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Formulation of sensor ranking associated in categorical perception: a pilot study using machine learning

The concept of categorical perception has been enormously investigated to digitalize the process of auditory processing when perceiving speech stimulus at higher brain neurological signal. Despite the nature non-stationary property of electroencephalography (EEG) during any task processing, scientis...

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Main Authors:	Bakar, Abdul Rauf A., Lai, Khin Wee, Hamzaid, Nur Azah
Format:	Conference or Workshop Item
Published:	Springer Science and Business Media Deutschland GmbH 2022
Subjects:	TA Engineering (General). Civil engineering (General)
Online Access:	http://eprints.um.edu.my/43404/ https://www.scopus.com/inward/record.uri?eid=2-s2.0-85129321333&doi=10.1007%2f978-3-030-90724-2_1&partnerID=40&md5=c9d95432d0832a41454066d8d1040816
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spelling	my.um.eprints.434042023-11-03T06:53:33Z http://eprints.um.edu.my/43404/ Formulation of sensor ranking associated in categorical perception: a pilot study using machine learning Bakar, Abdul Rauf A. Lai, Khin Wee Hamzaid, Nur Azah TA Engineering (General). Civil engineering (General) The concept of categorical perception has been enormously investigated to digitalize the process of auditory processing when perceiving speech stimulus at higher brain neurological signal. Despite the nature non-stationary property of electroencephalography (EEG) during any task processing, scientists and clinicians find it to be not well fitted in the healthcare setting application without professional monitoring, and even worst, the hearing aid device functionality was also reported at a low success rate. In this study, we embraced machine learning technology and extracting contribution into our auditory research area. The present pilot work aims to create a robust computational framework to formulate the sensor ranking principle in auditory speech perception. The ranking for sensors could facilitate in identifying the minimal set sensor-of-interest (ROI) that are sufficient in specific auditory task processing using an optimally trained model. The trained Support Vector Machine (SVM) highest performance reported at random 2 training dataset with scoring of 92.3 using 70 triple-random training dataset. Based on the sensor ranking, the CZ electrode outperformed the other electrodes with scoring of 96.74, followed by PZ and FPZ for the 2nd and 3rd rank (95.66 and 95.34 respectively). Our pilot study anticipated that the sensor ranking formula able to underline more precise neural correlates based on current auditory categorical perception response. The excellence sensor ranking in delivering a minimal set of sensor-of-interest (SOI) drive the capability of the SVM model in classifying auditory brain response in high-performance prediction metrics and possible reliability in the healthcare setting application. © 2022, Springer Nature Switzerland AG. Springer Science and Business Media Deutschland GmbH 2022 Conference or Workshop Item PeerReviewed Bakar, Abdul Rauf A. and Lai, Khin Wee and Hamzaid, Nur Azah (2022) Formulation of sensor ranking associated in categorical perception: a pilot study using machine learning. In: 6th Kuala Lumpur International Conference on Biomedical Engineering, BioMed 2021. https://www.scopus.com/inward/record.uri?eid=2-s2.0-85129321333&doi=10.1007%2f978-3-030-90724-2_1&partnerID=40&md5=c9d95432d0832a41454066d8d1040816
institution	Universiti Malaya
building	UM Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Malaya
content_source	UM Research Repository
url_provider	http://eprints.um.edu.my/
topic	TA Engineering (General). Civil engineering (General)
spellingShingle	TA Engineering (General). Civil engineering (General) Bakar, Abdul Rauf A. Lai, Khin Wee Hamzaid, Nur Azah Formulation of sensor ranking associated in categorical perception: a pilot study using machine learning
description	The concept of categorical perception has been enormously investigated to digitalize the process of auditory processing when perceiving speech stimulus at higher brain neurological signal. Despite the nature non-stationary property of electroencephalography (EEG) during any task processing, scientists and clinicians find it to be not well fitted in the healthcare setting application without professional monitoring, and even worst, the hearing aid device functionality was also reported at a low success rate. In this study, we embraced machine learning technology and extracting contribution into our auditory research area. The present pilot work aims to create a robust computational framework to formulate the sensor ranking principle in auditory speech perception. The ranking for sensors could facilitate in identifying the minimal set sensor-of-interest (ROI) that are sufficient in specific auditory task processing using an optimally trained model. The trained Support Vector Machine (SVM) highest performance reported at random 2 training dataset with scoring of 92.3 using 70 triple-random training dataset. Based on the sensor ranking, the CZ electrode outperformed the other electrodes with scoring of 96.74, followed by PZ and FPZ for the 2nd and 3rd rank (95.66 and 95.34 respectively). Our pilot study anticipated that the sensor ranking formula able to underline more precise neural correlates based on current auditory categorical perception response. The excellence sensor ranking in delivering a minimal set of sensor-of-interest (SOI) drive the capability of the SVM model in classifying auditory brain response in high-performance prediction metrics and possible reliability in the healthcare setting application. © 2022, Springer Nature Switzerland AG.
format	Conference or Workshop Item
author	Bakar, Abdul Rauf A. Lai, Khin Wee Hamzaid, Nur Azah
author_facet	Bakar, Abdul Rauf A. Lai, Khin Wee Hamzaid, Nur Azah
author_sort	Bakar, Abdul Rauf A.
title	Formulation of sensor ranking associated in categorical perception: a pilot study using machine learning
title_short	Formulation of sensor ranking associated in categorical perception: a pilot study using machine learning
title_full	Formulation of sensor ranking associated in categorical perception: a pilot study using machine learning
title_fullStr	Formulation of sensor ranking associated in categorical perception: a pilot study using machine learning
title_full_unstemmed	Formulation of sensor ranking associated in categorical perception: a pilot study using machine learning
title_sort	formulation of sensor ranking associated in categorical perception: a pilot study using machine learning
publisher	Springer Science and Business Media Deutschland GmbH
publishDate	2022
url	http://eprints.um.edu.my/43404/ https://www.scopus.com/inward/record.uri?eid=2-s2.0-85129321333&doi=10.1007%2f978-3-030-90724-2_1&partnerID=40&md5=c9d95432d0832a41454066d8d1040816
_version_	1781777956513775616
score	13.211869

Formulation of sensor ranking associated in categorical perception: a pilot study using machine learning

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