Volume 4, Issue 2, September 2017, Pages 124 - 128
Experiments on Classification of Electroencephalography (EEG) Signals in Imagination of Direction using Stacked Autoencoder
Kenta Tomonaga, Takuya Hayakawa, Jun Kobayashi
Available Online 1 September 2017.
- https://doi.org/10.2991/jrnal.2017.4.2.4How to use a DOI?
- electroencephalography, stacked autoencoder, neural network, portable EEG headset, imagination of direction
- 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.
- Open Access
- This is an open access article distributed under the CC BY-NC license.
Cite this article
TY - JOUR AU - Kenta Tomonaga AU - Takuya Hayakawa AU - Jun Kobayashi PY - 2017 DA - 2017/09 TI - Experiments on Classification of Electroencephalography (EEG) Signals in Imagination of Direction using Stacked Autoencoder JO - Journal of Robotics, Networking and Artificial Life SP - 124 EP - 128 VL - 4 IS - 2 SN - 2352-6386 UR - https://doi.org/10.2991/jrnal.2017.4.2.4 DO - https://doi.org/10.2991/jrnal.2017.4.2.4 ID - Tomonaga2017 ER -