Klasifikasi Sinyal EEG Untuk Mengenali Jenis Emosi Menggunakan Deep Learning

 Pita Rosemari (Universitas Sriwijaya, Palembang, Indonesia)
 Dian Palupi Rini (Universitas Sriwijaya, Palembang, Indonesia)
 (*)Winda Kurnia Sari Mail (Universitas Sriwijaya, Palembang, Indonesia)

(*) Corresponding Author

Submitted: December 15, 2023; Published: December 29, 2023


This research focuses on in-depth exploration and analysis of the application of three types of deep learning, namely Convolutional Neural Networks (CNN), Bidirectional LSTM (BI-LSTM) and Deep Neural Network (DNN). The three models are trained with the same parameters, consisting of three layers, using the Relu activation function, and applying 1 dropout level. In order to compare the performance of the three, experiments were carried out using three dataset groups for training and evaluation of performance. The evaluation includes metrics such as accuracy, recall, F1-Score, and areas under the curve (AUC). The dataset used is EEG Emotion which consists of 2458 unique variables. In terms of performance, BI-LSTM succeeded in outperformed the performance of CNN and DNN in the task of classification of emotional data based on EEG signals. On the other hand, CNN and DNN show excess in the acceleration of the training process compared to BI-LSTM. Although the accuracy of the two methods is almost similar in all data distribution, but in the evaluation of the ROC curve, the BI-LSTM model demonstrates superior with a more optimal curve than CNN and DNN.


Convolutional Neural Networks (CNN); Bidirectional LSTM (Bi-LSTM); Deep Neural Network (DNN); Sinyal EEG

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