Authors
Kenta Tomonaga, Takuya Hayakawa, Jun Kobayashi
Corresponding Author
Kenta Tomonaga
Available Online 1 September 2017.
DOI
https://doi.org/10.2991/jrnal.2017.4.2.4How to use a DOI?
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.

Copyright
© 2013, the Authors. Published by ALife Robotics Corp. Ltd.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).