Hybrid mean fuzzy approach for attention detection

Mohd Nasir, Haslinah and Mohamed Aminuddin, Mai Mariam and Brahin, Noor Mohd Ariff and Mispan, Mohd Syafiq (2021) Hybrid mean fuzzy approach for attention detection. International Journal Of Online And Biomedical Engineering, 17 (6). pp. 58-72. ISSN 2626-8493

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Abstract

Statistics around the world showed that attention deficit significantly leads to road accidents. Hence, the growth of studies on attention deficit detection becoming more important. The studies obtained the waveform from electroencephalography (EEG) to identify the characteristic of attention. However, each individual has own unique characteristics to significantly shown the attention deficit. Thus, this research aim is to use the fuzzy approach to minimize the variability gap of the EEG signal between each individual. The research conducted the prior experiment to develop control parameter for training set of fuzzy by using two distinct stimulations to create two groups of attention sample i.e., attentive and inattentive. An approach of novel Hybrid Mean Fuzzy (HMF) was proposed in this research to detect attention deficit in EEG signal. It is the combination of simple averaging (Mean) and Fuzzy approaches for EEG analysis and classification. The results of using this method shows a significantly change in EEG signal which correlates to the attention detection. An Attention Degradation Scale (ADS) is successfully developed as the threshold value of EEG for attention detection. Therefore, the findings in this research can be a promising foundation on attention deficit detection in large application not only for reducing the road accidents.

Item Type:	Article
Uncontrolled Keywords:	Biomedical signal processing, Encephalography, Fuzzy, Hybrid intelligence system, Simple averaging
Divisions:	Faculty of Electrical and Electronic Engineering Technology
Depositing User:	Sabariah Ismail
Date Deposited:	16 Mar 2022 15:47
Last Modified:	25 Jul 2023 15:04
URI:	http://eprints.utem.edu.my/id/eprint/25759
Statistic Details:	View Download Statistic

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