Publication detail

Deep learning-based assessment model for Real-time identification of visual learners using Raw EEG

JAWED, S. FAYE, I. MALIK, A.

Original Title

Deep learning-based assessment model for Real-time identification of visual learners using Raw EEG

Type

journal article in Scopus

Language

English

Original Abstract

Automatic identification of visual learning style in real time using raw electroencephalogram (EEG) is challenging. In this work, inspired by the powerful abilities of deep learning techniques, deep learning-based models are proposed to learn high-level feature representation for EEG visual learning identification. Existing computer-aided systems that use electroencephalograms and machine learning can reasonably assess learning styles. Despite their potential, offline processing is often necessary to eliminate artifacts and extract features, making these methods unsuitable for real-time applications. The dataset was chosen with 34 healthy subjects to measure their EEG signals during resting states (eyes open and eyes closed) and while performing learning tasks. The subjects displayed no prior knowledge of the animated educational content presented in video format. The paper presents an analysis of EEG signals measured during a resting state with closed eyes using three deep learning techniques: Long-term, short-term memory (LSTM), Long-term, short-term memory-convolutional neural network (LSTM-CNN), and Long-term, short-term memory - Fully convolutional neural network (LSTM-FCNN). The chosen techniques were based on their suitability for real-time applications with varying data lengths and the need for less computational time. The optimization of hypertuning parameters has enabled the identification of visual learners through the implementation of three techniques. LSTM- CNN technique has the highest average accuracy of 94%, a sensitivity of 80%, a specificity of 92%, and an F1 score of 94% when identifying the visual learning style of the student out of all three techniques. This research has shown that the most effective method is the deep learning-based LSTM-CNN technique, which accurately identifies a student's visual learning style. 

Keywords

Raw-Electroencephalogram, Deep learning, Machine learning, Visual Learner, Classification, Learning styles

Authors

JAWED, S.; FAYE, I.; MALIK, A.

Released

2. 1. 2024

ISBN

1558-0210

Periodical

IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING

Year of study

32

Number

1

State

United States of America

Pages from

378

Pages to

390

Pages count

13

URL

https://ieeexplore.ieee.org/document/10387266?source=authoralert

Full text in the Digital Library

http://hdl.handle.net/11012/249160

BibTex

@article{BUT187445,
  author="Soyiba {Jawed} and Ibrahima {Faye} and Aamir Saeed {Malik}",
  title="Deep learning-based assessment model for Real-time identification of visual learners using Raw EEG",
  journal="IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING",
  year="2024",
  volume="32",
  number="1",
  pages="378--390",
  doi="10.1109/TNSRE.2024.3351694",
  issn="1558-0210",
  url="https://ieeexplore.ieee.org/document/10387266?source=authoralert"
}