Emotion analysis for diagnostic of autism spectrum disorder using electroencephalogram signals / Maizura Mohd Sani … [et al.]
Learning is a lasting change in behavior that results from experience. An important element during learning is emotion. During happy time, perception is biased in selecting happy events, likewise for negative emotions. Similarly, while making decisions, human are often influenced by their affective...
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my.uitm.ir.492852021-08-26T03:06:47Z https://ir.uitm.edu.my/id/eprint/49285/ Emotion analysis for diagnostic of autism spectrum disorder using electroencephalogram signals / Maizura Mohd Sani … [et al.] Mohd Sani, Maizura Zaini, Norliza Harun, Nur Fadzilah Hamzah, Nabilah Norhazman, Haryanti Mohd Hussain, Mashitah Affection. Feeling. Emotion Emotion Learning is a lasting change in behavior that results from experience. An important element during learning is emotion. During happy time, perception is biased in selecting happy events, likewise for negative emotions. Similarly, while making decisions, human are often influenced by their affective states. Autism spectrum disorder (ASD) disease usually associated with learning disabilities among children. ASD patients have normal intelligence and can talk; however they usually misinterpret the emotion of what they have seen or felt, unlike normal children. Currently, autism diagnosing in Malaysia still needs to be performed by psychologist, psychiatrist, neurologist, developmental pediatrician, or similarly qualified medical professional. There are also no medical tests performed on the subjects, the diagnosis is made based fully on the subjects history and symptoms. An invasive method such as EEG is proven to characterize emotion of a person. The objective of this research is to diagnose ASD patient based on emotion analysis of brainwave pattern when the person being stimulate with certain emotion state using EEG signals. The analysis involved three emotions i.e. sad, happy and neutral. Using machine learning approach, the data are train both for normal and ASD patients. Comparison are made between ANN and SVM method. The testing result shows high accuracy up to 90.5% using ANN for neutral emotion. Division of Research and Industry Linkages Ismail, Shafinar Mahphoth, Mohd Halim Abas, Aemillyawaty Mohd Radzi, Fazlina Alias, Aidah Jamil, Ilinadia Hassan, Nor Yus Shahirah Shaari, Shafirah Zahari, Farihan 2017 Book Section PeerReviewed text en https://ir.uitm.edu.my/id/eprint/49285/1/49285.pdf ID49285 Mohd Sani, Maizura and Zaini, Norliza and Harun, Nur Fadzilah and Hamzah, Nabilah and Norhazman, Haryanti and Mohd Hussain, Mashitah (2017) Emotion analysis for diagnostic of autism spectrum disorder using electroencephalogram signals / Maizura Mohd Sani … [et al.]. In: Melaka International Intellectual Exposition (MIIEX 2017). Division of Research and Industry Linkages, Alor Gajah, Melaka. |
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Affection. Feeling. Emotion Emotion Mohd Sani, Maizura Zaini, Norliza Harun, Nur Fadzilah Hamzah, Nabilah Norhazman, Haryanti Mohd Hussain, Mashitah Emotion analysis for diagnostic of autism spectrum disorder using electroencephalogram signals / Maizura Mohd Sani … [et al.] |
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Learning is a lasting change in behavior that results from experience. An important element during learning is emotion. During happy time, perception is biased in selecting happy events, likewise for negative emotions. Similarly, while making decisions, human are often influenced by their affective states. Autism spectrum disorder (ASD) disease usually associated with learning disabilities among children. ASD patients have normal intelligence and can talk; however they usually misinterpret the emotion of what they have seen or felt, unlike normal children. Currently, autism diagnosing in Malaysia still needs to be performed by psychologist, psychiatrist, neurologist, developmental pediatrician, or similarly qualified medical professional. There are also no medical tests performed on the subjects, the diagnosis is made based fully on the subjects history and symptoms. An invasive method such as EEG is proven to characterize emotion of a person. The objective of this research is to diagnose ASD patient based on emotion analysis of brainwave pattern when the person being stimulate with certain emotion state using EEG signals. The analysis involved three emotions i.e. sad, happy and neutral. Using machine learning approach, the data are train both for normal and ASD patients. Comparison are made between ANN and SVM method. The testing result shows high accuracy up to 90.5% using ANN for neutral emotion. |
author2 |
Ismail, Shafinar |
author_facet |
Ismail, Shafinar Mohd Sani, Maizura Zaini, Norliza Harun, Nur Fadzilah Hamzah, Nabilah Norhazman, Haryanti Mohd Hussain, Mashitah |
format |
Book Section |
author |
Mohd Sani, Maizura Zaini, Norliza Harun, Nur Fadzilah Hamzah, Nabilah Norhazman, Haryanti Mohd Hussain, Mashitah |
author_sort |
Mohd Sani, Maizura |
title |
Emotion analysis for diagnostic of autism spectrum disorder using electroencephalogram signals / Maizura Mohd Sani … [et al.] |
title_short |
Emotion analysis for diagnostic of autism spectrum disorder using electroencephalogram signals / Maizura Mohd Sani … [et al.] |
title_full |
Emotion analysis for diagnostic of autism spectrum disorder using electroencephalogram signals / Maizura Mohd Sani … [et al.] |
title_fullStr |
Emotion analysis for diagnostic of autism spectrum disorder using electroencephalogram signals / Maizura Mohd Sani … [et al.] |
title_full_unstemmed |
Emotion analysis for diagnostic of autism spectrum disorder using electroencephalogram signals / Maizura Mohd Sani … [et al.] |
title_sort |
emotion analysis for diagnostic of autism spectrum disorder using electroencephalogram signals / maizura mohd sani … [et al.] |
publisher |
Division of Research and Industry Linkages |
publishDate |
2017 |
url |
https://ir.uitm.edu.my/id/eprint/49285/1/49285.pdf https://ir.uitm.edu.my/id/eprint/49285/ |
_version_ |
1709671410034540544 |
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13.18916 |