Classification of Mental State Using a Muse Headband and Machine Learning Algorithm

K. K.Mujeeb Rahman; K. Mohamed Nasor

doi:10.1007/978-981-97-0562-7_34

Classification of Mental State Using a Muse Headband and Machine Learning Algorithm

K. K.Mujeeb Rahman
, K. Mohamed Nasor

College of Engineering and Information Technology

Ajman University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Scopus citations

Abstract

This paper describes an experimental study on the automatic classification of mental states using electroencephalogram (EEG) signals and a machine learning algorithm. We adopted the statistical and frequency domain features of EEG signals captured by Muse headband, which correspond to three classes of mental state that include relaxation, concentration, and neutral states. We experimented with different lightweight machine learning models to get an optimum classification. The paper includes descriptions of the Muse headband, feature extraction techniques, and machine learning algorithm development. We found the Random Forest and XGBoost algorithms achieved over 99% accuracy for the three classes. The study’s findings demonstrate that the Muse headband and XGBoost algorithm can predict three mental states with remarkably high levels of accuracy, providing promise for the development of novel methods to identify mental health disorders and the ability to detect complex mental states.

Original language	English
Title of host publication	Advances in Signal Processing and Communication Engineering - Select Proceedings of ICASPACE 2023
Editors	Pradip Kumar Jain, Yatindra Nath Singh, Ravi Paul Gollapalli, S. P. Singh
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	453-462
Number of pages	10
ISBN (Print)	9789819705610
DOIs	https://doi.org/10.1007/978-981-97-0562-7_34
State	Published - 2024
Event	International Conference on Advances in Signal Processing and Communication Engineering, ICASPACE 2023 - Hyderabad, India Duration: 28 Apr 2023 → 29 Apr 2023

Publication series

Name	Lecture Notes in Electrical Engineering
Volume	1157
ISSN (Print)	1876-1100
ISSN (Electronic)	1876-1119

Conference

Conference	International Conference on Advances in Signal Processing and Communication Engineering, ICASPACE 2023
Country/Territory	India
City	Hyderabad
Period	28/04/23 → 29/04/23

Keywords

Electroencephalogram
Machine learning
Muse headband
Random forest
XGBoost

Access to Document

10.1007/978-981-97-0562-7_34

Cite this

Rahman, K. K. M., & Nasor, K. M. (2024). Classification of Mental State Using a Muse Headband and Machine Learning Algorithm. In P. Kumar Jain, Y. Nath Singh, R. P. Gollapalli, & S. P. Singh (Eds.), Advances in Signal Processing and Communication Engineering - Select Proceedings of ICASPACE 2023 (pp. 453-462). (Lecture Notes in Electrical Engineering; Vol. 1157). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-981-97-0562-7_34

Rahman, K. K.Mujeeb ; Nasor, K. Mohamed. / Classification of Mental State Using a Muse Headband and Machine Learning Algorithm. Advances in Signal Processing and Communication Engineering - Select Proceedings of ICASPACE 2023. editor / Pradip Kumar Jain ; Yatindra Nath Singh ; Ravi Paul Gollapalli ; S. P. Singh. Springer Science and Business Media Deutschland GmbH, 2024. pp. 453-462 (Lecture Notes in Electrical Engineering).

@inproceedings{1853f7b89bf0426da26c41222a6ba79c,

title = "Classification of Mental State Using a Muse Headband and Machine Learning Algorithm",

abstract = "This paper describes an experimental study on the automatic classification of mental states using electroencephalogram (EEG) signals and a machine learning algorithm. We adopted the statistical and frequency domain features of EEG signals captured by Muse headband, which correspond to three classes of mental state that include relaxation, concentration, and neutral states. We experimented with different lightweight machine learning models to get an optimum classification. The paper includes descriptions of the Muse headband, feature extraction techniques, and machine learning algorithm development. We found the Random Forest and XGBoost algorithms achieved over 99\% accuracy for the three classes. The study{\textquoteright}s findings demonstrate that the Muse headband and XGBoost algorithm can predict three mental states with remarkably high levels of accuracy, providing promise for the development of novel methods to identify mental health disorders and the ability to detect complex mental states.",

keywords = "Electroencephalogram, Machine learning, Muse headband, Random forest, XGBoost",

author = "Rahman, \{K. K.Mujeeb\} and Nasor, \{K. Mohamed\}",

note = "Publisher Copyright: {\textcopyright} The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.; International Conference on Advances in Signal Processing and Communication Engineering, ICASPACE 2023 ; Conference date: 28-04-2023 Through 29-04-2023",

year = "2024",

doi = "10.1007/978-981-97-0562-7\_34",

language = "English",

isbn = "9789819705610",

series = "Lecture Notes in Electrical Engineering",

publisher = "Springer Science and Business Media Deutschland GmbH",

pages = "453--462",

editor = "\{Kumar Jain\}, Pradip and \{Nath Singh\}, Yatindra and Gollapalli, \{Ravi Paul\} and Singh, \{S. P.\}",

booktitle = "Advances in Signal Processing and Communication Engineering - Select Proceedings of ICASPACE 2023",

address = "Germany",

}

Rahman, KKM & Nasor, KM 2024, Classification of Mental State Using a Muse Headband and Machine Learning Algorithm. in P Kumar Jain, Y Nath Singh, RP Gollapalli & SP Singh (eds), Advances in Signal Processing and Communication Engineering - Select Proceedings of ICASPACE 2023. Lecture Notes in Electrical Engineering, vol. 1157, Springer Science and Business Media Deutschland GmbH, pp. 453-462, International Conference on Advances in Signal Processing and Communication Engineering, ICASPACE 2023, Hyderabad, India, 28/04/23. https://doi.org/10.1007/978-981-97-0562-7_34

Classification of Mental State Using a Muse Headband and Machine Learning Algorithm. / Rahman, K. K.Mujeeb; Nasor, K. Mohamed.
Advances in Signal Processing and Communication Engineering - Select Proceedings of ICASPACE 2023. ed. / Pradip Kumar Jain; Yatindra Nath Singh; Ravi Paul Gollapalli; S. P. Singh. Springer Science and Business Media Deutschland GmbH, 2024. p. 453-462 (Lecture Notes in Electrical Engineering; Vol. 1157).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Classification of Mental State Using a Muse Headband and Machine Learning Algorithm

AU - Rahman, K. K.Mujeeb

AU - Nasor, K. Mohamed

N1 - Publisher Copyright: © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

PY - 2024

Y1 - 2024

N2 - This paper describes an experimental study on the automatic classification of mental states using electroencephalogram (EEG) signals and a machine learning algorithm. We adopted the statistical and frequency domain features of EEG signals captured by Muse headband, which correspond to three classes of mental state that include relaxation, concentration, and neutral states. We experimented with different lightweight machine learning models to get an optimum classification. The paper includes descriptions of the Muse headband, feature extraction techniques, and machine learning algorithm development. We found the Random Forest and XGBoost algorithms achieved over 99% accuracy for the three classes. The study’s findings demonstrate that the Muse headband and XGBoost algorithm can predict three mental states with remarkably high levels of accuracy, providing promise for the development of novel methods to identify mental health disorders and the ability to detect complex mental states.

AB - This paper describes an experimental study on the automatic classification of mental states using electroencephalogram (EEG) signals and a machine learning algorithm. We adopted the statistical and frequency domain features of EEG signals captured by Muse headband, which correspond to three classes of mental state that include relaxation, concentration, and neutral states. We experimented with different lightweight machine learning models to get an optimum classification. The paper includes descriptions of the Muse headband, feature extraction techniques, and machine learning algorithm development. We found the Random Forest and XGBoost algorithms achieved over 99% accuracy for the three classes. The study’s findings demonstrate that the Muse headband and XGBoost algorithm can predict three mental states with remarkably high levels of accuracy, providing promise for the development of novel methods to identify mental health disorders and the ability to detect complex mental states.

KW - Electroencephalogram

KW - Machine learning

KW - Muse headband

KW - Random forest

KW - XGBoost

UR - https://www.scopus.com/pages/publications/85200324397

U2 - 10.1007/978-981-97-0562-7_34

DO - 10.1007/978-981-97-0562-7_34

M3 - Conference contribution

AN - SCOPUS:85200324397

SN - 9789819705610

T3 - Lecture Notes in Electrical Engineering

SP - 453

EP - 462

BT - Advances in Signal Processing and Communication Engineering - Select Proceedings of ICASPACE 2023

A2 - Kumar Jain, Pradip

A2 - Nath Singh, Yatindra

A2 - Gollapalli, Ravi Paul

A2 - Singh, S. P.

PB - Springer Science and Business Media Deutschland GmbH

T2 - International Conference on Advances in Signal Processing and Communication Engineering, ICASPACE 2023

Y2 - 28 April 2023 through 29 April 2023

ER -

Rahman KKM, Nasor KM. Classification of Mental State Using a Muse Headband and Machine Learning Algorithm. In Kumar Jain P, Nath Singh Y, Gollapalli RP, Singh SP, editors, Advances in Signal Processing and Communication Engineering - Select Proceedings of ICASPACE 2023. Springer Science and Business Media Deutschland GmbH. 2024. p. 453-462. (Lecture Notes in Electrical Engineering). doi: 10.1007/978-981-97-0562-7_34