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Updated: Jun 13, 2025

Electroencephalography Network Indices as Biomarkers of Upper Limb Impairment in Chronic Stroke
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Noninvasive brain stimulation during EEG improves machine learning classification in chronic stroke.

Rishishankar E Suresh1, M S Zobaer1, Matthew J Triano1

  • 1Medical University of South Carolina.

Research Square
|September 16, 2024
PubMed
Summary

Machine learning accurately classifies movement states in stroke patients undergoing noninvasive brain stimulation (NIBS). Transcranial direct current stimulation (tDCS) enhances classification accuracy, aiding motor recovery.

Keywords:
chronic strokeelectroencephalogrammachine learningnoninvasive brain stimulationtranscranial direct current stimulation

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Area of Science:

  • Neuroscience
  • Rehabilitation Medicine
  • Machine Learning

Background:

  • Noninvasive brain stimulation (NIBS) aids motor recovery in chronic stroke patients with hemiparesis.
  • Identifying optimal stimulation periods requires auto-detection of movement states.
  • Transcranial direct current stimulation (tDCS) may modulate brain activity during movement.

Purpose of the Study:

  • To compare machine learning models for classifying movement states during EEG recordings in hemiparetic stroke patients.
  • To evaluate the impact of tDCS on movement state classification accuracy.
  • To determine if tDCS improves classification compared to sham stimulation.

Main Methods:

  • Collected EEG data from 10 chronic stroke and 11 healthy participants performing a motor task during tDCS.
  • Utilized 8 traditional and 5 ensemble machine learning algorithms to classify 'hold' and 'reach' movement states.
  • Applied z-score normalization and power binning into five frequency bands for preprocessing and feature extraction.

Main Results:

  • Classification accuracy was significantly higher in the tDCS group (78.9%) versus sham (55.6%).
  • Stroke patients showed higher classification accuracy (77.6%) than healthy controls (64.1%) during stimulation.
  • Movement state classification was highest during stimulation (75.2%) in stroke patients; Linear Discriminant Analysis, Logistic Regression, and Decision Trees were most accurate.

Conclusions:

  • Machine learning effectively classifies movement states in stroke patients during NIBS.
  • tDCS significantly improves both disease and movement state classification in chronic stroke.
  • Automated movement state detection can optimize therapeutic brain stimulation for motor recovery.