Authors
Takuya Hayakawa, Jun [email protected]
Department of Systems Design and Informatics, Kyushu Institute of Technology,
Kawazu 680-4, Iizuka, 820-8502, Japan
lab.jkoba.net
Available Online 30 June 2018.
DOI
https://doi.org/10.2991/jrnal.2018.5.1.10
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 ALife Robotics Corp. Ltd.
Open Access
This is an open access article under the CC BY-NC license (http://creativecommons.org/licences/by-nc/4.0/).