Hand and Foot Movement of Motor Imagery Classification Using Wavelet Packet Decomposition and Multilayer Perceptron Backpropagation
- 10.2991/aer.k.210810.042How to use a DOI?
- EEG, Motor Imagery, Wavelet Packet Decomposition, MLP-BP
The development of bionic aids for paralyzed patients leads to the Brain-Computer Interface (BCI) implementation with various obstacles, especially in interpreting brain signals as triggers for the bionic organ. The reading of electrical signal activity in the brain in the BCI system uses electroencephalography (EEG) signal, which comes from many electrodes in the head area and is non-stationary. The measured EEG signal contains much information, including information for the hands and feet motor imagery, so a classification system is needed to separate the information to be processed, such as hand and foot movements. This research aims to develop an imagery motor classification system for the hands and feet so that signals can be classified correctly. The system design is made through several stages of the signal processing process consisting of the pre-processing stage using centering, the feature extraction stage with wavelet packet decomposition (WPD), and multilayer perceptron back-propagation (MLP-BP) as the classifier. Based on the result, this study got the highest accuracy value, about 26.8% at level three, and gain above 0.02. This small accuracy is due to the large error due to under fitting.
- © 2021, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - CONF AU - Prasetyo Cahyo Nugroho AU - Rahmat Widadi AU - Dodi Zulherman PY - 2021 DA - 2021/08/11 TI - Hand and Foot Movement of Motor Imagery Classification Using Wavelet Packet Decomposition and Multilayer Perceptron Backpropagation BT - Proceedings of the 2nd Borobudur International Symposium on Science and Technology (BIS-STE 2020) PB - Atlantis Press SP - 248 EP - 252 SN - 2352-5401 UR - https://doi.org/10.2991/aer.k.210810.042 DO - 10.2991/aer.k.210810.042 ID - Nugroho2021 ER -