Automatic identification of artifacts in Electrodermal Activity data

S. Taylor*, Natasha Jaques*, W. Chen, S. Fedor, A. Sano, R. Picard
2015
PDF Cite Code EDA Explorer tool Artifact detection tutorial SCR detection tutorial Research which uses EDA Explorer

Abstract

Recently, wearable devices have allowed for long term, ambulatory measurement of electrodermal activity (EDA). Despite the fact that ambulatory recording can be noisy, and recording artifacts can easily be mistaken for a physiological response during analysis, to date there is no automatic method for detecting artifacts. This paper describes the development of a machine learning algorithm for automatically detecting EDA artifacts, and provides an empirical evaluation of classification performance. We have encoded our results into a freely available web-based tool for artifact and peak detection.

Type
Conference paper
Publication
In International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
Affective Computing Physiology Sensors Machine Learning Electrodermal Activity
Natasha Jaques
Natasha Jaques

My research is focused on Social Reinforcement Learning–developing algorithms that use insights from social learning to improve AI agents’ learning, generalization, coordination, and human-AI interaction.

Related