Experiments on Classification of Electroencephalography (EEG) Signals in Imagination of Direction using Stacked Autoencoder
抄録
This paper presents classification methods for electroencephalography (EEG) signals in imagination of direction measured by a portable EEG headset. In the authors’ previous studies, principal component analysis extracted significant features from EEG signals to construct neural network classifiers. To improve the performance, the authors have implemented a Stacked Autoencoder (SAE) for the classification. The SAE carries out feature extraction and classification in a form of multi-layered neural network. Experimental results showed that the SAE outperformed the previous classifiers.
収録刊行物
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- Journal of Robotics, Networking and Artificial Life
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Journal of Robotics, Networking and Artificial Life 4 (2), 124-128, 2017-01-19
Atlantis Press
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キーワード
詳細情報 詳細情報について
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- CRID
- 1050855522060207360
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- NII論文ID
- 120006665057
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- ISSN
- 23526386
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- HANDLE
- 10228/00006862
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- 本文言語コード
- en
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- 資料種別
- conference paper
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- データソース種別
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- IRDB
- CiNii Articles