Machine Learning models for the Identification of Cognitive Tasks using Autonomic Reactions from Heart Rate Variability and Electrodermal Activity
Indices of heart rate variability (HRV) and electrodermal activity (EDA), in conjunction with machine learning models, were used to identify one of three tasks a subject is performing based on autonomic response elicited by the specific task. Using non-invasive measures to identify the task performe...
Main Authors: | Hugo F. Posada-Quintero, Jeffrey B. Bolkhovsky |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2019-04-01
|
Series: | Behavioral Sciences |
Subjects: | |
Online Access: | https://www.mdpi.com/2076-328X/9/4/45 |
Similar Items
-
Analysis of Reproducibility of Noninvasive Measures of Sympathetic Autonomic Control Based on Electrodermal Activity and Heart Rate Variability
by: Hugo F. Posada-Quintero, et al.
Published: (2019-01-01) -
Sleep Deprivation in Young and Healthy Subjects Is More Sensitively Identified by Higher Frequencies of Electrodermal Activity than by Skin Conductance Level Evaluated in the Time Domain
by: Hugo F. Posada-Quintero, et al.
Published: (2017-06-01) -
Human Performance Deterioration Due to Prolonged Wakefulness Can Be Accurately Detected Using Time-Varying Spectral Analysis of Electrodermal Activity
by: Bolkhovsky, J.B, et al.
Published: (2018) -
Electrodermal Activity Is Sensitive to Cognitive Stress under Water
by: Hugo F. Posada-Quintero, et al.
Published: (2018-01-01) -
Wrist-Worn Electrodermal Activity as a Novel Neurophysiological Biomarker of Autonomic Symptoms in Spatial Disorientation
by: Atsushi Tamura, et al.
Published: (2018-12-01)