| Title: | GS5-1 Improving EEG-based BCI Neural Networks for Mobile Robot Control by Bayesian Optimization |
|---|---|
| Publication: | ICAROB2018 |
| Volume: | 23 |
| Pages: | 120-123 |
| ISSN: | 2188-7829 |
| DOI: | 10.5954/ICAROB.2018.GS5-1 |
| Author(s): | Takuya Hayakawa, Jun Kobayashi |
| Publication Date: | February 2, 2018 |
| 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. |
| PDF File: | https://alife-robotics.co.jp/members2018/icarob/data/html/data/GS_pdf/GS5/GS5-1.pdf |
| Copyright: | © The authors. This article is distributed under the terms of the Creative Commons Attribution License 4.0, which permits non-commercial use, distribution and reproduction in any medium, provided the original work is properly cited. See for details: https://creativecommons.org/licenses/by-nc/4.0/ |
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