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Updated: Jan 28, 2026

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
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Lobe-wise cognitive load detection using empirical Fourier decomposition and optimized machine learning.

Kunamneni Chervitha1, Lakhan Dev Sharma1

  • 1School of Electronics Engineering, VIT-AP University, Guntur, Andhra Pradesh, India.

Frontiers in Physiology
|January 26, 2026
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Summary

This study introduces an Empirical Fourier Decomposition (EMFD) method combined with Optimized Ensemble Machine Learning (OML) for accurate electroencephalography (EEG)-based cognitive load detection, achieving over 97% accuracy.

Keywords:
cognitive loadelectroencephalogramempirical Fourier decompositionlobe-wiseoptimized machine learning

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Area of Science:

  • Neuroscience
  • Human-Computer Interaction
  • Machine Learning

Background:

  • Cognitive load influences neural activity, necessitating accurate assessment in neuroscience and HCI.
  • Electroencephalography (EEG) offers a noninvasive method for monitoring brain responses to mental effort.

Purpose of the Study:

  • To explore EEG-based feature extraction and classification for precise cognitive load assessment.
  • To evaluate the efficacy of Empirical Fourier Decomposition (EMFD) and Optimized Ensemble Machine Learning (OML) for cognitive load detection.

Main Methods:

  • EEG signals were decomposed using EMFD into intrinsic mode functions.
  • Entropy-based features were extracted and reduced.
  • Classifications were performed using OML and conventional machine learning (ML) classifiers on lobe-wise and overall data.
  • The method was validated on the Mental Arithmetic Task (MAT) and Spatial Transcriptomic Multi-View (STEW) datasets.

Main Results:

  • The EMFD-based OML framework achieved high classification accuracies: 97.8% on MAT and 96.4% on STEW.
  • Lobe-wise analysis demonstrated strong performance across all brain regions.
  • The frontal lobe yielded the highest accuracies, reaching 97.8% (MAT) and 96.08% (STEW).

Conclusions:

  • EMFD combined with OML effectively enhances EEG-based cognitive load detection.
  • The framework's consistent performance across datasets confirms its robustness.
  • The findings highlight the frontal lobe's significant role in cognitive processing and the method's potential for real-world applications.