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!
- 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
- eda-explorer : Scripts to detect artifacts in EDA data
- 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
- pyEDA : Open-Source Python Library for Electrodermal Activity (Galvanic Skin Response) Analysis | pdf
- LEDALAB : Open source Matlab software for analysis of skin conductance data (viz. EDA; GSR)
- Ledalab
- 2005-Detecting stress during real-world driving tasks using physiological sensors-2368
- 2010-A continuous measure of phasic electrodermal activity-1582
- 2012-A stress sensor based on Galvanic Skin Response (GSR) controlled by ZigBee-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-14
- 2015-A Guide for Analysing Electrodermal Activity (EDA) & Skin Conductance Responses (SCRs) for Psychological Experiments-891
- 2015-EDA Positive Change: A Simple Algorithm for Electrodermal Activity to Measure General Audience Arousal During Media Exposure-85
- 2016-cvxEDA: a Convex Optimization Approach to Electrodermal Activity Processing-452 | code
- 2015-Automatic Identification of Artifacts in Electrodermal Activity Data-272 | code | code-EDArtifact
- 2017-Feature extraction of galvanic skin responses by nonnegative sparse deconvolution-17 | code
- 2017-Unsupervised Motion Artifact Detection in Wrist-Measured Electrodermal Activity Data-38 | code
- 2017-A wearable system for stress detection through physiological data analysis-27
- 2019-Design and Implementation of an Ultra-Low Resource Electrodermal Activity Sensor for Wearable Applications-23
- 2019-Wearables and location tracking technologies for mental-state sensing in outdoor environments-86
- 2019-A Deep-Learning Model for Subject-Independent Human Emotion Recognition Using Electrodermal Activity Sensors-97
- 2020-Innovations in Electrodermal Activity Data Collection and Signal Processing: A Systematic Review-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-102
- 2020-Electrodermal activity - a beginner’s guide-20
- 2020-Detection of Artifacts in Ambulatory Electrodermal Activity Data-29
- 2021-A Preliminary Study on Automatic Motion Artifact Detection in Electrodermal Activity Data Using Machine Learning-8
- 2021-Correlation Analysis of Different Measurement Places of Galvanic Skin Response in Test Groups Facing Pleasant and Unpleasant Stimuli-19
- 2022-PREDICTING HUMAN STRESS EMOTIONS USING MACHINE LEARNING MODELS | code
- 2022-Automatic motion artifact detection in electrodermal activity data using machine learning-12
- 2023-Human Emotion Recognition Based On Galvanic Skin Response signal Feature Selection and SVM-75
- 2023-Driver Drowsiness Detection: A Machine Learning Approach on Skin Conductance-2
- 2023-Wearable Technologies for Electrodermal and Cardiac Activity Measurements: A Comparison between Fitbit Sense, Empatica E4 and Shimmer GSR3+-3
- 2023-A Method for Stress Detection Using Empatica E4 Bracelet and Machine-Learning Techniques
- Electrodermal Activity(2nd).pdf
- Handbook of Psychophysiology, Fourth Edition
- Galvanic Skin Response Devices The Ultimate Step-By-Step Guide
- All You Need To Know: Galvanic Skin Response (GSR)
- Galvanic Skin Response (GSR): The Complete Pocket Guide
- Tobii Galvanic skin response (GSR)
- Electrodermal Activity Data Collection
- Physiology of Auditory Attention (PhyAAt) : 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
- Electrodermal Activity artifact correction BEnchmark (EDABE)
- Continuously Annotated Signals of Emotion (CASE) | A dataset of continuous affect annotations and physiological signals for emotion analysis-paper
- WESAD (Wearable Stress and Affect Detection) | GSR Analysis for Stress: Development and Validation of an Open Source Tool for Noisy Naturalistic GSR Data-paper
- DEAPdataset : a dataset for emotion analysis using eeg, physiological and video signals | DEAP: A Database for Emotion Analysis using Physiological Signals-paper-3904
- MAHNOB-HCI : A Multimodal Database for Affect Recognition and Implicit Tagging
- Stress Recognition in Automobile Drivers
- The SWELL Knowledge Work Dataset for Stress and User Modeling Research
We welcome your contributions! Please follow these steps to contribute:
- Fork the repo.
- Create a new branch (e.g.,
feature/new-gsr-resource). - Commit your changes to the new branch.
- 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.
This work is licensed under a Creative Commons Attribution 4.0 International License.