| Title: | GS1-3 Experiments on classification of electroencephalography (EEG) signals in imagination of direction using Stacked Autoencoder |
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| Publication: | ICAROB2017 |
| Volume: | 22 |
| Pages: | 468-471 |
| ISSN: | 2188-7829 |
| DOI: | 10.5954/ICAROB.2017.GS1-3 |
| Author(s): | Kenta Tomonaga, Takuya Hayakawa, Jun Kobayashi |
| Publication Date: | January 19, 2017 |
| Keywords: | electroencephalography, stacked autoencoder, neural network, portable EEG headset, imagination of direction |
| Abstract: | 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. |
| PDF File: | https://alife-robotics.co.jp/members2017/icarob/data/html/data/GS_pdf/GS1/GS1-3.pdf |
| Copyright: | © The authors. This article is distributed under the terms of the Creative Commons Attribution License 4.0, which permits non-commercial use, distribution and reproduction in any medium, provided the original work is properly cited. See for details: https://creativecommons.org/licenses/by-nc/4.0/ |
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