The biosignalsplux EDA sensor is capable of accurately measuring the electrical properties of the skin which changes. These changes are caused by alterations in sweat secretion and sweat gland activity as a result of changing sympathetic nervous system activity. The low-noise signal conditioning and amplification circuit design provide optimal performance in the detection of even the most feeble electrodermal skin response events.
|Input Bias Current||70pA; DC|
|Connector Type||UC-E6 (Male)|
|Cable Length||100cm±0.5cm (customizable)|
This sensor is not a standalone use sensor and requires the use of a biosignalsplux hub and electrodes in order to acquire any data. It can be included in the following biosignalsplux kits which come with all the needed hardware and accessories for EDA data acquisition:
This add-on is designed to process EDA signals acquired using the biosignalsplux EDA sensor (also commonly known as Galvanic Skin Response (GSR) sensor) to compute statistical temporal parameters and signal power estimations in the frequency domain from event-related phasic features. Electrodermal responses are phasic changes of the electrical properties of the skin (e.g. conductance) caused by alterations in the sweat secretion and sweat gland activity as the result of changing sympathetic nervous system activation.Find Out More
J. Pinto, A. Fred, H. Silva, Biosignal-Based Multimodal Emotion Recognition in a Valence-Arousal Affective Framework Applied to Immersive Video Visualization, in 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3577-3583, 2019
Y. Suko, K. Saito, N. Takayama, S. Warisawa, T. Sakum, Effect of Faint Road Traffic Noise Mixed in Birdsong on the Perceived Restorativeness and Listeners’ Physiological Response: An Exploratory Study, in Int. Journal of Environmental Research and Public Health, vol. 16, no. 24, 2019
L. Shu, J. Xie, M. Yang, Z. Li, D. Liao, X. Xu, X. Yang, A Review of Emotion Recognition Using Physiological Sensors, in Sensors, vol. 18, no. 2074, pp. 1-41, 2018
J. Pagán, J. Risco-Martín, J. Moya, J. Ayala, Modeling methodology for the accurate and prompt prediction of symptomatic events in chronic diseases., in Journal of Biomedical Informatics, vol. 62, pp. 136-147, 2016
S. Quakinin, S. Eusebio, M. Torrado, H. Silva, I. Nabais, G. Gonçalves, L. Bacelar-Nicolau, Stress reactivity, distress and attachment in newly diagnosed breast cancer patients, in Health Psychology and Behavioral Medicine, vol. 3, no. 1, pp. 424-438, 2015
Visit the biosignalsplux publications page for a full list of available publications.