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...
| Main Authors: | , , , , |
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| Format: | Conference or Workshop Item |
| Language: | English English |
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Springer
2022
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| Subjects: | |
| 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 |
| _version_ | 1825814186249158656 |
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| 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 |
| collection | UMP |
| 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. |
| first_indexed | 2024-03-06T12:55:06Z |
| format | Conference or Workshop Item |
| id | UMPir33316 |
| institution | Universiti Malaysia Pahang |
| language | English English |
| last_indexed | 2024-03-06T12:55:06Z |
| publishDate | 2022 |
| publisher | Springer |
| record_format | dspace |
| spelling | UMPir333162022-02-07T04:05:28Z 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 (Published) https://doi.org/10.1007/978-981-33-4597-3_45 |
| 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 |
| title | 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_short | 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 |
| topic | TK Electrical engineering. Electronics Nuclear engineering |
| 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 |
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