Classification of a person's cognitive state is important in many applications. The aim of this work is to develop a reliable classifier for predicting the cognitive state of the brain while performing various tasks. This dataset was recorded using a Muse headset with 10-20 EEG electrode placements and four different electrodes. The cognitive state of ten students was recorded while performing various tasks, such as mental arithmetic, reading technical articles, listening to technical podcasts, surfing the Internet, and resting with eyes open or closed. The EEG dataset is preprocessed and feature sets of five signals alpha, beta, gamma, delta and theta are selected for prediction. This dataset is trained by any machine learning algorithm such as decision trees, naive Bayes, and support vector machines. All machine learning algorithms are then embedded into a Stacking Ensemble Classifier with 10-fold cross-validation to predict the cognitive state of brain signals and classify them as high and low. Compared with existing algorithms, the method achieves an overall accuracy rate of 96%.

1.
AhnafAkif
,
MuntequeImtiazSiraji
,
LamimIbtisam
Khalid
,
Fahim
Faisal
,
MirzaMuntasir
Nishat
,
Mohammad Rakibul
Islam
, "
Detection of mental state from EEG signal data: An investigation with machine learning classifiers
”, (
14th International Conference on Knowlegde and Smart Technology(KST)
,
152
156
,
2022
).
2.
AshwinKamble
,
PradnyaGhare
,
Vinay
Kumar
, “
Machine learning enabled adaptive signal decomposition for a brain computer interface using EEG
”, (
Biomedical Signal Processing and Control
74
,
103526
,
2022
).
3.
Chaojie
Fan
,
Yong
Peng
,
Shuangling
Peng
,
Honghao
Zhang
,
Yuankai
Wu
,
Sam
Kwong
, “
Detection of train driver fatique and distraction based on forehead EEG: a time series ensemble learning model
”, (
IEEE Transactions on Intelligent Transportation Systems
,
2021
).
4.
CosimoIeracitano
,
Nadia Mammone
,
Alesia
Bramanti
,
Silivia
Marino
,
Amir
Hussain
,
Francesco Carlo
Morabito
, “
A time-frequency based machine learning system for brain states
”, (
International Joint Conference on Neural Networks(IJCNN)
,
11
8
,
2019
).
5.
Deepak O
Patil
,
Sathish T
Hamde
, “
Automated detection of brain tumor disesase using empirical wavelet transform based LBP variants and ant-lion optimization
”,
Multimedia Tools and Applications
80
(
12
),
17955
17982
,
2021
).
6.
Devesh Kumar
Upadhyay
,
Subrajeet
Mohapatra
,
Niraj Kumar
Singh
,
Ajay Kumar
Bakhla
, “
Stacked SVM model for Dysthmia prediction in undergraduate students
”, (
2021 8th International Conference on Signal Processing and Integrated Networks(SPIN)
,
1148
1153
,
2021
).
7.
DonghaiZhai
,
Yufan Pan
,
Peige
Li
,
Guofa
Li
, “
Estimating the vigilance of high-speed rail drivers using a stacking ensemble leraning method
”, (
IEEE Sensors Journal
21
(
15
),
16826
16838
,
2021
).
8.
GunasekaranManogaran
,
P
Mohamed
Shakeel
,
Azza S
Hassanein
,
PriyanMalarvizhi
Kumar
,
Gokulnath Chandra
Babu
, “Machine learning approach-based gamma distribution for brain tumor detection and data sample imbalance analysis, (
IEEE Access
7
,
112
19
,
2018
).
9.
HabibUllah
,
MuhammedUzair
,
ArifMahmood
,
MohibUllah
,
Sultan Daud
Khan
,
Faouzi
AlayaCheikh
, “
Internal emotion classification using EEG signal with sparse discriminative ensemble
”, (
IEEE Access
7
,
40144
40153
,
2019
).
10.
IaoqingGu
,
WeiweiCai
,
Ming
Gao
,
Yizhang
Jiang
,
in
Ning
,
Pengjiang
Qian
, “
Multi-source domain transfer discriminative dictionary learning modeling for EEG based emotion recognition
”, (
IEEE Transactions on Computational Social Systems
,
2022
).
11.
Jinjin
Zhou
,
Guangsheng
Wang
,
Junbiao
Liu
,
Duanpo
Wu
,
Weifeng
u
,
Zimeng
Wang
,
Jing
Ye
,
Ming
Xia
,
Ying
Hu
,
Yuanyuan
Tian
, “
Automatic sleep stage classification with single channel EEG signal based on two-layer stacked ensemble model
”, (
IEEE Access
8
,
57283
57297
,
2020
).
12.
Jordan J.
Bird
,
Luis J.
Manso
,
EduaedoP.
Riberio
,
Aniko
Ekart
,
Diego R.
Faria
, “
A Study on Mental State Classification using EEG-based Brain-Machine Interface
”, (
International Conference on intelligent systems(IS)
,
795
800
,
2018
).
13.
Kranti S.
Kamble
,
Joydeep
Sengupta
, “
Ensemble Machine Learning-Based Affective Computing for Emotion Recognition using Dual-Decomposed EEG signals
”, (
IEEE sensors Journal
,
2021
).
14.
LiangshengZheng
,
Yang Xiao
,
Yue
Ma
,
Mengayao
Li
,
Wei
Feng
,
Xinyu
Wu
, “
Time frequency decomposition based weighted ensemble learning for motor imagery EEG classification
”, (
IEEE International Conference on Real-Time Computing and Robotics(RCAR)
,
620
625
,
2021
).
15.
MoeinRadman
,
MiladMoradi
,
Ali
Chaibakhsh
,
MojtabaKordetani
,
MehrdadSaif
, “
Multi-feature fusion approach for epileptic seizure detection from EEEG signals
”, (
IEEE Sensors Journal
21
(
3
),
13533
3543
,
2020
).
16.
Mohammad-Parsa
Hosseini
,
Amin
Hosseini
,
Kiarash
Ahi
, “
A review on machine learning for EEG signal processing in bioengineering
”, (
IEEE reviews in biomedical engineering
14
,
1204
218
,
2020
).
17.
Moumen T
El-Melegy
,
Khaled M Abo
El-Magd
,
Samia A
Ali
,
Khaled F
Hussain
,
Yousef B
Mahdy
, “
Ensemble of multiple classifiers for automatic multimodal brain tumor segmentation
”, (
2019 International Conference on Innovative Trends in Computer Engineering(ITCE)
,
158
63
,
2019
).
18.
Neelum
Noreen
,
Sellappan
Palaniappan
,
Abdul
Qayyum
,
Iftikhar
Ahmad
,
Muhammad
Imran
,
Muhammad
Shoaib
, “
A deep learning model based on concatenation approach for the diagnosis of brain tumor
”,
IEEE Access8
,
55135
55144
,
2020
).
19.
Shalini
Mahato
and
Sanchita
Paul
, “
Detection of major depressive disorder using linear and non-linear features from EEG signals
”, (
Microsyst. Technol.
,
11065
1076
, March
2019
).
20.
ShrutiGedam
,
Sanchita Paul
, ―A Review on Mental Stress Detection using Wearable Sensors and Machine Learning Techniques. (
IEEE Access
2021
.
3085502
).
21.
TonmoyHossain
,
FairuzShadmaniShishir
,
Mohsena
Ashraf
,
MD Abdullah
Al Nasim
,
Faisal Muhammad
Shah
, “Brain tumor detection using convolutional neural network”, (
International Conference on Advances in Science
,
Engineering and Robotics Technology(ICASERT
),
1
6
,
2019
).
22.
Xiaojun
Yu
,
Muhammad Zulkifal
Aziz
,
Muhammad Tariq
Sadiq
,
KeJia
,
Zeming
Fan
, “
Computerized Multidomain EEG Classification System: A new paradigm
”, (
IEEE Journal of Biomedical and Health Informatics
,
2022
).
This content is only available via PDF.
You do not currently have access to this content.