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Multimodal Integration of EEG and Near-Infrared Spectroscopy for Robust Cross-Frequency Coupling Estimation.

Nicolás J Gallego-Molina1,2, Andrés Ortiz1,2, Francisco J Martínez-Murcia2,3,4

  • 1Department of Communications Engineering, Escuela Técnica Superior Ingeniería de Telecomunicación, University of Malaga Campus de Teatinos s/n, Málaga 29071, Spain.

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Summary

This study introduces a new way to combine EEG and fNIRS brain imaging for analyzing language processing in children. The method successfully differentiates between skilled and dyslexic readers, offering insights into neurological differences.

Keywords:
Integrated EEG-fNIRS analysisSHAPcross-frequency couplingfunctional brain patterns

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Area of Science:

  • Neuroscience
  • Cognitive Science
  • Biomedical Engineering

Background:

  • Multimodal neuroimaging enhances understanding of brain activity by integrating data from different techniques.
  • Electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) offer complementary insights into neural and hemodynamic responses.
  • Understanding language processing in dyslexia is crucial for early diagnosis and intervention.

Purpose of the Study:

  • To develop and validate a novel multimodal neuroimaging method combining EEG and fNIRS.
  • To investigate brain activity during low-level language processing in seven-year-old skilled and dyslexic readers.
  • To enhance the explainability of neuroimaging-based classification models.

Main Methods:

  • EEG signals were transformed into image sequences using cross-frequency coupling (CFC) to capture neural interactions across frequency bands.
  • fNIRS data provided activation masks reflecting local functional brain activity.
  • A combined EEG-fNIRS approach was used to analyze spatial and temporal brain dynamics.
  • SHAP values were visualized using brainSHAP maps for model interpretability.

Main Results:

  • The proposed method successfully differentiated between control and dyslexic subjects with an Area Under the Curve (AUC) of 77.1%.
  • The integrated approach preserved crucial spatial and temporal information regarding neural communication.
  • Cross-frequency coupling in EEG, combined with fNIRS activation, provided discriminative features for classification.

Conclusions:

  • The novel multimodal EEG-fNIRS approach offers a powerful tool for studying brain function in developmental disorders like dyslexia.
  • This method provides a more comprehensive understanding of neural processes underlying language.
  • Improved explainability through brainSHAP maps facilitates clinical interpretation and trust in AI-driven diagnostic tools.