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Automatic Speech Discrimination Assessment Methods Based on Event-Related Potentials (ERP).

Pimwipa Charuthamrong1, Pasin Israsena2, Solaphat Hemrungrojn3

  • 1Interdisciplinary Program of Biomedical Engineering, Faculty of Engineering, Chulalongkorn University, Pathumwan, Bangkok 10330, Thailand.

Sensors (Basel, Switzerland)
|April 12, 2022
PubMed
Summary

This study introduces an objective, visual electroencephalography (EEG) method for speech discrimination assessment. This approach, using pictures and machine learning, achieved high accuracy, potentially improving accessibility for hearing loss diagnosis.

Keywords:
EEGERPclassifierspeech discrimination

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Area of Science:

  • Neuroscience
  • Audiology
  • Biomedical Engineering

Background:

  • Traditional speech discrimination tests for hearing loss rely on subjective patient responses.
  • Objective assessment methods are needed to improve diagnostic accuracy and accessibility.
  • Electroencephalography (EEG) offers a potential avenue for objective neurophysiological measures.

Purpose of the Study:

  • To develop and evaluate a novel visual-event-related potential (ERP)-based method for objective speech discrimination assessment.
  • To compare the efficacy of the proposed visual-ERP method against existing visual-ERP and auditory mismatch negativity (MMN) based approaches.
  • To explore the potential of machine learning for classifying speech discrimination tasks using EEG features.

Main Methods:

  • A visual-ERP-based method was developed using pictures representing word meanings, implemented with three distinct strategies.
  • Machine learning algorithms, specifically support vector machine (SVM) and common spatial pattern (CSP), were employed for EEG signal classification.
  • Performance was evaluated by comparing classification accuracy with a letter-based visual-ERP method and an auditory MMN-based method.

Main Results:

  • The visual-ERP methods elicited P3 and late positive potential (LPP) components.
  • The auditory MMN-based method showed the MMN component.
  • Two of the three proposed visual-ERP strategies and the MMN-based method achieved approximately 80% classification accuracy using SVM and CSP.

Conclusions:

  • The proposed visual-ERP method demonstrates potential as an objective tool for speech discrimination assessment.
  • High classification accuracy suggests feasibility for pre-screening applications, especially in resource-limited settings.
  • This approach could enhance the accessibility of hearing loss diagnosis and treatment planning.