Experiments on Classification of Electroencephalography (EEG) Signals in Imagination of Direction using Stacked Autoencoder

DOI

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 Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - JOUR
AU  - Kenta Tomonaga
AU  - Takuya Hayakawa
AU  - Jun Kobayashi
PY  - 2017
DA  - 2017/09/01
TI  - Experiments on Classification of Electroencephalography (EEG) Signals in Imagination of Direction using Stacked Autoencoder
JO  - Journal of Robotics, Networking and Artificial Life
SP  - 124
EP  - 128
VL  - 4
IS  - 2
SN  - 2352-6386
UR  - https://doi.org/10.2991/jrnal.2017.4.2.4
DO  - 10.2991/jrnal.2017.4.2.4
ID  - Tomonaga2017
ER  -