Optimizing Hyperparameters of CNN and DNN for Emotion Classification Based on EEG Signals

EEG emotion is a research topic that has gained significant attention in the development of emotion classification systems. This study focuses on optimizing the hyperparameters of CNN (Convolutional Neural Network) and DNN (Deep Neural Network) for classifying EEG emotion signals. The data is divided into three train-test data ratio scenarios: 80:20, 70:30, and 60:40. After modeling and the classification process, hyperparameter tuning was conducted on both models to achieve the best results. Experimental results showed the highest accuracy of 98.36% for CNN, while DNN reached 98.18% in the 80:20 data ratio scenario. Despite the high accuracy, the differences in the loss curves between CNN and DNN reflect the complexity of the performance of both models. The train-test data ratio was also found to significantly impact the performance of both models, with the 80:20 data split yielding the best results, while the 70:30 and 60:40 splits resulted in slightly lower accuracies.