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Detecting acute pain signals from human EEG.

Guanghao Sun1, Zhenfu Wen1, Deborah Ok2

  • 1Department of Psychiatry, New York University School of Medicine, New York, NY, United States.

Journal of Neuroscience Methods
|October 3, 2020
PubMed
Summary

This study introduces an unsupervised learning method using electroencephalography (EEG) to detect acute pain signals. The approach accurately identifies pain onset, offering potential for improved pain management and neuromodulation.

Keywords:
Acute painEvent-related potentialSource localizationState-space model

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Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Signal Processing

Background:

  • Human neuroimaging advances allow studying brain functional connectivity in pain states.
  • Identifying neural signals for pain timing is crucial for understanding pain dynamics.
  • Understanding pain onset from cortical circuits can inform closed-loop neuromodulation.

Purpose of the Study:

  • To develop an unsupervised learning method for detecting acute pain signals using human EEG.
  • To identify the precise timing of pain onset from distributed cortical circuits.
  • To provide feedback for closed-loop neuromodulation strategies.

Main Methods:

  • Developed an unsupervised learning method for sequential detection of acute pain signals.
  • Utilized multichannel human EEG recordings and EEG source localization.
  • Applied a state-space model (SSM) to detect pain onset in regions of interest (ROIs).

Main Results:

  • Validated the SSM-based detection strategy on two human EEG datasets.
  • Observed varying detection accuracy across subjects and methods.
  • Demonstrated feasibility for cross-subject and cross-modality prediction of pain signals.

Conclusions:

  • The unsupervised SSM-based method with EEG source localization robustly detects acute pain signal onset.
  • This method requires fewer training trials than supervised methods.
  • It shows comparable or improved performance over supervised learning for pain detection.