Koelstra, S., Muhl, C., Soleymani, M., Lee, J.S., Yazdani, A., Ebrahimi, T., Pun, T., Nijholt, A., Patras, I.: Deap: a database for emotion analysis using physiological signals. Katsigiannis, S., Ramzan, N.: Dreamer: a database for emotion recognition through EEG and ECG signals from wireless low-cost off-the-shelf devices. In: 2019 International Conference on Electrical, Computer and Communication Engineering (ECCE), IEEE, pp. Islam, M.R., Ahmad, M.: Wavelet analysis based classification of emotion from EEG signal. In: International Conference on Artificial Neural Networks. Hinton, G.E., Krizhevsky, A., Wang, S.D.: Transforming auto-encoders. Hinton, G.E., Osindero, S., Teh, Y.W.: A fast learning algorithm for deep belief nets. Hinton, G.E., Salakhutdinov, R.R.: Reducing the dimensionality of data with neural networks. 81–84 (2013)įei, H., Ji, D., Zhang, Y., Ren, Y.: Topic-enhanced capsule network for multi-label emotion classification. In: 2013 6th International IEEE/EMBS Conference on Neural Engineering (NER), IEEE, pp. 27(4), 760–771 (2019)ĭuan, R.N., Zhu, J.Y., Lu, B.L.: Differential entropy feature for EEG-based emotion classification. Stimuli 56(3), 1–17 (2006)Ĭôté-Allard, U., Fall, C.L., Drouin, A., Campeau-Lecours, A., Gosselin, C., Glette, K., Laviolette, F., Gosselin, B.: Deep learning for electromyographic hand gesture signal classification using transfer learning. 1–9 (2019)īos, D.O., et al.: EEG-based emotion recognition. In: Signal, Image and Video Processing, pp. Thesis, Erciyes University (2019)īasar, M.D., Duru, A.D., Akan, A.: Emotional state detection based on common spatial patterns of EEG. Finally, the method was tested with Dreamer and Deap EEG datasets.Īldemir, R.: Evaluation of drug treatment processes of children with attention deficit and hyperactivity by EEG analysis. The obtained results were also compared and evaluated with other state-of-the-art methods. Thanks to the proposed method, 99.51% training and 98.21% test accuracy on positive, negative and neutral emotions were achieved in the Seed EEG dataset. The most important innovation of the method is to adjust the architecture of the capsule network to adapt to the EEG signals. In this paper, a method including selection of suitable channels from EEG data, feature extraction by Welch power spectral density estimation of selected channels and enhanced capsule network-based classification model is presented. In addition, traditional algorithms used to classify emotion ignore the neighborhood relationship and hierarchical order within the EEG signals. Therefore, EEG signals obtained from the human brain must be analyzed and processed accurately and consistently. There may be inconsistencies even in signals recorded from the same person. But, EEG signals are much more complex than image and audio signals. Jeff Taylor dreamed of his patent for Monster.Recently, it has become very popular to use electroencephalogram (EEG) signals in emotion recognition studies. ![]() Paul McCartney praised his dreams for his multi-platinum song, Yesterday.Bob Dylan composed music from his dreams.Stravinsky, Wagner, and Beethoven heard musical compositions, from fragments to entire canons, in their dreams.The planet Uranus was discovered by William Herschel in a dream.Mendeleyev beheld the complete periodic table in his dream.A dream led Otto Loewi to a Nobel Prize for his contribution to medicine. ![]()
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