Vol. 4 No. 4 (2025): October
RESEARCH ARTICLES

Identification of Visual Learners Using Raw EEG

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  • P Arshiya Khannam,
  • S Fathima Zakiya,
  • M Mounika,
  • B R V Chaitanya,
  • M Sasank,
  • A Ajay
P Arshiya Khannam
Department of AI and Data Science, Annamacharya Institute of Technology and Sciences, Kadapa, Andhra Pradesh, India
S Fathima Zakiya
Department of AI and Data Science, Annamacharya Institute of Technology and Sciences, Kadapa, Andhra Pradesh, India
M Mounika
Department of AI and Data Science, Annamacharya Institute of Technology and Sciences, Kadapa, Andhra Pradesh, India
B R V Chaitanya
Department of AI and Data Science, Annamacharya Institute of Technology and Sciences, Kadapa, Andhra Pradesh, India
M Sasank
Department of AI and Data Science, Annamacharya Institute of Technology and Sciences, Kadapa, Andhra Pradesh, India
A Ajay
Department of AI and Data Science, Annamacharya Institute of Technology and Sciences, Kadapa, Andhra Pradesh, India
DOI: https://doi.org/10.5281/zenodo.15251297

Published 2025-04-20

Keywords

  • Visual Learners,
  • Electroencephalography (EEG),
  • Long Short-Term Memory (LSTM),
  • Random Forest Classifier,
  • Feature Extraction

How to Cite

P Arshiya Khannam, S Fathima Zakiya, M Mounika, B R V Chaitanya, M Sasank, & A Ajay. (2025). Identification of Visual Learners Using Raw EEG. International Journal of Computational Learning & Intelligence, 4(4), 733–742. https://doi.org/10.5281/zenodo.15251297

Copyright (c) 2025 P Arshiya Khannam, S Fathima Zakiya, M Mounika, B R V Chaitanya, M Sasank, A Ajay

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Abstract

The project titled "IDENTIFICATION OF VISUAL LEARNERS USING RAW ELECTROENCEPHLOGRAPHY" addresses the challenge of accurately identifying visual learners, who are a significant portion of the student population that benefits from visual stimuli in their learning processes. Traditional methods of identifying learning styles, such as self-report questionnaires, are often subjective and prone to biases, highlighting the need for more objective approaches. To tackle this issue, the project employs a novel hybrid methodology that integrates Convolutional Neural Networks (CNNs) and Long Short-Term Memory networks (LSTMs) with a Random Forest classifier. This approach leverages the strengths of CNNs in extracting spatial features from raw EEG data, while LSTMs capture the temporal dependencies inherent in the sequential nature of EEG signals. The implications for educational practices are profound. This project not only paves the way for personalized educational strategies tailored to individual learning styles but also emphasizes the potential of neuroeducational techniques in enhancing learning outcomes. By utilizing advanced machine learning algorithms, educators can develop targeted interventions that align with students' cognitive preferences, ultimately optimizing the learning experience and fostering better academic performance.

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