超参数
脑电图
计算机科学
人工智能
模式识别(心理学)
语音识别
情绪检测
情绪分类
心理学
情绪识别
神经科学
作者
Dian Palupi Rini,Winda Sari
出处
期刊:International journal on information and communication technology
[School of Computing, Telkom University]
日期:2024-06-24
卷期号:10 (1): 1-12
标识
DOI:10.21108/ijoict.v10i1.857
摘要
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.
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