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

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.

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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.

Item Type: Conference or Workshop Item (Paper)
Funders: RU Geran, Universiti Malaya [Grant No: ST005-2020], Jabatan Perkhidmatan Awam Malaysia
Uncontrolled Keywords: Audition; Electrodes; Electroencephalography; Electrophysiology; Health care; Hearing aids; Categorical perception; Cortical auditory evoked potential; Electroencephalography; Machine-learning; Pilot studies; Sensor rankings; Support vector machine; Support vectors machine; Task-processing; Training dataset; Support vector machines
Subjects: T Technology > TA Engineering (General). Civil engineering (General)
Divisions: Faculty of Engineering > Biomedical Engineering Department
Depositing User: Ms. Juhaida Abd Rahim
Date Deposited: 03 Nov 2023 06:53
Last Modified: 03 Nov 2023 06:53

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