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Correlation Analysis of Features for Fusing in User Verification Using EEG Evoked by Ultrasound
https://repository.lib.tottori-u.ac.jp/records/7054
https://repository.lib.tottori-u.ac.jp/records/70540130dfd5-772b-4808-8404-9f3b35ac20d6
| 名前 / ファイル | ライセンス | アクション |
|---|---|---|
CANDARW2022_388.pdf (376.7 kB)
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| Item type | 会議発表論文 / Conference Paper(1) | |||||
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| 公開日 | 2022-12-07 | |||||
| タイトル | ||||||
| タイトル | Correlation Analysis of Features for Fusing in User Verification Using EEG Evoked by Ultrasound | |||||
| 言語 | en | |||||
| 言語 | ||||||
| 言語 | eng | |||||
| キーワード | ||||||
| 主題 | biometrics, evoked brain wave, reduction of classifiers, statistical values of brain waves, support vector machine, ultrasound biometrics(en), evoked brain wave(en), reduction of classifiers(en), statistical values of brain waves(en), support vector machine(en), ultrasound(en) |
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| 資源タイプ | ||||||
| 資源タイプ | conference paper | |||||
| 著者 |
中西, 功
× 中西, 功× Ishikawa, Yuta× Mukai, Kotaro |
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| 著者所属（英） | ||||||
| 言語 | en | |||||
| 値 | Faculty of Engineering Tottori University | |||||
| 著者所属（英） | ||||||
| 言語 | en | |||||
| 値 | School of Engineering Tottori University | |||||
| 著者所属（英） | ||||||
| 言語 | en | |||||
| 値 | Graduate School of Sustainability Sciences Tottori University | |||||
| 抄録 | ||||||
| 内容記述タイプ | Other | |||||
| 内容記述 | In user verification using electroencephalograms (EEGs) evoked by ultrasound, an error rate of 0% was achieved. However, to achieve this, the classifiers for the number of features multiplied by the number of electrodes must be learned. Therefore, reducing the number of classifiers is crucial and must be achieved. This study confirmed that the random selection of features and electrodes facilitates further reduction in the number of classifiers. Random selection is equivalent to evenly selecting electrodes for each feature and electrode position. Consequently, the effectiveness of even selection was statistically confirmed. Furthermore, even selection resulted in the fusion of uncorrelated features. Thus, four statistical values of an EEG were introduced, and the effectiveness of fusing uncorrelated (independent) features was confirmed. | |||||
| 書誌情報 |
Proceedings of 2022 tenth International Symposium on Computing and Networking Workshops(CANDARW) en : Proceedings of 2022 tenth International Symposium on Computing and Networking Workshops(CANDARW) p. 388-391, 発行日 2022 |
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| 出版者 | ||||||
| 出版者 | IEEE | |||||
| 権利 | ||||||
| 権利情報 | © 2022 IEEE. | |||||
| 情報源 | ||||||
| 関連名称 | I Nakanishi, Y Ishikawa, K Mukai. Correlation Analysis of Features for Fusing in User Verification Using EEG Evoked by Ultrasound. Proceedings of 2022 tenth International Symposium on Computing and Networking Workshops(CANDARW). 2022, 388-391. | |||||
| 関連サイト | ||||||
| 識別子タイプ | URI | |||||
| 関連識別子 | https://www.computer.org/csdl/proceedings/1829704 | |||||
| 関連名称 | https://www.computer.org/csdl/proceedings/1829704 | |||||
| 著者版フラグ | ||||||
| 出版タイプ | AM | |||||
