Breathe Easy EDA: A MATLAB toolbox for psychophysiology data management, cleaning, and analysis.

John C Ksander,Sarah M Kark,Christopher R Madan

doi:10.12688/f1000research.13849.2

John C Ksander, Sarah M Kark + Show 1 more

Open Access

https://doi.org/10.12688/f1000research.13849.2

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Abstract

Electrodermal activity (EDA) recordings are widely used in experimental psychology to measure skin conductance responses (SCRs) that reflect sympathetic nervous system arousal. However, irregular respiration patterns and deep breaths can cause EDA fluctuations that are difficult to distinguish from genuine arousal-related SCRs, presenting a methodological challenge that increases the likelihood of false positives in SCR analyses. Thus, it is crucial to identify respiration-related artifacts in EDA data. Here we developed a novel and freely distributed MATLAB toolbox, Breathe Easy EDA (BEEDA). BEEDA is a flexible toolbox that facilitates EDA visual inspection, allowing users to identify and eliminate respiration artifacts. BEEDA further includes functionality for EDA data analyses (measuring tonic and phasic EDA components) and reliability analyses for artifact identification. The toolbox is suitable for any experiment recording both EDA and respiration data, and flexibly adjusts to experiment-specific parameters (e.g., trial structure and analysis parameters).

Highlights

Electrodermal activity (EDA) methods evaluate fluctuations in skin electrical conductance caused by changes in sweat gland production
We have developed a novel MATLAB toolbox for efficiently eliminating EDA respiration artifacts and analyzing EDA data, which we freely distribute as Breathe Easy EDA or ‘BEEDA’
This presentation simplifies the manual identification of problematic breathing (e.g. Figure 4), and the recommended procedures for EDA respiration artifact scrubbing can be found in Schneider et al (2003)

Summary

22 Feb 2018 report report

Any reports and responses or comments on the article can be found at the end of the article. We would like to thank the reviewers for their time and expertise We have addressed their concerns and changes are noted in the article. The first reviewer noted that our article would benefit from describing the signal processing and analysis calculations in greater detail. The first reviewer noted that hypothesis testing functionality would benefit users, when users are more comfortable with GUI tools. We agree this functionality would be useful, we believe users would benefit most from using statistical software they are already comfortable and familiar with. 32), which mimic SCRs on EDA recordings As mentioned above, this relationship is useful for checking psychophysiological signal integrity, but can bias SCR analyses. A lack of analytical solutions has motivated software development within this field since the early 1990’s, with the goal of improving how researchers inspect and manipulate respiration data (Wilhelm & Roth, 1993)

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