Real-time Efficacy of Features Extraction using Machine Learning and Deep Learning for Frontal Alpha Asymmetry.

Yasir Hafeez; Syed Saad Azhar Ali; Hafeez Ullah Amin; Syed Faraz Naqvi; Syed Hasan Adil; Tang Tong Boon

doi:10.1109/ROMA55875.2022.9915702

Real-time Efficacy of Features Extraction using Machine Learning and Deep Learning for Frontal Alpha Asymmetry.

Yasir Hafeez, Syed Saad Azhar Ali, Hafeez Ullah Amin, Syed Faraz Naqvi, Syed Hasan Adil, Tang Tong Boon

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

1 Scopus citations

Abstract

The frontal alpha asymmetry represents as the neuromarker for stress. Stress is the psycho-physiological state of brain in response to some event or a demand. The continuous monitoring of mental stress is necessary to avoid chronic health issues. The real-time monitoring of frontal alpha asymmetry is necessary in daily life and to help in the therapy for example neurofeedback. In this paper, different approaches of machine learning and deep learning were adopted to extract the frontal alpha asymmetry features. The results analysis was based on the efficacy and the comparison of techniques for feature extraction has also been presented.

Original language	English
Title of host publication	2022 IEEE 5th International Symposium in Robotics and Manufacturing Automation, ROMA 2022
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781665459327
DOIs	https://doi.org/10.1109/ROMA55875.2022.9915702
State	Published - 2022
Externally published	Yes
Event	5th IEEE International Symposium in Robotics and Manufacturing Automation, ROMA 2022 - Malacca, Malaysia Duration: 5 Aug 0202 → 7 Aug 0202

Publication series

Name	2022 IEEE 5th International Symposium in Robotics and Manufacturing Automation, ROMA 2022

Conference

Conference	5th IEEE International Symposium in Robotics and Manufacturing Automation, ROMA 2022
Country/Territory	Malaysia
City	Malacca
Period	5/08/02 → 7/08/02

Bibliographical note

Publisher Copyright:
© 2022 IEEE.

Keywords

deep learning
feature extraction
machine learning
mental stress detection
physiological signals

ASJC Scopus subject areas

Artificial Intelligence
Industrial and Manufacturing Engineering
Control and Optimization

Access to Document

10.1109/ROMA55875.2022.9915702

Cite this

Hafeez, Y., Ali, S. S. A., Amin, H. U., Naqvi, S. F., Adil, S. H., & Boon, T. T. (2022). Real-time Efficacy of Features Extraction using Machine Learning and Deep Learning for Frontal Alpha Asymmetry. In 2022 IEEE 5th International Symposium in Robotics and Manufacturing Automation, ROMA 2022 (2022 IEEE 5th International Symposium in Robotics and Manufacturing Automation, ROMA 2022). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ROMA55875.2022.9915702

Hafeez, Yasir ; Ali, Syed Saad Azhar ; Amin, Hafeez Ullah et al. / Real-time Efficacy of Features Extraction using Machine Learning and Deep Learning for Frontal Alpha Asymmetry. 2022 IEEE 5th International Symposium in Robotics and Manufacturing Automation, ROMA 2022. Institute of Electrical and Electronics Engineers Inc., 2022. (2022 IEEE 5th International Symposium in Robotics and Manufacturing Automation, ROMA 2022).

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title = "Real-time Efficacy of Features Extraction using Machine Learning and Deep Learning for Frontal Alpha Asymmetry.",

abstract = "The frontal alpha asymmetry represents as the neuromarker for stress. Stress is the psycho-physiological state of brain in response to some event or a demand. The continuous monitoring of mental stress is necessary to avoid chronic health issues. The real-time monitoring of frontal alpha asymmetry is necessary in daily life and to help in the therapy for example neurofeedback. In this paper, different approaches of machine learning and deep learning were adopted to extract the frontal alpha asymmetry features. The results analysis was based on the efficacy and the comparison of techniques for feature extraction has also been presented.",

keywords = "deep learning, feature extraction, machine learning, mental stress detection, physiological signals",

author = "Yasir Hafeez and Ali, \{Syed Saad Azhar\} and Amin, \{Hafeez Ullah\} and Naqvi, \{Syed Faraz\} and Adil, \{Syed Hasan\} and Boon, \{Tang Tong\}",

note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 5th IEEE International Symposium in Robotics and Manufacturing Automation, ROMA 2022 ; Conference date: 05-08-0202 Through 07-08-0202",

year = "2022",

doi = "10.1109/ROMA55875.2022.9915702",

language = "English",

series = "2022 IEEE 5th International Symposium in Robotics and Manufacturing Automation, ROMA 2022",

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booktitle = "2022 IEEE 5th International Symposium in Robotics and Manufacturing Automation, ROMA 2022",

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Hafeez, Y, Ali, SSA, Amin, HU, Naqvi, SF, Adil, SH & Boon, TT 2022, Real-time Efficacy of Features Extraction using Machine Learning and Deep Learning for Frontal Alpha Asymmetry. in 2022 IEEE 5th International Symposium in Robotics and Manufacturing Automation, ROMA 2022. 2022 IEEE 5th International Symposium in Robotics and Manufacturing Automation, ROMA 2022, Institute of Electrical and Electronics Engineers Inc., 5th IEEE International Symposium in Robotics and Manufacturing Automation, ROMA 2022, Malacca, Malaysia, 5/08/02. https://doi.org/10.1109/ROMA55875.2022.9915702

Real-time Efficacy of Features Extraction using Machine Learning and Deep Learning for Frontal Alpha Asymmetry. / Hafeez, Yasir; Ali, Syed Saad Azhar; Amin, Hafeez Ullah et al.
2022 IEEE 5th International Symposium in Robotics and Manufacturing Automation, ROMA 2022. Institute of Electrical and Electronics Engineers Inc., 2022. (2022 IEEE 5th International Symposium in Robotics and Manufacturing Automation, ROMA 2022).

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

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AU - Adil, Syed Hasan

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AB - The frontal alpha asymmetry represents as the neuromarker for stress. Stress is the psycho-physiological state of brain in response to some event or a demand. The continuous monitoring of mental stress is necessary to avoid chronic health issues. The real-time monitoring of frontal alpha asymmetry is necessary in daily life and to help in the therapy for example neurofeedback. In this paper, different approaches of machine learning and deep learning were adopted to extract the frontal alpha asymmetry features. The results analysis was based on the efficacy and the comparison of techniques for feature extraction has also been presented.

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KW - physiological signals

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M3 - Conference contribution

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Hafeez Y, Ali SSA, Amin HU, Naqvi SF, Adil SH, Boon TT. Real-time Efficacy of Features Extraction using Machine Learning and Deep Learning for Frontal Alpha Asymmetry. In 2022 IEEE 5th International Symposium in Robotics and Manufacturing Automation, ROMA 2022. Institute of Electrical and Electronics Engineers Inc. 2022. (2022 IEEE 5th International Symposium in Robotics and Manufacturing Automation, ROMA 2022). doi: 10.1109/ROMA55875.2022.9915702

Real-time Efficacy of Features Extraction using Machine Learning and Deep Learning for Frontal Alpha Asymmetry.

Abstract

Publication series

Conference

Bibliographical note

Keywords

ASJC Scopus subject areas

Access to Document

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Cite this