Emotional profiling through supervised machine learning of interrupted EEG interpolation
It has been reported that the construction of emotion profiling models using supervised machine learning involves data acquisition, signal pre-processing, feature extraction and classification. However, almost all papers do not address the issue of profiling emotion using supervised machine learning...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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ACCENTS JOURNAL
2019
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| Subjects: | |
| Online Access: | http://irep.iium.edu.my/75492/ http://irep.iium.edu.my/75492/1/6.pdf |
| _version_ | 1848788136183726080 |
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| author | Yaacob, Hamwira Sakti Omar, Hazim Handayani, Dini Hassan, Raini |
| author_facet | Yaacob, Hamwira Sakti Omar, Hazim Handayani, Dini Hassan, Raini |
| author_sort | Yaacob, Hamwira Sakti |
| building | IIUM Repository |
| collection | Online Access |
| description | It has been reported that the construction of emotion profiling models using supervised machine learning involves data acquisition, signal pre-processing, feature extraction and classification. However, almost all papers do not address the issue of profiling emotion using supervised machine learning on the interrupted encephalogram (EEG) signals. Based on a preliminary study, emotion profiling on interrupted EEG signals produces low classification accuracy, using multilayer perceptron (MLP). Furthermore, lower emotion classification accuracy is produced from interrupted EEG signals with higher number of segments. Thus, the objective of this paper is to propose a technique and present the outcomes of handling interrupted EEG signals for emotion profiling. This is done by the suppression and interpolation of originally interrupted EEG signals at pre-process stage. As a result, emotion classification using MLP on interpolated data improves from 80.1% to 95%. |
| first_indexed | 2025-11-14T17:36:01Z |
| format | Article |
| id | iium-75492 |
| institution | International Islamic University Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-14T17:36:01Z |
| publishDate | 2019 |
| publisher | ACCENTS JOURNAL |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | iium-754922022-03-04T00:19:51Z http://irep.iium.edu.my/75492/ Emotional profiling through supervised machine learning of interrupted EEG interpolation Yaacob, Hamwira Sakti Omar, Hazim Handayani, Dini Hassan, Raini BF511 Affection. Feeling. Emotion T58.5 Information technology It has been reported that the construction of emotion profiling models using supervised machine learning involves data acquisition, signal pre-processing, feature extraction and classification. However, almost all papers do not address the issue of profiling emotion using supervised machine learning on the interrupted encephalogram (EEG) signals. Based on a preliminary study, emotion profiling on interrupted EEG signals produces low classification accuracy, using multilayer perceptron (MLP). Furthermore, lower emotion classification accuracy is produced from interrupted EEG signals with higher number of segments. Thus, the objective of this paper is to propose a technique and present the outcomes of handling interrupted EEG signals for emotion profiling. This is done by the suppression and interpolation of originally interrupted EEG signals at pre-process stage. As a result, emotion classification using MLP on interpolated data improves from 80.1% to 95%. ACCENTS JOURNAL 2019-07 Article PeerReviewed application/pdf en cc_by http://irep.iium.edu.my/75492/1/6.pdf Yaacob, Hamwira Sakti and Omar, Hazim and Handayani, Dini and Hassan, Raini (2019) Emotional profiling through supervised machine learning of interrupted EEG interpolation. International Journal of Advanced Computer Research, 9 (43). pp. 242-251. ISSN 2249-7277 E-ISSN 2277-7970 https://www.accentsjournals.org/journals.php?journalsId=103 10.19101/IJACR.PID17 |
| spellingShingle | BF511 Affection. Feeling. Emotion T58.5 Information technology Yaacob, Hamwira Sakti Omar, Hazim Handayani, Dini Hassan, Raini Emotional profiling through supervised machine learning of interrupted EEG interpolation |
| title | Emotional profiling through supervised machine learning of interrupted EEG interpolation |
| title_full | Emotional profiling through supervised machine learning of interrupted EEG interpolation |
| title_fullStr | Emotional profiling through supervised machine learning of interrupted EEG interpolation |
| title_full_unstemmed | Emotional profiling through supervised machine learning of interrupted EEG interpolation |
| title_short | Emotional profiling through supervised machine learning of interrupted EEG interpolation |
| title_sort | emotional profiling through supervised machine learning of interrupted eeg interpolation |
| topic | BF511 Affection. Feeling. Emotion T58.5 Information technology |
| url | http://irep.iium.edu.my/75492/ http://irep.iium.edu.my/75492/ http://irep.iium.edu.my/75492/ http://irep.iium.edu.my/75492/1/6.pdf |