Emotion brain-computer interface using wavelet and recurrent neural networks

International Journal of Advances in Intelligent Informatics

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Field Value
 
Title Emotion brain-computer interface using wavelet and recurrent neural networks
 
Creator Djamal, Esmeralda Contessa
Fadhilah, Hamid
Najmurrokhman, Asep
Wulandari, Arlisa
Renaldi, Faiza
 
Subject Brain-Computer Interface; Emotion recognition; Recurrent Neural Networks; robot simulator; Wavelet; LSTM
 
Description Brain-Computer Interface (BCI) has an intermediate tool that is usually obtained from EEG signal information. This paper proposed the BCI to control a robot simulator based on three emotions for five seconds by extracting a wavelet function in advance with Recurrent Neural Networks (RNN). Emotion is amongst variables of the brain that can be used to move external devices. BCI's success depends on the ability to recognize one person’s emotions by extracting their EEG signals. One method to appropriately recognize EEG signals as a moving signal is wavelet transformation. Wavelet extracted EEG signal into theta, alpha, and beta wave, and consider them as the input of the RNN technique. Connectivity between sequences is accomplished with Long Short-Term Memory (LSTM). The study also compared frequency extraction methods using Fast Fourier Transform (FFT). The results showed that by extracting EEG signals using Wavelet transformations, we could achieve a confident accuracy of 100% for the training data and 70.54% of new data. While the same RNN configuration without pre-processing provided 39% accuracy, even adding FFT would only increase it to 52%. Furthermore, by using features of the frequency filter, we can increase its accuracy from 70.54% to 79.3%. These results showed the importance of selecting features because of RNNs concern to sequenced its inputs. The use of emotional variables is still relevant for instructions on BCI-based external devices, which provide an average computing time of merely 0.235 seconds.
 
Publisher Universitas Ahmad Dahlan
 
Contributor PTUPT –Penelitian Terapan Unggulan Perguruan Tinggi funded by Ministry of Research Technology, Republik Indonesia
 
Date 2020-03-29
 
Type info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion

 
Format application/pdf
 
Identifier http://ijain.org/index.php/IJAIN/article/view/432
10.26555/ijain.v6i1.432
 
Source International Journal of Advances in Intelligent Informatics; Vol 6, No 1 (2020): March 2020; 1-12
2548-3161
2442-6571
 
Language eng
 
Relation http://ijain.org/index.php/IJAIN/article/view/432/ijain_v6i1_p1-12
 
Rights https://creativecommons.org/licenses/by-sa/4.0
 

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