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Related Experiment Video

Updated: Jan 17, 2026

Functional Near Infrared Spectroscopy of the Sensory and Motor Brain Regions with Simultaneous Kinematic and EMG Monitoring During Motor Tasks
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Artificial Intelligence-Driven Hemodynamic Monitoring of Simulated Bruxism Using Functional Near-Infrared

Noor Fatima1, Zia Mohy Ud Din1, Abdullah Al Aishan2

  • 1Department of Biomedical Engineering, Air University, Islamabad, Pakistan.

CNS Neuroscience & Therapeutics
|September 22, 2025
PubMed
Summary

This study shows functional Near Infrared Spectroscopy (fNIRS) can detect bruxism (teeth grinding) by monitoring brain activity. The kNN machine learning model achieved 92% accuracy in identifying bruxism-related jaw movements.

Keywords:
bruxismfNIRShemodynamic assessmentmachine learning

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Area of Science:

  • Neuroscience
  • Biomedical Engineering

Background:

  • Bruxism, characterized by involuntary teeth grinding and jaw clenching, can lead to significant dental and temporomandibular joint issues.
  • Current diagnostic methods like polysomnography (PSG) lack detailed spatial mapping of neural activity during rhythmic masticatory muscular activity (RMMA).

Purpose of the Study:

  • To introduce functional Near Infrared Spectroscopy (fNIRS) as a neuroimaging technique for monitoring cortical activity associated with RMMA in bruxism.
  • To differentiate bruxism-induced RMMA from other jaw movements using fNIRS data and machine learning.

Main Methods:

  • fNIRS data was collected from 10 subjects using a 20-channel optode setup over the motor cortex.
  • Feature selection, importance, and reduction techniques were applied to 12 temporal and frequency domain features.
  • Data augmentation techniques (SMOTE, SMOTEN, ADASYN) and machine learning classifiers (kNN, LR, NB, DT, RF) were evaluated.

Main Results:

  • The k-Nearest Neighbors (kNN) classifier demonstrated superior performance in detecting simulated bruxism.
  • An accuracy of 92% was achieved by the kNN model in distinguishing bruxism from other mandible joint movements.

Conclusions:

  • Functional Near Infrared Spectroscopy (fNIRS) shows promise as a non-invasive tool for identifying and differentiating bruxism-related motor activities.
  • These findings support the potential for timely bruxism detection and management through advanced neuroimaging techniques.