Self-paced emotional imagery-based brain computer interface system
© Springer Nature Singapore Pte Ltd. 2018. This paper proposed a self-paced emotional imagery based noninvasive Brain-computer interface (BCI) system. Electroencephalography (EEG) was used to observe brain phenomenon and regions during imagery positive and negative emotions. Absolute power at peak f...
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th-mahidol.454482019-08-23T18:09:54Z Self-paced emotional imagery-based brain computer interface system Yunyong Punsawad Yodchanan Wongsawat Silpakorn University Mahidol University Chemical Engineering Engineering © Springer Nature Singapore Pte Ltd. 2018. This paper proposed a self-paced emotional imagery based noninvasive Brain-computer interface (BCI) system. Electroencephalography (EEG) was used to observe brain phenomenon and regions during imagery positive and negative emotions. Absolute power at peak frequency of EEG bands from quantitative EEG analysis was used to create a parameter two classes of emotion by using Linear discriminant analysis (LDA) classifier. The study of brain response via EEG supports previously proposed EEG-based emotion recognition. The results showed the proposed algorithms achieved averaged accuracy rate of 53.3–83.3%. The proposed system can be used for real-time BCI. The aim is an assistive devices and emotion monitoring based on BCI that can practically use in clinical applications. 2019-08-23T10:46:32Z 2019-08-23T10:46:32Z 2018-01-01 Conference Paper IFMBE Proceedings. Vol.63, (2018), 567-571 10.1007/978-981-10-4361-1_97 16800737 2-s2.0-85030847489 https://repository.li.mahidol.ac.th/handle/123456789/45448 Mahidol University SCOPUS https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85030847489&origin=inward |
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Chemical Engineering Engineering Yunyong Punsawad Yodchanan Wongsawat Self-paced emotional imagery-based brain computer interface system |
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© Springer Nature Singapore Pte Ltd. 2018. This paper proposed a self-paced emotional imagery based noninvasive Brain-computer interface (BCI) system. Electroencephalography (EEG) was used to observe brain phenomenon and regions during imagery positive and negative emotions. Absolute power at peak frequency of EEG bands from quantitative EEG analysis was used to create a parameter two classes of emotion by using Linear discriminant analysis (LDA) classifier. The study of brain response via EEG supports previously proposed EEG-based emotion recognition. The results showed the proposed algorithms achieved averaged accuracy rate of 53.3–83.3%. The proposed system can be used for real-time BCI. The aim is an assistive devices and emotion monitoring based on BCI that can practically use in clinical applications. |
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Silpakorn University |
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Silpakorn University Yunyong Punsawad Yodchanan Wongsawat |
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Conference or Workshop Item |
author |
Yunyong Punsawad Yodchanan Wongsawat |
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Yunyong Punsawad |
title |
Self-paced emotional imagery-based brain computer interface system |
title_short |
Self-paced emotional imagery-based brain computer interface system |
title_full |
Self-paced emotional imagery-based brain computer interface system |
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Self-paced emotional imagery-based brain computer interface system |
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Self-paced emotional imagery-based brain computer interface system |
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self-paced emotional imagery-based brain computer interface system |
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2019 |
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https://repository.li.mahidol.ac.th/handle/123456789/45448 |
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