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Electroencephalogram Analysis Method to Detect Unspoken Answers to Questions Using Multistage Neural Networks
| 名前 / ファイル | ライセンス | アクション |
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access_11_137151.pdf (2.15 MB)
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| Item type | 文献 / Documents(1) | |||||||||||||||||||||||
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| 公開日 | 2023-12-22 | |||||||||||||||||||||||
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| アクセス権 | open access | |||||||||||||||||||||||
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| 資源タイプ識別子 | http://purl.org/coar/resource_type/c_6501 | |||||||||||||||||||||||
| 資源タイプ | journal article | |||||||||||||||||||||||
| 出版社版DOI | ||||||||||||||||||||||||
| 関連識別子 | https://doi.org/10.1109/ACCESS.2023.3339665 | |||||||||||||||||||||||
| 関連名称 | 10.1109/ACCESS.2023.3339665 | |||||||||||||||||||||||
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| 出版タイプ | VoR | |||||||||||||||||||||||
| 出版タイプResource | http://purl.org/coar/version/c_970fb48d4fbd8a85 | |||||||||||||||||||||||
| タイトル | ||||||||||||||||||||||||
| タイトル | Electroencephalogram Analysis Method to Detect Unspoken Answers to Questions Using Multistage Neural Networks | |||||||||||||||||||||||
| タイトル別表記 | ||||||||||||||||||||||||
| その他のタイトル | EEG Analysis Method to Detect Unspoken Answers to Questions Using MSNNs | |||||||||||||||||||||||
| 著者 |
伊藤, 伸一
× 伊藤, 伸一
WEKO
373
× 伊藤, 桃代
WEKO
421
× 福見, 稔
WEKO
96
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| 内容記述 | Brain–computer interfaces (BCI) facilitate communication between the human brain and computational systems, additionally offering mechanisms for environmental control to enhance human life. The current study focused on the application of BCI for communication support, especially in detecting unspoken answers to questions. Utilizing a multistage neural network (MSNN) replete with convolutional and pooling layers, the proposed method comprises a threefold approach: electroencephalogram (EEG) measurements, EEG feature extraction, and answer classification. The EEG signals of the participants are captured as they mentally respond with “yes” or “no” to the posed questions. Feature extraction was achieved through an MSNN composed of three distinct convolutional neural network models. The first model discriminates between the EEG signals with and without discernible noise artifacts, whereas the subsequent two models are designated for feature extraction from EEG signals with or without such noise artifacts. Furthermore, a support vector machine is employed to classify the answers to the questions. The proposed method was validated via experiments using authentic EEG data. The mean and standard deviation values for sensitivity and precision of the proposed method were 99.6% and 0.2%, respectively. These findings demonstrate the viability of attaining high accuracy in a BCI by preliminarily segregating the EEG signals based on the presence or absence of artifact noise and underscore the stability of such classification. Thus, the proposed method manifests prospective advantages of separating EEG signals characterized by noise artifacts for enhanced BCI performance. | |||||||||||||||||||||||
| キーワード | ||||||||||||||||||||||||
| 主題 | Answer to question | |||||||||||||||||||||||
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| 主題 | convolutional neural networks | |||||||||||||||||||||||
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| 主題 | electroencephalogram | |||||||||||||||||||||||
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| 主題 | multistage neural networks | |||||||||||||||||||||||
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| 主題 | personal model | |||||||||||||||||||||||
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| 主題 | support vector machine | |||||||||||||||||||||||
| 書誌情報 |
en : IEEE Access 巻 11, p. 137151-137162, 発行日 2023-12-05 |
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| 収録物識別子タイプ | ISSN | |||||||||||||||||||||||
| 収録物識別子 | 21693536 | |||||||||||||||||||||||
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| 出版者 | IEEE | |||||||||||||||||||||||
| 権利情報 | ||||||||||||||||||||||||
| 権利情報 | This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. For more information, see https://creativecommons.org/licenses/by-nc-nd/4.0/ | |||||||||||||||||||||||
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| 識別子 | 404272 | |||||||||||||||||||||||
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| 言語 | eng | |||||||||||||||||||||||
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Cite as
伊藤, 伸一, 伊藤, 桃代, 福見, 稔, 2023, Electroencephalogram Analysis Method to Detect Unspoken Answers to Questions Using Multistage Neural Networks: IEEE, 137151–137162 p.
