Pain phenotypes classified by machine learning using electroencephalography features
Pain is a multidimensional experience mediated by distributed neural networks in the brain. To study this phenomenon, EEGs were collected from 20 subjects with chronic lumbar radiculopathy, 20 age and gender matched healthy subjects, and 17 subjects with chronic lumbar pain scheduled to receive an i...
Main Authors: | Joshua Levitt, Muhammad M. Edhi, Ryan V. Thorpe, Jason W. Leung, Mai Michishita, Suguru Koyama, Satoru Yoshikawa, Keith A. Scarfo, Alexios G. Carayannopoulos, Wendy Gu, Kyle H. Srivastava, Bryan A. Clark, Rosana Esteller, David A. Borton, Stephanie R. Jones, Carl Y. Saab |
---|---|
Format: | Article |
Language: | English |
Published: |
Elsevier
2020-12-01
|
Series: | NeuroImage |
Online Access: | http://www.sciencedirect.com/science/article/pii/S1053811920307424 |
Similar Items
-
Classification of electroencephalography signal using statistical features and regression classifier
by: Sabri, Nurbaity
Published: (2014) -
Classifying Schizotypy Using an Audiovisual Emotion Perception Test and Scalp Electroencephalography
by: Ji Woon Jeong, et al.
Published: (2017-09-01) -
Classifying the Epilepsy Based on the Phase Space Sorted With the Radial Poincaré Sections in Electroencephalography
by: Reyhaneh Zarifiyan Irani Nezhad, et al.
Published: (2021-04-01) -
Concerning electroencephalography
by: Krynauw, R. A. H.
Published: (1939) -
Analyzing and Classifying Neural Dynamics from Intracranial Electroencephalography Signals in Brain-Computer Interface Applications
by: Nagabushan, Naresh
Published: (2019)