Journal of Robotics, Networking and Artificial Life

Volume 4, Issue 2, August 2017, Pages 124 - 128

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

Authors
Kenta Tomonaga, Takuya Hayakawa, Jun Kobayashi
Corresponding Author
Kenta Tomonaga
Available Online 1 August 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.
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This is an open access article distributed under the CC BY-NC license.

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Journal
Journal of Robotics, Networking and Artificial Life
Volume-Issue
4 - 2
Pages
124 - 128
Publication Date
2017/08
ISSN (Online)
2352-6386
ISSN (Print)
2405-9021
DOI
https://doi.org/10.2991/jrnal.2017.4.2.4How to use a DOI?
Open Access
This is an open access article distributed under the CC BY-NC license.

Cite this article

TY  - JOUR
AU  - Kenta Tomonaga
AU  - Takuya Hayakawa
AU  - Jun Kobayashi
PY  - 2017
DA  - 2017/08
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  - https://doi.org/10.2991/jrnal.2017.4.2.4
ID  - Tomonaga2017
ER  -