Emotion brain-computer interface using wavelet and recurrent neural networks

(1) * Esmeralda Contessa Djamal Mail (Department of Informatics, Universitas Jenderal Achmad Yani, Indonesia)
(2) Hamid Fadhilah Mail (Department of Informatics, Universitas Jenderal Achmad Yani, Indonesia)
(3) Asep Najmurrokhman Mail (Department of Electrical Engineering, Universitas Jenderal Achmad Yani, Indonesia)
(4) Arlisa Wulandari Mail (Department of Medicine, Universitas Jenderal Achmad Yani, Indonesia)
(5) Faiza Renaldi Mail (Department of Informatics, Universitas Jenderal Achmad Yani, Indonesia)
*corresponding author


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.


Brain-Computer Interface; Emotion recognition; Recurrent Neural Networks; robot simulator; Wavelet; LSTM




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