Electrodermal activity (EDA) is a measure of the sympathetic activity, typically caused by stress or an emotional state. Analysis not a straightforward process like analysing reaction times. It requires some sophisticated algorithms to distinguish tonic activity (Galvanic Skin Level; GSL) and phasic activity (Galvanic Skin Responses; GSRs; see Figure 5). For a more elaborate explanation of EDA and how to analyze it, please see Bouscein (2012).

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BioPac (Braithwaite, 2013) and Movisens provide software toolboxes for analyzing this data, but these are costly packages. Ledalab and PsPM are open-source Matlab toolboxes that are also able to pre-process and quantify GSL and GSRs. Unfortunately, Matlab itself is not open-source and quite expensive. Are there other free solutions that allow you to analyse EDA data, such as Python or R packages?

Bouscein, W., Roth, W. T., Dawson, M. E., & Filion, D. L. (2012). Publication recommendations for electrodermal measurements. Psychophysiology, 49, 1017-1034.

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    $\begingroup$ Don't you guys have any money ;-) I'm unsure where you are at, but if you partner up with a (proper :-) uni, you may get your hands on Matlab for free. Uni Utrecht has an institutional license for example. Pair up, arrange a visiting fellowship or what have you. You need the tools to work. $\endgroup$
    – AliceD
    Commented Oct 6, 2016 at 8:50
  • $\begingroup$ @Christiaan The company did have MatLab and SPSS but had bad experiences, regarding support and licencing etc. They are thus looking for better (and free) alternatives, such as Python or R. I'll definitely give them the suggestion of partnering up though, but I am only an intern (work experience position) for a short period so I need to produce quickly ;) $\endgroup$ Commented Oct 6, 2016 at 9:10
  • $\begingroup$ Check. I understand $\endgroup$
    – AliceD
    Commented Oct 6, 2016 at 9:57
  • $\begingroup$ I had this exact same question as part of my PhD and ended up using a trial of Matlab and Ledalab. In the end I dropped my GSR data from my thesis. :) $\endgroup$
    – Steven Jeuris
    Commented Aug 14, 2017 at 10:44

1 Answer 1


I have found a list of Python and Matlab packages. I'll summarize them over here. As soon as I have gone through the packages, I'll provide some additional details.


  • edaExplorer: Also in Python.*
    EdaExplorer is a tool that is able to detect noisy data from clean data. Five second epochs are made which will be categorized by a model that is the result of a supervised machine learning algorithm (a support vector machine). The data can be marked binary (clean vs noisy) or multiclass (clean, doubtfull or noisy). The noisy data can subsequently be removed. Moreover, edaExplorer is able to find peaks (GSRs) and you can label epochs by walking through the data.


  • *edaExplorer:
  • cvxEDA: Also in Matlab.**
    cvxEDA uses a convex optimization procedure to separate the data into three components: (1) a tonic component, (2) a phasic component and (3) a noise term. The noise term is simply a sequence of zero-averaged Gaussian random variables with variance $\sigma^{2}$. The phasic component is determined by the convolution of sudomotor nerve pulses. The tonic component is all that is left, slow varying changes in conductivity. The function is physiologically plausibly and has shown to outperform the continuous deconvolution analysis (CDA), as implemented in Ledalab.
  • PyPsy 0.1.1 or PyPsy 0.1.5:
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Most packages came from http://affect.media.mit.edu/software.php. The website provides tools for analyzing several other stuff, such as facial recognition. Definitely worth taking a look.


edaExplorer: Taylor, S., Jaques, N., Chen, W., Fedor, S., Sano, A., & Picard, R. Automatic identification of artifacts in electrodermal activity data. In Engineering in Medicine and Biology Conference. 2015.

cvxEDA: A Greco, G Valenza, A Lanata, EP Scilingo, and L Citi. cvxEDA: a Convex Optimization Approach to Electrodermal Activity Processing, IEEE Transactions on Biomedical Engineering, 2015. DOI: 10.1109/TBME.2015.2474131

edaSleep: Akane Sano, Rosalind W. Picard, Toward a Taxonomy of Autonomic Sleep Patterns with Electrodermal Activity, IEEE EMBC 2011, Boston, USA, August 2011


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