TY - GEN
T1 - The Classification of Hallucination
T2 - Innovative Manufacturing, Mechatronics and Materials Forum, iM3F 2020
AU - Lim, Chin Hau
AU - Mahendra Kumar, Jothi Letchumy
AU - Rashid, Mamunur
AU - Musa, Rabiu Muazu
AU - Mohd Razman, Mohd Azraai
AU - Sulaiman, Norizam
AU - Jailani, Rozita
AU - P. P. Abdul Majeed, Anwar
N1 - Funding Information:
We would like to gratefully acknowledge Universiti Malaysia Pahang for supporting the present study [RDU180321].
Publisher Copyright:
© 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
PY - 2022
Y1 - 2022
N2 - 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.
AB - 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.
KW - Classification
KW - EEG
KW - Hallucination
KW - Machine learning
UR - http://www.scopus.com/inward/record.url?scp=85112549676&partnerID=8YFLogxK
U2 - 10.1007/978-981-33-4597-3_90
DO - 10.1007/978-981-33-4597-3_90
M3 - Conference Proceeding
AN - SCOPUS:85112549676
SN - 9789813345966
T3 - Lecture Notes in Electrical Engineering
SP - 989
EP - 997
BT - Recent Trends in Mechatronics Towards Industry 4.0 - Selected Articles from iM3F 2020
A2 - Ab. Nasir, Ahmad Fakhri
A2 - Ibrahim, Ahmad Najmuddin
A2 - Ishak, Ismayuzri
A2 - Mat Yahya, Nafrizuan
A2 - Zakaria, Muhammad Aizzat
A2 - P. P. Abdul Majeed, Anwar
PB - Springer Science and Business Media Deutschland GmbH
Y2 - 6 August 2020 through 6 August 2020
ER -