TELKOMNIKA Telecommunication, Computing, Electronics and Control
Brain-computer interface of focus and motor imagery using wavelet and recurrent neural networks
Dublin Core
Title
TELKOMNIKA Telecommunication, Computing, Electronics and Control
Brain-computer interface of focus and motor imagery using wavelet and recurrent neural networks
Brain-computer interface of focus and motor imagery using wavelet and recurrent neural networks
Subject
Brain-computer interface, EEG signal, Focus, Motor imagery, Recurrent neural networks, Wavelet
Description
Brain-computer interface is a technology that allows operating a device
without involving muscles and sound, but directly from the brain through the processed electrical signals. The technology works by capturing electrical or magnetic signals from the brain, which are then processed to obtain information contained therein. Usually, BCI uses information from electroencephalogram (EEG) signals based on various variables reviewed. This study proposed BCI to move external devices such as a drone simulator based on EEG signal information. From the EEG signal was extracted to get motor imagery (MI) and focus variable using wavelet. Then, they were classified by recurrent neural networks (RNN). In overcoming the problem of vanishing memory from RNN, was used long short-term memory (LSTM). The results showed that BCI used wavelet, and RNN can drive external devices of non-training data with an accuracy of 79.6%. The experiment gave AdaDelta model is better than the Adam model in terms of accuracy and value losses. Whereas in computational learning time, Adam's model is faster than AdaDelta's model.
without involving muscles and sound, but directly from the brain through the processed electrical signals. The technology works by capturing electrical or magnetic signals from the brain, which are then processed to obtain information contained therein. Usually, BCI uses information from electroencephalogram (EEG) signals based on various variables reviewed. This study proposed BCI to move external devices such as a drone simulator based on EEG signal information. From the EEG signal was extracted to get motor imagery (MI) and focus variable using wavelet. Then, they were classified by recurrent neural networks (RNN). In overcoming the problem of vanishing memory from RNN, was used long short-term memory (LSTM). The results showed that BCI used wavelet, and RNN can drive external devices of non-training data with an accuracy of 79.6%. The experiment gave AdaDelta model is better than the Adam model in terms of accuracy and value losses. Whereas in computational learning time, Adam's model is faster than AdaDelta's model.
Creator
Esmeralda C. Djamal, Rifqi D. Putra
Source
DOI: 10.12928/TELKOMNIKA.v18i5.14899
Publisher
Universitas Ahmad Dahlan
Date
October 2020
Contributor
Sri Wahyuni
Rights
ISSN: 1693-6930
Relation
http://journal.uad.ac.id/index.php/TELKOMNIKA
Format
PDF
Language
English
Type
Text
Coverage
TELKOMNIKA Telecommunication, Computing, Electronics and Control
Files
Collection
Citation
Esmeralda C. Djamal, Rifqi D. Putra, “TELKOMNIKA Telecommunication, Computing, Electronics and Control
Brain-computer interface of focus and motor imagery using wavelet and recurrent neural networks,” Repository Horizon University Indonesia, accessed March 9, 2025, https://repository.horizon.ac.id/items/show/4081.
Brain-computer interface of focus and motor imagery using wavelet and recurrent neural networks,” Repository Horizon University Indonesia, accessed March 9, 2025, https://repository.horizon.ac.id/items/show/4081.