The performance of university students during their academic session are vital to their overall grade throughout their term in the university. There are multiple factors that could lead to the loss of performance but the foremost factor is their level of emotions. Previous research has shown that to determine the performance of the students, the best way to do so is by analysing their attention levels. With the development of portable Electroencephalogram (EEG) devices and machine learning algorithms, it is easy to obtain the students attention and emotion level during their academic sessions. This paper aims to present a method of obtaining the EEG signals using a portable EEG device and classifying it into the type of emotions that are present in the human brain. The EEG device will obtain the attention level and EEG signals during two scenarios which are lectures/tutorials and exams/quizzes. The signals are then compiled and analysed to determine the emotion labels based on a normalization process that categories the signals into positive or negative emotions. The dataset and labels are then used to train and evaluate multiple machine learning models and a deep learning model in order to determine which model has the best accuracy and performance. The chosen model is then used to predict the emotions of several students during both scenarios and the average emotions are then compared with their average attention to determine the effect of emotions on the students’ performance. Hence, this paper will first provide a method on obtaining the emotion labels, followed by the models’ development and finally correlating the predicted emotions with the students’ performance during their academic sessions.

1.
P. A.
Banerjee
,
Cogent Education
3
, p.
1178441
(
2016
).
2.
X.
Oriol-Granado
,
M.
Mendoza-Lira
,
C.-G.
Covarrubias-Apablaza
, and
V.-M.
Molina-López
,
Revista de Psicodidáctica
22
,
45
53
(
2017
).
3.
M. L. R.
Menezes
 et al.,
Pers. Ubiquitous Comput.
21
,
1003
1013
(
2017
).
4.
S.
Siuly
,
Y.
Li
, and
Y.
Zhang
, ‘Electroencephalogram (EEG) and Its Background’ in
EEG Signal Analysis and Classification. Health Information Science
(
Springer
,
Cham
,
2017
), pp.
3
21
.
5.
J.
Xu
and
B.
Zhong
,
Comput. Human Behav.
81
,
340
349
(
2018
).
6.
W.-L.
Zheng
,
J.-Y.
Zhu
, and
B.-L.
Lu
,
IEEE Trans. Affect. Comput.
10
,
417
429
(
2017
).
7.
J.
Gemignani
,
E.
Middell
,
R. L.
Barbour
,
H. L.
Graber
, and
B.
Blankertz
,
J. Neural Eng.
15
,
0
11
(
2016
).
8.
Z.
Wang
and
R. S.
Srinivasan
,
Renewable and Sustainable Energy Reviews
75
,
796
808
(
2017
).
9.
V.
Mohan
,
M.
Amran
,
Y.
Yahaya
, and
N.
Yusop
,
International Journal of Recent Technology and Engineering
8
,
2736
2740
(
2019
).
10.
L.
Yang
, “Enhancing positive emotions and reducing negative emotions of Chinese university students: An exploratory intervention study in a general education course,” in
9th European Conference on Positive Psychology: Positive psychology for a flourishing Europe in times of transitions, ELTE Conference Center
,
Budapest
,
Hungary
(
2018
).
11.
J.
Liu
,
H.
Meng
,
A.
Nandi
, and
M.
Li
, “
Emotion detection from EEG recordings
,” in
2016 12th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery, ICNC-FSKD 2016
(
2016
), pp.
1722
1727
.
12.
W.
Zheng
,
J.
Zhu
, B. L.-I. T.,
IEEE Transactions on Affective Computing
10
,
417
429
(
2017
).
13.
T. H.
Supervised
and
P. T.
Mitrovic
,
The Emotiv mind: Investigating the accuracy of the Emotiv EPOC in identifying emotions and its use in an Intelligent Tutoring System
,
Honours Report, University of Canterbury
(
2013
).
14.
R.
Lievesley
,
M.
Wozencroft
, and
D.
Ewins
,
J. Assist. Technol.
,
5
,
67
82
(
2011
).
15.
J.
Katona
,
I.
Farkas
,
T.
Ujbanyi
,
P.
Dukan
and
A.
Kovari
, “
Evaluation of the NeuroSky MindFlex EEG headset brain waves data
,” in
2014 IEEE 12th international symposium on applied machine intelligence and informatics (SAMI)
(
2014
).
16.
EEG: The Ultimate Guide
. http://neurosky.com/biosensors/eeg-sensor/ultimate-guide-to-eeg/ (Accessed 10 Jun
2020
).
17.
C. M.
Chen
,
J. Y.
Wang
, and
Y. C.
Lin
,
Electron. Libr.
37
,
680
702
(
2019
).
18.
C. M.
Chen
and
S. H.
Huang
,
Br. J. Educ. Technol
,
45
,
959
980
(
2014
).
19.
R.
Shadiev
,
W. Y.
Hwang
, and
Y. M.
Huang
,
Comput. Assist. Lang. Learn.
30
,
284
303
(
2017
).
20.
C.
Chen
and C. W.-C,
Computers & Education
80
,
108
121
(
2015
).
21.
Y.-M.
Huang
,
M.-C.
Liu
,
C.-H.
Lai
, and
C.-J.
Liu
,
Br. J. Educ. Technol.
48
,
878
896
(
2017
).
22.
J. C. Y.
Sun
,
Comput. Educ.
72
,
80
89
(
2014
).
23.
C.-M.
Chen
,
J.-Y.
Wang
, and
C.-M.
Yu
,
Br. J. Educ. Technol.
48
,
348
369
(
2017
).
24.
S.
Alarcao
, M. F.-I. T.,
IEEE Transactions on Affective Computing
10
,
374
393
(
2017
).
25.
T.
Ergin
,
M.
Ozdemir
, A. A. 2019 M. “
Technologies, and undefined 2019, ‘Emotion Recognition with Multi-Channel EEG Signals Using Visual Stimulus
,” in
2019 Medical Technologies Congress (TIPTEKNO)
(
2019
).
26.
D. J.
Mcfarland
,
W. A.
Sarnacki
,
J. R.
Wolpaw
, and
D. J.
Krusienski
,
Journal of Neural Engineering
14
, p.
016009
(
2016
).
27.
J.
Demos
,
Getting Started with EEG Neurofeedback.
2019
.
28.
Making Sense of EEG Bands
. http://neurosky.com/2015/05/greek-alphabet-soup-making-sense-of-eeg-bands/ (Accessed 10 Jun
2020
).
29.
V. S.
Mohan
,
M. F. M.
Amran
,
Y. H.
Yahaya
,
N. M. M.
Yusop
,
T. M. T.
Sembok
and
M. A.
Ahmad
,
International Journal of Recent Technology and Engineering
,
8
,
2736
2740
(
2019
).
This content is only available via PDF.
You do not currently have access to this content.