Human-Wants Detection Based on Electroencephalogram Analysis During Exposure to Music
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- Ito Shin-ichi
- Tokushima University
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- Ito Momoyo
- Tokushima University
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- Fukumi Minoru
- Tokushima University
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説明
<p>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.</p>
収録刊行物
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- Journal of Robotics and Mechatronics
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Journal of Robotics and Mechatronics 32 (4), 724-730, 2020-08-20
富士技術出版株式会社
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詳細情報 詳細情報について
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- CRID
- 1390566775159902208
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- NII論文ID
- 130007888803
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- NII書誌ID
- AA10809998
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- ISSN
- 18838049
- 09153942
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- NDL書誌ID
- 030578966
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- 本文言語コード
- en
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- データソース種別
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- JaLC
- IRDB
- NDL
- Crossref
- CiNii Articles
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- 抄録ライセンスフラグ
- 使用不可