ID | 116074 |
タイトル別表記 | Human-Wants Detection Based on Electroencephalogram Analysis
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著者 | |
キーワード | wants detection
electroencephalogram
listening to music
convolutional neural networks
support vector machine
brain computer interface
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資料タイプ |
学術雑誌論文
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抄録 | We propose a method to detect human wants by using an electroencephalogram (EEG) test and specifying brain activity sensing positions. EEG signals can be analyzed by using various techniques. Recently, convolutional neural networks (CNNs) have been employed to analyze EEG signals, and these analyses have produced excellent results. Therefore, this paper employs CNN to extract EEG features. Also, support vector machines (SVMs) have shown good results for EEG pattern classification. This paper employs SVMs to classify the human cognition into “wants,” “not wants,” and “other feelings”. In EEG measurements, the electrical activity of the brain is recorded using electrodes placed on the scalp. The sensing positions are related to the frontal cortex and/or temporal cortex activities although the mechanism to create wants is not clear. To specify the sensing positions and detect human wants, we conducted experiments using real EEG data. We confirmed that the mean and standard deviation values of the detection accuracy rate were 99.4% and 0.58, respectively, when the target sensing positions were related to the frontal and temporal cortex activities. These results prove that both the frontal and temporal cortex activities are relevant for creating wants in the human brain, and that CNN and SVM are effective for the detection of human wants.
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掲載誌名 |
Journal of Robotics and Mechatronics
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ISSN | 09153942
18838049
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cat書誌ID | AA10809998
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出版者 | Fuji Technology Press
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巻 | 32
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号 | 4
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開始ページ | 724
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終了ページ | 730
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発行日 | 2020-08-20
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権利情報 | This is an Open Access article distributed under the terms of the Creative Commons Attribution-NoDerivatives 4.0 International License (http://creativecommons.org/licenses/by-nd/4.0/).
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言語 |
eng
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部局 |
理工学系
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