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Recording Human Electrocorticographic (ECoG) Signals for Neuroscientific Research and Real-time Functional Cortical Mapping
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DiagPat: An Explainable Language Detection Model Using EEG Signals.

Tugce Keles1, Kubra Yildirim1, Dahiru Tanko2

  • 1Department of Digital Forensics Engineering, College of Technology, Firat University, 23119 Elazig, Turkey.

Sensors (Basel, Switzerland)
|June 12, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces DiagPat, an explainable feature engineering model for electroencephalography (EEG) language detection. DiagPat accurately classifies languages and tasks from brain activity, offering a lightweight and interpretable solution for EEG-based language identification.

Keywords:
DiagPatEEG language detectioncognitive scienceexplainable feature engineeringneuroscience

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Area of Science:

  • Neuroscience
  • Computational Linguistics
  • Biomedical Engineering

Background:

  • Electroencephalography (EEG) is a non-invasive, cost-effective tool for studying brain activity during language processing.
  • Previous EEG language studies faced limitations: small datasets, focus on native speakers or speech units, limited experimental settings, and reliance on complex, uninterpretable deep learning models.

Purpose of the Study:

  • To develop and validate an explainable feature engineering (XFE) model for accurate EEG-based language detection.
  • To address limitations of prior studies by using a larger, diverse dataset and focusing on direct language and task mode classification.
  • To create a computationally efficient and interpretable framework for analyzing brain activity related to language.

Main Methods:

  • Curated a new EEG dataset from 346 participants (Arabic and Turkish) in reading and listening modes, yielding 6364 EEG segments.
  • Proposed DiagPat, an XFE model using diagonal pattern analysis for feature extraction from EEG channels and signals.
  • Integrated DiagPat with iterative neighborhood component analysis (INCA) for feature selection and a k-nearest neighbors (tkNN) classifier for prediction, utilizing Directed Lobish (DLob) for explainability.

Main Results:

  • Achieved >90% accuracy across nine classification cases (language, mode, mixed), with accuracies ranging from 92.14% to 99.35% (10-fold CV).
  • Generated case-specific cortical connectome diagrams for interpretable characterization of language- and mode-related brain activity.
  • Reported subject-independent accuracies (LOSO CV) ranging from 29.75% to 83.50%, demonstrating generalization capabilities.

Conclusions:

  • DiagPat offers an accurate, lightweight, and explainable framework for EEG-based language detection and task mode classification.
  • The model's explainability through cortical connectome diagrams aids in understanding language-related brain activity.
  • The study provides a robust methodology for advancing EEG applications in neurolinguistics and brain-computer interfaces.