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Neural network based BCI by using orthogonal components of multi-channel brain waves and generalization
http://hdl.handle.net/2297/18078
http://hdl.handle.net/2297/18078759491cb-192a-42cf-b40d-8b81444e0009
| 名前 / ファイル | ライセンス | アクション |
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TE-PR-NAKAYAMA-K-879.pdf (367.4 kB)
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| Item type | 会議発表論文 / Conference Paper(1) | |||||
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| 公開日 | 2017-10-03 | |||||
| タイトル | ||||||
| タイトル | Neural network based BCI by using orthogonal components of multi-channel brain waves and generalization | |||||
| 言語 | ||||||
| 言語 | eng | |||||
| 資源タイプ | ||||||
| 資源タイプ識別子 | http://purl.org/coar/resource_type/c_5794 | |||||
| 資源タイプ | conference paper | |||||
| 著者 |
Nakayama, Kenji
× Nakayama, Kenji× Horita, Hiroki× Hirano, Akihiro |
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| 提供者所属 | ||||||
| 内容記述タイプ | Other | |||||
| 内容記述 | 金沢大学理工研究域 電子情報学系 | |||||
| 書誌情報 |
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 巻 5164 LNCS, Issue PART 2, 号 2008, p. 879-888, 発行日 2008-09-01 |
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| ISSN | ||||||
| 収録物識別子タイプ | ISSN | |||||
| 収録物識別子 | 0302-9743 | |||||
| NCID | ||||||
| 収録物識別子タイプ | NCID | |||||
| 収録物識別子 | AA0071599X | |||||
| DOI | ||||||
| 関連タイプ | isVersionOf | |||||
| 識別子タイプ | DOI | |||||
| 関連識別子 | 10.1007/978-3-540-87559-8_91 | |||||
| 出版者 | ||||||
| 出版者 | Springer Verlag | |||||
| 抄録 | ||||||
| 内容記述タイプ | Abstract | |||||
| 内容記述 | FFT and Multilayer neural networks (MLNN) have been applied to 'Brain Computer Interface' (BCI). In this paper, in order to extract features of mental tasks, individual feature of brain waves of each channel is emphasized. Since the brain wave in some interval can be regarded as a vector, Gram-Schmidt orthogonalization is applied for this purpose. There exists degree of freedom in the channel order to be orthogonalized. Effect of the channel order on classification accuracy is investigated. Next, two channel orders are used for generating the MLNN input data. Two kinds of methods using a single NN and double NNs are examined. Furthermore, a generalization method, adding small random numbers to the MLNN input data, is applied. Simulations are carried out by using the brain waves, available from the Colorado State University website. By using the orthogonal components, a correct classification rate P c can be improved from 70% to 78%, an incorrect classification rate P e can be suppressed from 10% to 8%. As a result, a rate R c ∈=∈P c /(P c ∈+∈P e ) can be improved from 0.875 to 0.907. When two different channel orders are used, P e can be drastically suppressed from 10% to 2%, and R c can be improved up to 0.973. The generalization method is useful especially for using a sigle channel order. P c can be increased up to 84~88% and P e can be suppressed down to 2~4%, resulting in R c ∈=∈0.957~0.977. © 2008 Springer-Verlag Berlin Heidelberg. | |||||
| 著者版フラグ | ||||||
| 出版タイプ | AM | |||||
| 出版タイプResource | http://purl.org/coar/version/c_ab4af688f83e57aa | |||||
