One of the most significant qualities of the human brain is cognition, which incorporates both attention and recall. Attention is the initial process used when dealing with sensory inputs, prior to cognition, which acts to identify the nature of the stimuli as it impacts the individual's sensory system to select which details will be retained, processed, and recognised. Based on the increased use of e-learning, it has thus become necessary to determine the extent of student attention under the learning circumstances by looking at the state of the brain during this process. This can be achieved by reading brain signals using modern and advanced technologies with sensors that detect brain signals such as electroencephalography (EEG). The attention spans of students and their situational interest during learning have been studied for many years with respect to classroom learning; however, e-learning is becoming a highly popular method of learning, and the goal of this study is thus to use brain signals to measure learner attention levels in e-learning settings as compared to those seen in traditional classroom learning. The standard approach records EEG signals and their frequency bands across several subjects as they listen to a lecture in a classroom setting, characterising students' mental states as attentive or no attentive by means of machine learning classification models and algorithms. In this work, the best result were achieved with training using the Random Forest Classifier on the frequency band data set, which gave an accuracy of 91.4%, and by training using the Gated Recurrent Units neural network on the raw signal data set, which gave an accuracy of 96.4%.

1.
Laibow
,
R.
,
"Medical applications of neurobiofeedback
, in
Introduction to quantitative EEG and neurofeedback.
"
1999
,
Elsevier
. p.
83
102
.
2.
Kora
,
P.
, et al.,
EEG based interpretation of human brain activity during yoga and meditation using machine learning: A systematic review.
Complementary therapies in clinical practice
,
2021
.
43
: p.
101329
.
3.
Rosca
,
S.D.
and
M.
Leba
. Design of a brain-controlled video game based on a BCI system. in
MATEC Web of Conferences
.
2019
.
EDP Sciences
.
4.
Saleh
,
S.
, et al.,
A REVIEW OF ELECTROENCEPHALOGRAPHY (EEG) APPLICATION IN EDUCATION.
International Journal of Early Childhood
,
2022
.
14
(
03
): p.
2022
.
5.
Vasiljevic
,
G.A.M.
and
L.C.
de Miranda
,
Brain–computer interface games based on consumer-grade EEG Devices: A systematic literature review.
International Journal of Human–Computer Interaction
,
2020
.
36
(
2
): p.
105
142
.
6.
Lenartowicz
,
A.
, et al.,
Aberrant modulation of brain oscillatory activity and attentional impairment in attention-deficit/hyperactivity disorder. Biological Psychiatry: Cognitive Neuroscience and Neuroimaging
,
2018
.
3
(
1
): p.
19
29
.
7.
Marcuse
,
L.
,
M.
Fields
, and
J.
Yoo
,
The normal adult EEG.
Rowan's Primer of EEG
,
2016
: p.
39
66
.
8.
Udaya
,
C.
and
M.U.
Rani
.
Neuroelectrical Effect of Meditation Evaluated by Using EEG
. in
2018 IADS International Conference on Computing, Communications & Data Engineering (CCODE).
2018
.
9.
Ko
,
L.-W.
, et al.,
Sustained attention in real classroom settings: An EEG study.
Frontiers in human neuroscience
,
2017
.
11
: p.
388
.
10.
Mohamed
,
Z.
, et al. Facilitating classroom orchestration using eeg to detect the cognitive states of learners. in
International Conference on Advanced Machine Learning Technologies and Applications
.
2019
.
Springer
.
11.
Aggarwal
,
S.
, et al.,
A preliminary investigation for assessing attention levels for Massive Online Open Courses learning environment using EEG signals: An experimental study.
Human Behavior and Emerging Technologies
,
2021
.
3
(
5
): p.
933
941
.
12.
Hassan
,
R.
, et al. Human attention recognition with machine learning from brain-EEG signals. in
2020 IEEE 2nd Eurasia Conference on Biomedical Engineering, Healthcare and Sustainability (ECBIOS)
.
2020
.
IEEE
.
13.
Toa
,
C.K.
,
K.S.
Sim
, and
S.C.
Tan
,
Electroencephalogram-based attention level classification using convolution attention memory neural network.
IEEE Access
,
2021
.
9
: p.
58870
58881
.
14.
Liao
,
C.-Y.
,
R.-C.
Chen
, and
S.-K.
Tai
,
Evaluating attention level on MOOCs learning based on brainwaves signals analysis.
Int. J. Innov. Comput. Inf. Control
,
2019
.
15
(
1
): p.
39
51
.
15.
Bilal
,
M.
, et al.,
EEG-Based BCI for Attention Assessment in E-Learning Environment using SVM.
KIET Journal of Computing and Information Sciences
,
2022
.
5
(
1
): p.
75
90
.
16.
Behzadfar
,
N.
,
A Brief Overview on Analysis and Feature Extraction of Electroencephalogram Signals.
Signal Processing and Renewable Energy
,
2022
.
6
(
1
): p.
39
64
.
17.
Maxwell
,
A.E.
,
T.A.
Warner
, and
L.A.
Guillén
,
Accuracy assessment in convolutional neural network-based deep learning remote sensing studies—part 1: Literature review.
Remote Sensing
,
2021
.
13
(
13
): p.
2450
.
18.
Debbarma
,
S.
,
S.
Nabavi
, and
S.
Bhadra
. A wireless flexible electrooculogram monitoring system with printed electrodes. in
2021 IEEE International Instrumentation and Measurement Technology Conference (I2MTC)
.
2021
.
IEEE
.
19.
Aziz
,
S.
, et al. Electromyography (EMG) data-driven load classification using empirical mode decomposition and feature analysis. in
2019 International Conference on Frontiers of Information Technology (FIT)
.
2019
.
IEEE
.
20.
Perusquía-Hernández
,
M.
, et al. Smile Action Unit detection from distal wearable Electromyography and Computer Vision. in
2021 16th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2021)
.
2021
.
IEEE
.
21.
Soundirarajan
,
M.
,
O.
Krejcar
, and
H.
Namazi
,
Evaluation of the coupling between the brain and facial muscles reactions to moving visual stimuli.
Fluctuation and Noise Letters
,
2021
.
20
(
05
): p.
2150042
.
22.
Malghan
,
P.G.
and
M.K.
Hota
,
A review on ECG filtering techniques for rhythm analysis.
Research on Biomedical Engineering
,
2020
.
36
(
2
): p.
171
186
.
23.
Jabreel
,
M.
and
A.
Moreno
,
A Deep Learning-Based Approach for Multi-Label Emotion Classification in Tweets.
Applied Sciences
,
2019
.
9
: p.
1123
.
24.
Chen
,
J.
,
D.
Jiang
, and
Y.
Zhang
,
A hierarchical bidirectional GRU model with attention for EEG-based emotion classification.
IEEE Access
,
2019
.
7
: p.
118530
118540
.
25.
Goldberg
,
P.
, et al.,
Attentive or not? Toward a machine learning approach to assessing students’ visible engagement in classroom instruction.
Educational Psychology Review
,
2021
.
33
(
1
): p.
27
49
.
26.
Padhi
,
A.
, et al., An iot model to improve cognitive skills of student learning experience using neurosensors, in
Internet of Things and Personalized Healthcare Systems
.
2019
,
Springer
. p.
37
50
.
27.
Vettivel
,
N.
, et al. System for detecting student attention pertaining and alerting. in
2018 3rd International Conference on Information Technology Research (ICITR)
.
2018
.
IEEE
.
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