Statistical model improves analysis of skin conductance

Statistical model improves analysis of skin conductance
With electrodes strapped to two fingers, researchers can read out a signal of electrodermal activity. Credit: Sandya Subramanian/MIT Picower Institute

Electrodermal activity—the sweat-induced fluctuations of skin conductance made famous in TV dramatizations of lie-detector tests—can be a truly strong indicator of subconscious, or “sympathetic,” nervous system activity for all kinds of purposes, but only if it is analyzed optimally. In a new study in the Proceedings of the National Academy of Sciences, an MIT-based team of scientists provides a new, fast and accurate statistical model for analyzing EDA.

“Only so much of EDA is intuitive just by looking at the signal,” said Sandya Subramanian, a graduate student in the Harvard-MIT Health Sciences and Technology program and the study’s lead author. Meanwhile, existing mathematical methods of analysis either compute averages of the signal that obscure its instantaneous nature, or inefficiently force measurements into a fit with signal processing models that

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