@inproceedings{oai:kyutech.repo.nii.ac.jp:00005651,
 author = {Tomonaga,  Kenta and Hayakawa,  Takuya and Kobayashi, Jun and 小林, 順},
 book = {Journal of Robotics, Networking and Artificial Life},
 issue = {2},
 month = {Jan},
 note = {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., 2017 International Conference on Artificial Life and Robotics(ICAROB 2017) , January 19 to 22, 2017, Seagaia Convention Center, Miyazaki, Japan.},
 pages = {124--128},
 publisher = {Atlantis Press},
 title = {Experiments on Classification of Electroencephalography (EEG) Signals in Imagination of Direction using Stacked Autoencoder},
 volume = {4},
 year = {2017},
 yomi = {コバヤシ, ジュン}
}