the data and export the results to Excel. The Epoch Analysis screencast will provide some additional information about this feature.
A: I’m not clear what you mean by EDA variability because there are two primary signals – Tonic and Phasic. The Skin Conductance Level will vary quite a lot among participants because this is the absolute signal, the tonic waveform. However, the Skin Conductance Response analysis determines how responsive a subject is and also measures the size and amplitude of each response. The software measures the SCR amplitude by measuring SCL at the point of SCR onset, and again at the peak of the SCR, and then subtracting the onset value from the peak value to provide the amplitude value. The size of the response is relative to the SCL at the point the response started. If you are concerned about the quality of your data, you can contact our Support Department and they will gladly provide you with some specific feedback.
A: If you are trying to compare data that has been previously published, you can, in most cases, adjust the settings in AcqKnowledge‘s automated analysis routines to match those described in the publication. I would also recommend consulting the list of references that are included in the webinar slide deck to determine current recommendations for EDA analysis. BIOPAC has created an automated solution that conforms to the guidelines but also provides users with flexibility to match other analysis strategies.
A: This example was a part of a biofeedback game that uses EDA and AcqKnowledge‘s Network Data Transfer functionality to provide real-time access to the data for biofeedback. A sphere would materialize after every SCR (skin conductance response). If the goal is for the participant to relax, they try to keep their responses down or else the bar will be tipped over. In another version, the game can be played against a human or computer with the goal of generating more and larger responses than the opponent in order to knock down the bar first. The game adapts the size of the spheres to the max response of the participant so far, thus accounting for individual variability.
A: AcqKnowledge does not currently include an automated deconvolution analysis option, but we are constantly reviewing feature requests. AcqKnowledge can export the data in 3rd-party formats to aid in the process.
A: Electrical noise is 50 Hz or 60 Hz. You can use the Spectrum analyzer palette (Display >Show >Spectrum analyzer palette) in real-time as well as after the recording. A peak will be obvious at 50 Hz/60 Hz (depending on your region). This is an unusual problem to have with the EDA data as it is low-pass filtered at the amplifier. This means the source of noise is not related to the EDA, with the most likely culprit being third party equipment that is connected to our system without optical isolation. Dry electrodes or poor contact between the electrode and the subject will also exasperate this problem.
A: What’s important is to be aware that a SCR reaches the peak over 1-3 seconds while 50% decay may take anywhere from 2-10 seconds. So skin conductance data rises much faster than it drops. For instance, a very fast drop, for example a full 1 µS over less than 2 seconds, is unlikely to be physiological in nature.
A: The median smoothing should be performed first to remove any high frequency artifacts from the signal before low pass filtering it. Low pass filtering will smooth artifacts into the data, so they should be removed first.
A: Scripting is covered in this EDA Analysis & Scripting webinar.
A: It’s best to avoid artifact in the first place by using fresh electrodes and taping over the leads and electrodes. Experiment with placing electrodes in alternate locations that will not be influenced by movement that much but explore the literature beforehand. We have included one such reference in the presentation. If you are already done with the experiment, please refer to the answer of question 11. We are also working on new strategies for mobile applications that will help to prevent or minimize artifacts created during mobile recordings.
A: This is discussed in detail during the follow-up webinar.
A: These are changes in skin conductance that do not make sense given physiological expectations. Typically this means the EDA goes up or down at a very fast rate. Such artifacts are very fast. Here is an example:
Next, we have resampled the data to 50 Hz and applied a 50-sample median smoothing filter (Transform->Smoothing) and then 1 Hz FIR low pass filter (Transform > Digital filters > FIR filter > Low pass). We have zoomed in and the original data is seen on top with the transformed result on the bottom. The artifacts are completely eliminated:
This is even better illustrated by showing the waveform in scope mode, with the green waveform representing the clean result:
This is an example of the most typical type of artifact and method of correction. However, we will release an EDA troubleshooting guide in the future that will include AcqKnowledge sample files of various types of issues so you can learn how to handle the data yourself.
The follow-up webinar will address artifact removal automation techniques.
A: The most common causes of low signal or poor data are poor electrode placement (location and/or adhesion) or the subject banging or knocking the electrodes. The subject banging or knocking the electrodes can also cause artifacts that are clearly not EDA. The presentation, along with BIOPAC Application Notes and Knowledge Base provides references on how to prevent issues from electrode placement. Use of a low-pass filter set to 1 Hz, as noted in the presentation, can help address some of the issues with “fuzzy” or noisy data. Artifact identification and removal (including motion artifacts), will be covered in the EDA Analysis Essentials Webinar and is covered in the recorded EDA Analysis and Scripting Webinar.
A: There are many techniques that can be employed to remove artifacts but we try and avoid spending too much time removing them by placing the electrodes and leads in locations that are less problematic. The tools in the webinar will provide you with some good examples for rapid data cleanup.
A: When we test the EDA signal, we ask the participant to take a sharp, deep breath and to hold it for a second. This process will result in a response from most subjects. However, it is unlikely that a subject will perform this maneuver under normal circumstances. Normal breathing patterns will not result in a response but you can always record the respiration signal and check to see whether participants have irregular breathing patterns that may be influencing the data.
A: There is a lot that can be done to recover the data. You can use the median smoothing, connect endpoints and filtering techniques and/or contact us at email@example.com so we can help with further suggestions. We have seen literally thousands of files with EDA and can usually help to extract some useable data. However, it makes sense for you to consider using alternative electrode locations where participants are less likely to play with the electrodes and leads.