Improving EEG-based BCI Neural Networks for Mobile Robot Control by Bayesian Optimization
- Takuya Hayakawa, Jun Kobayashijkoba@ces.kyutech.ac.jpDepartment of Systems Design and Informatics, Kyushu Institute of Technology, Kawazu 680-4, Iizuka, 820-8502, Japan
- https://doi.org/10.2991/jrnal.2018.5.1.10How to use a DOI?
- brain computer interface, electroencephalography, neural network, hyperparameters, Bayesian optimization, mobile robot control
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 © 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 -