Journal of Robotics, Networking and Artificial Life

Volume 5, Issue 1, June 2018, Pages 41 - 44

Improving EEG-based BCI Neural Networks for Mobile Robot Control by Bayesian Optimization

Authors
Takuya Hayakawa, Jun Kobayashijkoba@ces.kyutech.ac.jp
Department of Systems Design and Informatics, Kyushu Institute of Technology, Kawazu 680-4, Iizuka, 820-8502, Japan
Available Online 30 June 2018.
DOI
https://doi.org/10.2991/jrnal.2018.5.1.10How to use a DOI?
Keywords
brain computer interface, electroencephalography, neural network, hyperparameters, Bayesian optimization, mobile robot control
Abstract

The aim of this study is to improve classification performance of neural networks as an EEG-based BCI for mobile robot control by means of hyperparameter optimization in training the neural networks. The hyperparameters were intuitively decided in our preceding study. It is expected that the classification performance will improve if you determine the hyperparameters in a more appropriate way. Therefore, the authors have applied Bayesian optimization to training the EEG-based BCI neural networks and achieved the performance improvement.

Copyright
Copyright © 2018, the Authors. Published by Atlantis Press.
Open Access
This is an open access article under the CC BY-NC license (http://creativecommons.org/licences/by-nc/4.0/).

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Journal
Journal of Robotics, Networking and Artificial Life
Volume-Issue
5 - 1
Pages
41 - 44
Publication Date
2018/06
ISSN (Online)
2352-6386
ISSN (Print)
2405-9021
DOI
https://doi.org/10.2991/jrnal.2018.5.1.10How to use a DOI?
Copyright
Copyright © 2018, the Authors. Published by Atlantis Press.
Open Access
This is an open access article under the CC BY-NC license (http://creativecommons.org/licences/by-nc/4.0/).

Cite this article

TY  - JOUR
AU  - Takuya Hayakawa
AU  - Jun Kobayashi
PY  - 2018
DA  - 2018/06
TI  - Improving EEG-based BCI Neural Networks for Mobile Robot Control by Bayesian Optimization
JO  - Journal of Robotics, Networking and Artificial Life
SP  - 41
EP  - 44
VL  - 5
IS  - 1
SN  - 2352-6386
UR  - https://doi.org/10.2991/jrnal.2018.5.1.10
DO  - https://doi.org/10.2991/jrnal.2018.5.1.10
ID  - Hayakawa2018
ER  -