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https://github.com/mintisan/awesome-gsr

A comprehensive collection of GSR-related resources, including libraries, datasets, tutorials, papers, and more, for researchers and developers in the Galvanic Skin Response field.
https://github.com/mintisan/awesome-gsr

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A comprehensive collection of GSR-related resources, including libraries, datasets, tutorials, papers, and more, for researchers and developers in the Galvanic Skin Response field.

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README 

        
# Awesome GSR(Galvanic Skin Response)/EDA(Electrodermal Activity)



![Awesome](https://awesome.re/badge.svg) ![GitHub stars](https://img.shields.io/github/stars/mintisan/awesome-gsr.svg?style=social)



A curated list of awesome libraries, datasets, tutorials, papers, and other resources related to Galvanic Skin Response (GSR) analysis. This repository aims to be a comprehensive and organized collection that will help researchers and developers in the world of GSR!



## Table of Contents



- [Standard](#standard)

- [Library](#library)

- [Papers](#papers)

- [Books](#books)

- [Datasets](#datasets)

- [Tutorial](#tutorial)

- [Datasets](#datasets)

- [Contributing](#contributing)



## Standard



## Library



- [BIOBSS](https://github.com/obss/BIOBSS) : A package for processing signals recorded using wearable sensors, such as Electrocardiogram (ECG), Photoplethysmogram (PPG), Electrodermal activity (EDA) and 3-axis acceleration (ACC).

- [NeuroKit2](https://github.com/neuropsychology/NeuroKit)

- [eda-explorer](https://github.com/MITMediaLabAffectiveComputing/eda-explorer/tree/master) : Scripts to detect artifacts in EDA data

- [cvxEDA](https://github.com/lciti/cvxEDA) : Algorithm for the analysis of electrodermal activity (EDA) using convex optimization

- [Data Preprocessing- Sentiment Analysis of Electrodermal Activity Data for Personalized Interventions in Mental Health](https://medium.com/@sarthak.increase/data-preprocessing-sentiment-analysis-of-electrodermal-activity-data-for-personalized-4cdbcb375b94)

- [pyEDA](https://github.com/HealthSciTech/pyEDA) : Open-Source Python Library for Electrodermal Activity (Galvanic Skin Response) Analysis | [pdf](https://pdf.sciencedirectassets.com/280203/1-s2.0-S1877050921X00075/1-s2.0-S1877050921006438/main.pdf?X-Amz-Security-Token=IQoJb3JpZ2luX2VjEBsaCXVzLWVhc3QtMSJHMEUCIGEvAu4IUHnUUXV3R6KrYXZaaCwpRdj%2B0zNBxN%2F53swpAiEA6IDGBHGPFfidArVdWhLuD5dBTAYu8QJW1ogM%2BTgF7aAquwUIhP%2F%2F%2F%2F%2F%2F%2F%2F%2F%2FARAFGgwwNTkwMDM1NDY4NjUiDAaxbKQusBegJzkydiqPBYI1GWqEYi%2BZBpxncVdXVojBYu70CndrhJ5IOPsXfk68d3FxqJZh%2FGuvRywDvSiflHJm1XlyvMtdYK0%2BsS4wU%2Fo0MTvwMCXmSarIsVYNqm6lNuIgC8Lja7UivY3xX6qoHk1M73sHK%2FY3L2d5SZ7RyWGc%2ByHqEYnfU3yn1jQk9OdA1ZYdRk%2Be5W9VabiH6mbZ40SAYxHqtIxYT9ewYNR1uUaT4yX%2F5JAzsAB%2FdKfRCywanWTXpZOBuuZ%2FgMOQcV4sAal90RpGk6HJEbIZiWODdKIRuHRwveffQkjiXBsaZp2eDzgfATPFb3SlzUYvd48fmsaqjXRmIVkJ3UbzN0o2WMooSKofeXs6mlrkoyGLPsHy3R2A3ex2IUXYqys%2Fbq3RVTX1RnLabE4CDzeSDMbJOJlNCEypurYbg34rYuUPkp49gAVT4MU90ujpQVY8WFJlcAdAKw0qwMWzMYqLESWZfFJRVgeO7l%2BYaa1NQgfIY%2FFdCZpY2vSKTtXU7bD9AymjFijXcsDgK8wnZtXAGl1amsk34LSUjULCg6KH9ckug9zFn6L%2F9TXFpGfKqsAPWcuUF7YyTQw07K0y5BRg1S1PYhWr51suWzpKph1z714RcRTmSaFaQiHjdtFGv6nN1HL8TZsDSui3dD4PVRVhKLJGAcl5VA9YXSYzaJNIohN0TApOD%2FeNe5BLdIZFSEnjGf%2B%2FntTUM2%2FPfQZYlZZZ0fS1cG2tm6mJ6cJDojuSmtz4AoBtyYXt%2FqKIWLRBA4s%2B0Z3VVBMG712Hf5vDwUG6ZcmeyPpoBt1P9sTeQ2%2FJ0SDkf%2BQebMdwRDlCb4f7uRW5G3Jowys11fHWSYzYsckvdKDS8gQJqlS%2BnHSvhwiUMrXiPyUwtMO%2FqwY6sQGTDW0y7NHBiJ9U6NlOKDf1d2heE3zyzm08nAV5J37sq0BFSXvxKWan%2FWGkJ7TaBM4eazpshie9%2BRyPUxxSnng2iOTtjVnBbnuqt%2FxZWu9Ro9OG%2FfRq7Itq7vdsi5SCMa9H6dOMN54qSjuIk9NZNCbbtNvwFBXqeeyK%2B5GWsV6tVbGMORbyB29i65gfYpONu7fujFsQmFbNaErkhSSXSsmbQoS%2FdE67iNo113wIHQ85Ox8%3D&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Date=20231206T035457Z&X-Amz-SignedHeaders=host&X-Amz-Expires=300&X-Amz-Credential=ASIAQ3PHCVTY3JLTWFO5%2F20231206%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Signature=aca1b61d8384ee888418c2abcc703c355297165ad926dcdcc5556b14b4729942&hash=547ebeef9deaf044150ed13bc89a8a1a18ed2b75d1c9f3a485fc1e9f288c9f6a&host=68042c943591013ac2b2430a89b270f6af2c76d8dfd086a07176afe7c76c2c61&pii=S1877050921006438&tid=spdf-a81a8431-182b-493f-9dd5-14ecc2f5e213&sid=02333f2d9ccc16405f8a822029972f24677agxrqa&type=client&tsoh=d3d3LnNjaWVuY2VkaXJlY3QuY29t&ua=10165f5c56545a52550202&rr=8311972badcc1a14&cc=jp)

- [LEDALAB](https://nl.mathworks.com/matlabcentral/fileexchange/68502-ledalab-open-source-matlab-software-for-analysis-of-skin-conductance-data-viz-eda-gsr) : Open source Matlab software for analysis of skin conductance data (viz. EDA; GSR)

- [Ledalab](https://github.com/ledalab/ledalab)



### Comparison



## Papers



- 2005-[Detecting stress during real-world driving tasks using physiological sensors](https://ieeexplore.ieee.org/document/1438384)-2368

- 2010-[A continuous measure of phasic electrodermal activity](https://www.sciencedirect.com/science/article/pii/S0165027010002335)-1582

- 2012-[A stress sensor based on Galvanic Skin Response (GSR) controlled by ZigBee](https://pubmed.ncbi.nlm.nih.gov/22778631/)-405

- 2014-[Analysis of the quality of electrodermal activity and heart rate data recorded in daily life over a period of one week with an E4 wristband](https://essay.utwente.nl/70244/1/Enewoldsen_BA_Psychology.pdf)-14

- 2015-[A Guide for Analysing Electrodermal Activity (EDA) & Skin Conductance Responses (SCRs) for Psychological Experiments](https://www.birmingham.ac.uk/documents/college-les/psych/saal/guide-electrodermal-activity.pdf)-891

- 2015-[EDA Positive Change: A Simple Algorithm for Electrodermal Activity to Measure General Audience Arousal During Media Exposure](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2467983)-85

- 2016-[cvxEDA: a Convex Optimization Approach to Electrodermal Activity Processing](https://pubmed.ncbi.nlm.nih.gov/26336110/)-452 | [code](https://github.com/lciti/cvxEDA)

- 2015-[Automatic Identification of Artifacts in Electrodermal Activity Data](https://pubmed.ncbi.nlm.nih.gov/26736662/)-272 | [code](https://github.com/MITMediaLabAffectiveComputing/eda-explorer) | [code-EDArtifact](https://github.com/shkurtagashi/EDArtifact)

- 2017-[Feature extraction of galvanic skin responses by nonnegative sparse deconvolution](https://ieeexplore.ieee.org/document/8168337/)-17 | [code](https://github.com/yskong224/SprasEDA-Python)

- 2017-[Unsupervised Motion Artifact Detection in Wrist-Measured Electrodermal Activity Data](https://arxiv.org/abs/1707.08287)-38 | [code](https://github.com/IdeasLabUT/EDA-Artifact-Detection)

- 2017-[A wearable system for stress detection through physiological data analysis](https://www.iris.sssup.it/retrieve/dd9e0b32-0993-709e-e053-3705fe0a83fd/C029%20-%20A%20wearable%20system%20for%20stress%20detection%20through%20physiological%20data%20analysis.pdf)-27

- 2019-[Design and Implementation of an Ultra-Low Resource Electrodermal Activity Sensor for Wearable Applications](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6603545/)-23

- 2019-[Wearables and location tracking technologies for mental-state sensing in outdoor environments](https://arxiv.org/pdf/1910.06137.pdf)-86

- 2019-[A Deep-Learning Model for Subject-Independent Human Emotion Recognition Using Electrodermal Activity Sensors](https://www.mdpi.com/1424-8220/19/7/1659)-97

- 2020-[Innovations in Electrodermal Activity Data Collection and Signal Processing: A Systematic Review](https://www.mdpi.com/1424-8220/20/2/479)-227

- 2020-[Validating Measures of Electrodermal Activity and Heart Rate Variability Derived From the Empatica E4 Utilized in Research Settings That Involve Interactive Dyadic States](https://www.frontiersin.org/articles/10.3389/fnbeh.2020.00148/full)-102

- 2020-[Electrodermal activity - a beginner’s guide](https://ev.fe.uni-lj.si/4-2020/Gersak.pdf)-20

- 2020-[Detection of Artifacts in Ambulatory Electrodermal Activity Data](https://pc.inf.usi.ch/wp-content/cache/mendeley-file-cache/eb9f5551-d7a7-3069-8140-fa49744b99bc.pdf)-29

- 2021-[A Preliminary Study on Automatic Motion Artifact Detection in Electrodermal Activity Data Using Machine Learning](https://arxiv.org/ftp/arxiv/papers/2107/2107.07650.pdf)-8

- 2021-[Correlation Analysis of Different Measurement Places of Galvanic Skin Response in Test Groups Facing Pleasant and Unpleasant Stimuli](https://www.mdpi.com/1424-8220/21/12/4210)-19

- 2022-[PREDICTING HUMAN STRESS EMOTIONS USING MACHINE LEARNING MODELS](https://www.dropbox.com/s/dp3hm900j2x88j0/full_thesis_with_approvals.pdf?dl=0) | [code](https://github.com/KryeKuzhinieri/predicting-driver-stress-using-deep-learning)

- 2022-[Automatic motion artifact detection in electrodermal activity data using machine learning](https://www.sciencedirect.com/science/article/abs/pii/S1746809422000052)-12

- 2023-[Human Emotion Recognition Based On Galvanic Skin Response signal Feature Selection and SVM](https://arxiv.org/abs/2307.05383)-75

- 2023-[Driver Drowsiness Detection: A Machine Learning Approach on Skin Conductance](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10143251/)-2

- 2023-[Wearable Technologies for Electrodermal and Cardiac Activity Measurements: A Comparison between Fitbit Sense, Empatica E4 and Shimmer GSR3+](https://www.mdpi.com/1424-8220/23/13/5847)-3

- 2023-[A Method for Stress Detection Using Empatica E4 Bracelet and Machine-Learning Techniques](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10098696/)



## Books



- [Electrodermal Activity(2nd).pdf](https://link.springer.com/book/10.1007/978-1-4614-1126-0)

- [Handbook of Psychophysiology, Fourth Edition](https://www.cambridge.org/core/books/abs/handbook-of-psychophysiology/handbook-of-psychophysiology-fourth-edition/1FDD5C9CF84E602AA8346C328DA0CE74)

- [Galvanic Skin Response Devices The Ultimate Step-By-Step Guide](https://www.amazon.com/Galvanic-Response-Devices-Ultimate-Step-ebook/dp/B07HGLSPSH)



## Indices



## Tutorial



- [All You Need To Know: Galvanic Skin Response (GSR)](https://www.futureproofinsights.ie/2021/04/08/all-you-need-to-know-galvanic-skin-response-gsr/)

- [Galvanic Skin Response (GSR): The Complete Pocket Guide](https://imotions.com/blog/learning/research-fundamentals/galvanic-skin-response/)

- [Tobii Galvanic skin response (GSR)](https://connect.tobii.com/s/article/galvanic-skin-response-gsr?language=en_US)

- [Electrodermal Activity Data Collection](https://encyclopedia.pub/entry/277)



## Datasets



- [Physiology of Auditory Attention (PhyAAt)](https://phyaat.github.io/dataset) : The dataset contain three physiological signals recorded at sampling rate of 128Hz from 25 healthy subjects during the experiment. Electroenceplogram (EEG) signal is recorded using a 14-channel Emotiv Epoc device. Two signal streams of Galvanic Skin Response (GSR) were recorded, instantnious sample and moving averaged signal. From photoplethysmogram (PPG) sensor (pulse sensor), a raw signal, inter-beat interval (IBI), and pulse rate were recorded.

- [ECG and GSR Data for Emotion Recognition during Covid-19 Epidemic](https://data.mendeley.com/datasets/g2p7vwxyn2/1)

- [Electrodermal Activity artifact correction BEnchmark (EDABE)](https://data.mendeley.com/datasets/w8fxrg4pv5/2)

- [Continuously Annotated Signals of Emotion (CASE)](https://gitlab.com/karan-shr/case_dataset) | [A dataset of continuous affect annotations and physiological signals for emotion analysis-paper](https://www.nature.com/articles/s41597-019-0209-0)

- [WESAD (Wearable Stress and Affect Detection)](https://archive.ics.uci.edu/dataset/465/wesad+wearable+stress+and+affect+detection) | [GSR Analysis for Stress: Development and Validation of an Open Source Tool for Noisy Naturalistic GSR Data-paper](https://arxiv.org/ftp/arxiv/papers/2005/2005.01834.pdf)

- [DEAPdataset](https://www.eecs.qmul.ac.uk/mmv/datasets/deap/readme.html) : a dataset for emotion analysis using eeg, physiological and video signals | [DEAP: A Database for Emotion Analysis using Physiological Signals-paper](https://www.eecs.qmul.ac.uk/mmv/datasets/deap/doc/tac_special_issue_2011.pdf)-3904

- [MAHNOB-HCI : A Multimodal Database for Affect Recognition and Implicit Tagging](https://ieeexplore.ieee.org/document/5975141)

- [Stress Recognition in Automobile Drivers](https://physionet.org/content/drivedb/1.0.0/)

- [The SWELL Knowledge Work Dataset for Stress and User Modeling Research](http://cs.ru.nl/~skoldijk/SWELL-KW/Dataset.html)



## Contributing



We welcome your contributions! Please follow these steps to contribute:



1. Fork the repo.

2. Create a new branch (e.g., `feature/new-gsr-resource`).

3. Commit your changes to the new branch.

4. Create a Pull Request, and provide a brief description of the changes/additions.



Please make sure that the resources you add are relevant to the field of Heart Rate Variability. Before contributing, take a look at the existing resources to avoid duplicates.



## License



This work is licensed under a [Creative Commons Attribution 4.0 International License](https://creativecommons.org/licenses/by/4.0/).