Yun Lu
ABSTRACT
Emotion plays an important role in people's everyday routine and work. Using electroencephalograph (EEG) signals to identify emotional states of human brain is one of the most valuable methods for emotion recognition. This paper studied positive and negative emotional valences identification from EEG signals via memory-informed deep neural network with entropy features. To quantify EEG signals over time, we first used sliding time windows to calculate sample entropy in EEG signals. Then we integrated a life-long memory module into deep neural network to accumulate prior knowledge of the entropy features of positive and negative emotional valences during training phase, so as to enhance the performance of emotional valences identification. Finally, we performed our experimental analysis with the SEED (SJTU Emotion EEG Dataset) dataset, a publicly available EEG dataset for emotion analysis. The average accuracy of 92.22% was achieved for the identification of positive and negative emotional valences for 15 subjects in SEED dataset. The experimental results showed that the proposed framework could effectively achieve the identification of positive and negative emotional valences from EEG signals, which had broad application prospects in healthcare decision-making system.
- Karademas, E. C., Tsalikou, C., and Tallarou, M. C. 2011. The impact of emotion regulation and illness-focused coping strategies on the relation of illness-related negative emotions to subjective health. J. Health Psychol. 16, 3, 510--5199.Google Scholar
Cross Ref
- Barrett, L. F. 1998. Discrete Emotions or Dimensions? The Role of Valence Focus and Arousal Focus. Cogn. Emot. 12, 4, 579--599.Google Scholar
- Russell, J. A. 1980. A circumplex model of affect. J. Pers. Soc. Psychol. 39, 6, 1161--1178.Google ScholarCross Ref
- García-Martínez, B., Martínez-Rodrigo, A., Cantabrana, R. Z., García, J. P., and Alcaraz, R. J. E. 2016. Application of entropy-based metrics to identify emotional distress from electroencephalographic recordings. Entropy. 18, 6, 221.Google ScholarCross Ref
- Zhang, Y., Ji, X., and Zhang, S. J. 2016. An approach to EEG-based emotion recognition using combined feature extraction method. Neurosci. Lett. 633, 152--157.Google Scholar
- Mohammadi, Z., Frounchi, J., and Amiri, M. 2017. Wavelet-based emotion recognition system using EEG signal. Neural Comput. Appl. 28, 8, 1985--1990. Google ScholarDigital Library
- Li, M., Xu, H., Liu, X., and Lu, S. 2018. Emotion recognition from multichannel EEG signals using K-nearest neighbor classification. Technol. Health Care. 26, S1, 509--519.Google ScholarDigital Library
- Xiang, J., Rui, R., and Li, L. 2014. Emotion recognition based on the sample entropy of EEG. Biomed. Mater. Eng. 24, 1, 1185--1192.Google Scholar
- Zheng, W., Zhu, J., and Lu, B. L. 2018. Identifying Stable Patterns over Time for Emotion Recognition from EEG. IEEE Trans. Affect. Comput. pp, 99, 1--15.Google Scholar
- Zirui, L., Olga, S., Lipo, W., Reinhold, S., and Muller-Putz, G. R. 2018. Domain adaptation techniques for eeg-based emotion recognition: a comparative study on two public datasets. IEEE Trans. Cogn. Dev. Syst. pp, 99, 1--1.Google Scholar
- Jirayucharoensak, S., Pan-Ngum, S., and Israsena, P. 2014. EEG-based emotion recognition using deep learning network with principal component based covariate shift adaptation. Sci. World J. 2014, 627892.Google Scholar
- Duan, R. N., Zhu, J. Y., and Lu, B. L. 2013. Differential Entropy Feature for EEG-based Emotion Classification, In 2013 6th International IEEE/EMBS Conference on Neural Engineering (NER) (San Diego, CA, USA, 6-8 Nov. 2013). IEEE, 81--84.Google ScholarCross Ref
- Zheng, W. L., and Lu, B. L. 2015. Investigating Critical Frequency Bands and Channels for EEG-based Emotion Recognition with Deep Neural Networks. IEEE Trans. Auton. Ment. Dev. 7, 3, 162--175.Google ScholarDigital Library
- Zhu, J. Y., Zheng, W. L., and Lu, B. L. 2015. Cross-subject and Cross-gender Emotion Classification from EEG. In World Congress on Medical Physics and Biomedical Engineering (Toronto, Canada, June 7-12, 2015). Springer International Publishing, 1188--1191.Google Scholar
- Pincus, S. M. 1991. Approximate entropy as a measure of system complexity. Proc. Natl. Acad. Sci. USA. 88, 6, 2297--2301.Google ScholarCross Ref
- Manis, G., Aktaruzzaman, M., and Sassi, R. 2017. Bubble Entropy: An Entropy Almost Free of Parameters. IEEE Trans. Biomed. Eng. 64, 11, 2711--2718.Google ScholarCross Ref
- Richman, J. S., and Moorman, J. R. 2000. Physiological time-series analysis using approximate entropy and sample entropy. Am. J. Physiol. Heart. Circ. Physiol. 278, 6, H2039- H2049.Google ScholarCross Ref
- Ni, L., Cao, J., and Wang, R. 2013. Analyzing EEG of quasi-brain- death based on dynamic sample entropy measures. Comput. Math. Methods Med. 2013, 618743.Google ScholarCross Ref
- Sukhbaatar, S., szlam, A., Weston, J., and Fergus, R. 2015. End-to-end memory networks. In Advances in Neural Information Processing Systems (NIPS 2015), 2440--2448. Retrieved from https://arxiv.org/abs/1503.08895. Google ScholarDigital Library
- Graves, A., Wayne, G., and Danihelka, I. 2014. Neural Turing Machines. arXiv:1410.5401. Retrieved from https://arxiv.org/abs/1410.5401.Google Scholar
- Kaiser, Ł., Nachum, O., Roy, A., and Bengio, S. 2017. Learning to remember rare events. In 5th International Conference on Learning Representations (ICLR 2017). Retrieved from https://arxiv.org/abs/1703.03129.Google Scholar
Index Terms
- Identification of Emotional Valences via Memory-Informed Deep Neural Network with Entropy Features
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