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Investigation of Time-Domain and Frequency-Domain Based Features to Classify the EEG Auditory Evoked Potentials (AEPs) Responses

The auditory evoked potentials (AEPs) are a kind of electroencephalographic (EEG) signal that is produced by an acoustic stimulus from the region of the brain. The people who are unable to maintain the verbal communication and behavioral response through the sound stimulation, EEG based brain-comput...

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Main Authors: Islam, Md Nahidul, Norizam, Sulaiman, Rashid, Mamunur, Mahfuzah, Mustafa, Mohd Shawal, Jadin
Format: Conference or Workshop Item
Language:English
English
Published: Springer 2022
Subjects:
TK Electrical engineering. Electronics Nuclear engineering
Online Access:http://umpir.ump.edu.my/id/eprint/33316/1/InvestigationofTime-Domain.pdf
http://umpir.ump.edu.my/id/eprint/33316/2/Investigation%20of%20Time-Domain%20and%20Frequency.pdf
http://umpir.ump.edu.my/id/eprint/33316/
https://doi.org/10.1007/978-981-33-4597-3_45
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spelling my.ump.umpir.333162022-02-07T04:05:28Z http://umpir.ump.edu.my/id/eprint/33316/ Investigation of Time-Domain and Frequency-Domain Based Features to Classify the EEG Auditory Evoked Potentials (AEPs) Responses Islam, Md Nahidul Norizam, Sulaiman Rashid, Mamunur Mahfuzah, Mustafa Mohd Shawal, Jadin TK Electrical engineering. Electronics Nuclear engineering The auditory evoked potentials (AEPs) are a kind of electroencephalographic (EEG) signal that is produced by an acoustic stimulus from the region of the brain. The people who are unable to maintain the verbal communication and behavioral response through the sound stimulation, EEG based brain-computer interface (BCI) technology could be an effective alternative to rehabilitate their hearing ability. In this paper, the AEP responses of three distinct English words namely bed, please and sad have been recognized. The EEG features in terms of Fast Fourier Transform (FFT), power spectral density (PSD), spectral centroids, standard deviation, Log energy entropy, mean, skewness, kurtosis has been selected as a feature extraction method. Support Vector Machine (SVM), Linear discriminant analysis (LDA) and K-Nearest Neighbors (K-NN) have been employed to classify the extracted features. Among all these features, power spectral density with SVM classification has achieved the best accuracy. Different performance measures were evaluated to identify the best set of features as well as model. The best classification accuracy was demonstrated by the developed SVM model was observed as 82.86% which clearly indicates that the method provides a very encouraging performance for detecting the AEPs responses. Springer 2022 Conference or Workshop Item PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/33316/1/InvestigationofTime-Domain.pdf pdf en http://umpir.ump.edu.my/id/eprint/33316/2/Investigation%20of%20Time-Domain%20and%20Frequency.pdf Islam, Md Nahidul and Norizam, Sulaiman and Rashid, Mamunur and Mahfuzah, Mustafa and Mohd Shawal, Jadin (2022) Investigation of Time-Domain and Frequency-Domain Based Features to Classify the EEG Auditory Evoked Potentials (AEPs) Responses. In: Recent Trends in Mechatronics Towards Industry 4.0: Selected Articles from iM3F 2020, Malaysia, 6 August 2020 , Universiti Malaysia Pahang (Virtual). pp. 497-508., 730. ISBN 978-981-33-4597-3 https://doi.org/10.1007/978-981-33-4597-3_45
institution Universiti Malaysia Pahang
building UMP Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Pahang
content_source UMP Institutional Repository
url_provider http://umpir.ump.edu.my/
language English
English
topic TK Electrical engineering. Electronics Nuclear engineering
spellingShingle TK Electrical engineering. Electronics Nuclear engineering
Islam, Md Nahidul
Norizam, Sulaiman
Rashid, Mamunur
Mahfuzah, Mustafa
Mohd Shawal, Jadin
Investigation of Time-Domain and Frequency-Domain Based Features to Classify the EEG Auditory Evoked Potentials (AEPs) Responses
description The auditory evoked potentials (AEPs) are a kind of electroencephalographic (EEG) signal that is produced by an acoustic stimulus from the region of the brain. The people who are unable to maintain the verbal communication and behavioral response through the sound stimulation, EEG based brain-computer interface (BCI) technology could be an effective alternative to rehabilitate their hearing ability. In this paper, the AEP responses of three distinct English words namely bed, please and sad have been recognized. The EEG features in terms of Fast Fourier Transform (FFT), power spectral density (PSD), spectral centroids, standard deviation, Log energy entropy, mean, skewness, kurtosis has been selected as a feature extraction method. Support Vector Machine (SVM), Linear discriminant analysis (LDA) and K-Nearest Neighbors (K-NN) have been employed to classify the extracted features. Among all these features, power spectral density with SVM classification has achieved the best accuracy. Different performance measures were evaluated to identify the best set of features as well as model. The best classification accuracy was demonstrated by the developed SVM model was observed as 82.86% which clearly indicates that the method provides a very encouraging performance for detecting the AEPs responses.
format Conference or Workshop Item
author Islam, Md Nahidul
Norizam, Sulaiman
Rashid, Mamunur
Mahfuzah, Mustafa
Mohd Shawal, Jadin
author_facet Islam, Md Nahidul
Norizam, Sulaiman
Rashid, Mamunur
Mahfuzah, Mustafa
Mohd Shawal, Jadin
author_sort Islam, Md Nahidul
title Investigation of Time-Domain and Frequency-Domain Based Features to Classify the EEG Auditory Evoked Potentials (AEPs) Responses
title_short Investigation of Time-Domain and Frequency-Domain Based Features to Classify the EEG Auditory Evoked Potentials (AEPs) Responses
title_full Investigation of Time-Domain and Frequency-Domain Based Features to Classify the EEG Auditory Evoked Potentials (AEPs) Responses
title_fullStr Investigation of Time-Domain and Frequency-Domain Based Features to Classify the EEG Auditory Evoked Potentials (AEPs) Responses
title_full_unstemmed Investigation of Time-Domain and Frequency-Domain Based Features to Classify the EEG Auditory Evoked Potentials (AEPs) Responses
title_sort investigation of time-domain and frequency-domain based features to classify the eeg auditory evoked potentials (aeps) responses
publisher Springer
publishDate 2022
url http://umpir.ump.edu.my/id/eprint/33316/1/InvestigationofTime-Domain.pdf
http://umpir.ump.edu.my/id/eprint/33316/2/Investigation%20of%20Time-Domain%20and%20Frequency.pdf
http://umpir.ump.edu.my/id/eprint/33316/
https://doi.org/10.1007/978-981-33-4597-3_45
_version_ 1724073521402871808
score 13.222552

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