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Detail publikace
JAWED, S. FAYE, I. MALIK, A.
Originální název
Deep learning-based assessment model for Real-time identification of visual learners using Raw EEG
Typ
článek v časopise ve Scopus, Jsc
Jazyk
angličtina
Originální abstrakt
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.
Klíčová slova
Raw-Electroencephalogram, Deep learning, Machine learning, Visual Learner, Classification, Learning styles
Autoři
JAWED, S.; FAYE, I.; MALIK, A.
Vydáno
2. 1. 2024
ISSN
1558-0210
Periodikum
IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING
Ročník
32
Číslo
1
Stát
Spojené státy americké
Strany od
378
Strany do
390
Strany počet
13
URL
https://ieeexplore.ieee.org/document/10387266?source=authoralert
Plný text v Digitální knihovně
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" }