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The Classification of Hallucination: The Identification of Significant Time-Domain EEG Signals

Electroencephalogram (EEG) has now become one of the means in the medical sector to detect hallucination. The main objective of this study is to classify the onset of hallucination via time-domain based EEG signals. In this study, significant time-domain features were identified to determine the bes...

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Main Authors: Chin, Hau Lim, Mahendra Kumar, Jothi Letchumy, Rashid, Mamunur, Musa, Rabiu Muazu, Mohd Azraai, Mohd Razman, Norizam, Sulaiman, Rozita, Jailani, Anwar, P. P. Abdul Majeed
Format: Conference or Workshop Item
Language:English
Published: Springer 2022
Subjects:
TK Electrical engineering. Electronics Nuclear engineering
TS Manufactures
Online Access:http://umpir.ump.edu.my/id/eprint/33336/1/The%20classification%20of%20hallucination%20-%20the%20identification%20of%20significant.pdf
http://umpir.ump.edu.my/id/eprint/33336/
https://doi.org/10.1007/978-981-33-4597-3_90
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spelling my.ump.umpir.333362023-12-19T03:34:58Z http://umpir.ump.edu.my/id/eprint/33336/ The Classification of Hallucination: The Identification of Significant Time-Domain EEG Signals Chin, Hau Lim Mahendra Kumar, Jothi Letchumy Rashid, Mamunur Musa, Rabiu Muazu Mohd Azraai, Mohd Razman Norizam, Sulaiman Rozita, Jailani Anwar, P. P. Abdul Majeed TK Electrical engineering. Electronics Nuclear engineering TS Manufactures Electroencephalogram (EEG) has now become one of the means in the medical sector to detect hallucination. The main objective of this study is to classify the onset of hallucination via time-domain based EEG signals. In this study, significant time-domain features were identified to determine the best features that could yield high classification accuracy (CA) on different classifiers. Emotiv Insight, a 5 channels headset, was used to record the EEG signal of 5 subjects aged between 23 and 27 years old when they are in a hallucination state. Eight statistical-based features, i.e., mean, standard deviation, variance, median, minimum, maximum, kurtosis, skewness and standard error mean from each channel. The identification of the significant features is obtained via Extremely Randomised Trees. The classification performance of all features, as well as selected features, are evaluated through, i.e. Random Forest (RF), k-Nearest Neighbours (k-NN), Naïve Bayes (NB), Support Vector Machine (SVM), Artificial Neural Network (ANN) and Logistic Regression (LR). The dataset was separated into the ratio of 70:30 for training and testing data. It was shown from the study, that the LR classifier is able to provide excellent CA on both the train and test dataset by considering the identified significant features. The identification of such features is non-trivial towards classifying the onset of hallucination in real-time as the computational expense could be significantly reduced. Springer 2022 Conference or Workshop Item PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/33336/1/The%20classification%20of%20hallucination%20-%20the%20identification%20of%20significant.pdf Chin, Hau Lim and Mahendra Kumar, Jothi Letchumy and Rashid, Mamunur and Musa, Rabiu Muazu and Mohd Azraai, Mohd Razman and Norizam, Sulaiman and Rozita, Jailani and Anwar, P. P. Abdul Majeed (2022) The Classification of Hallucination: The Identification of Significant Time-Domain EEG Signals. In: Lecture Notes in Electrical Engineering; Innovative Manufacturing, Mechatronics and Materials Forum, iM3F 2020 , 6 August 2020 , Gambang, Kuantan. pp. 989-997., 730. ISSN 1876-1100 ISBN 978-981334596-6 https://doi.org/10.1007/978-981-33-4597-3_90
institution Universiti Malaysia Pahang Al-Sultan Abdullah
building UMPSA Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Pahang Al-Sultan Abdullah
content_source UMPSA Institutional Repository
url_provider http://umpir.ump.edu.my/
language English
topic TK Electrical engineering. Electronics Nuclear engineering
TS Manufactures
spellingShingle TK Electrical engineering. Electronics Nuclear engineering
TS Manufactures
Chin, Hau Lim
Mahendra Kumar, Jothi Letchumy
Rashid, Mamunur
Musa, Rabiu Muazu
Mohd Azraai, Mohd Razman
Norizam, Sulaiman
Rozita, Jailani
Anwar, P. P. Abdul Majeed
The Classification of Hallucination: The Identification of Significant Time-Domain EEG Signals
description Electroencephalogram (EEG) has now become one of the means in the medical sector to detect hallucination. The main objective of this study is to classify the onset of hallucination via time-domain based EEG signals. In this study, significant time-domain features were identified to determine the best features that could yield high classification accuracy (CA) on different classifiers. Emotiv Insight, a 5 channels headset, was used to record the EEG signal of 5 subjects aged between 23 and 27 years old when they are in a hallucination state. Eight statistical-based features, i.e., mean, standard deviation, variance, median, minimum, maximum, kurtosis, skewness and standard error mean from each channel. The identification of the significant features is obtained via Extremely Randomised Trees. The classification performance of all features, as well as selected features, are evaluated through, i.e. Random Forest (RF), k-Nearest Neighbours (k-NN), Naïve Bayes (NB), Support Vector Machine (SVM), Artificial Neural Network (ANN) and Logistic Regression (LR). The dataset was separated into the ratio of 70:30 for training and testing data. It was shown from the study, that the LR classifier is able to provide excellent CA on both the train and test dataset by considering the identified significant features. The identification of such features is non-trivial towards classifying the onset of hallucination in real-time as the computational expense could be significantly reduced.
format Conference or Workshop Item
author Chin, Hau Lim
Mahendra Kumar, Jothi Letchumy
Rashid, Mamunur
Musa, Rabiu Muazu
Mohd Azraai, Mohd Razman
Norizam, Sulaiman
Rozita, Jailani
Anwar, P. P. Abdul Majeed
author_facet Chin, Hau Lim
Mahendra Kumar, Jothi Letchumy
Rashid, Mamunur
Musa, Rabiu Muazu
Mohd Azraai, Mohd Razman
Norizam, Sulaiman
Rozita, Jailani
Anwar, P. P. Abdul Majeed
author_sort Chin, Hau Lim
title The Classification of Hallucination: The Identification of Significant Time-Domain EEG Signals
title_short The Classification of Hallucination: The Identification of Significant Time-Domain EEG Signals
title_full The Classification of Hallucination: The Identification of Significant Time-Domain EEG Signals
title_fullStr The Classification of Hallucination: The Identification of Significant Time-Domain EEG Signals
title_full_unstemmed The Classification of Hallucination: The Identification of Significant Time-Domain EEG Signals
title_sort classification of hallucination: the identification of significant time-domain eeg signals
publisher Springer
publishDate 2022
url http://umpir.ump.edu.my/id/eprint/33336/1/The%20classification%20of%20hallucination%20-%20the%20identification%20of%20significant.pdf
http://umpir.ump.edu.my/id/eprint/33336/
https://doi.org/10.1007/978-981-33-4597-3_90
_version_ 1822923953398611968
score 13.235318

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