External validation of binary machine learning models for pain intensity perception classification from EEG in healthy individuals
Abstract Discrimination of pain intensity using machine learning (ML) and electroencephalography (EEG) has significant potential for clinical applications, especially in scenarios where self-report is unsuitable. However, existing research is limited due to a lack of external validation (assessing p...
Main Authors: | Tyler Mari, Oda Asgard, Jessica Henderson, Danielle Hewitt, Christopher Brown, Andrej Stancak, Nicholas Fallon |
---|---|
Format: | Article |
Language: | English |
Published: |
Nature Portfolio
2023-01-01
|
Series: | Scientific Reports |
Online Access: | https://doi.org/10.1038/s41598-022-27298-1 |
Similar Items
-
Machine learning and EEG can classify passive viewing of discrete categories of visual stimuli but not the observation of pain
by: Tyler Mari, et al.
Published: (2023-09-01) -
The neural correlates of texture perception: A systematic review and activation likelihood estimation meta‐analysis of functional magnetic resonance imaging studies
by: Jessica Henderson, et al.
Published: (2023-11-01) -
Neural correlates of perceptual texture change during active touch
by: Jessica Henderson, et al.
Published: (2023-06-01) -
EEG Signal Classification: Introduction to the Problem
by: A. Stancak, et al.
Published: (2003-09-01) -
Binary answering machine using EEG brain signals
by: Chen, Wei Si.
Published: (2010)