Identification of Emotional Valences via Memory-Informed Deep Neural Network with Entropy Features
Pages 31 - 35
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.
References
[1]
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.
[2]
Barrett, L. F. 1998. Discrete Emotions or Dimensions? The Role of Valence Focus and Arousal Focus. Cogn. Emot. 12, 4, 579--599.
[3]
Russell, J. A. 1980. A circumplex model of affect. J. Pers. Soc. Psychol. 39, 6, 1161--1178.
[4]
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.
[5]
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.
[6]
Mohammadi, Z., Frounchi, J., and Amiri, M. 2017. Wavelet-based emotion recognition system using EEG signal. Neural Comput. Appl. 28, 8, 1985--1990.
[7]
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.
[8]
Xiang, J., Rui, R., and Li, L. 2014. Emotion recognition based on the sample entropy of EEG. Biomed. Mater. Eng. 24, 1, 1185--1192.
[9]
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.
[10]
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.
[11]
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.
[12]
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.
[13]
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.
[14]
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.
[15]
Pincus, S. M. 1991. Approximate entropy as a measure of system complexity. Proc. Natl. Acad. Sci. USA. 88, 6, 2297--2301.
[16]
Manis, G., Aktaruzzaman, M., and Sassi, R. 2017. Bubble Entropy: An Entropy Almost Free of Parameters. IEEE Trans. Biomed. Eng. 64, 11, 2711--2718.
[17]
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.
[18]
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.
[19]
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.
[20]
Graves, A., Wayne, G., and Danihelka, I. 2014. Neural Turing Machines. arXiv:1410.5401. Retrieved from https://arxiv.org/abs/1410.5401.
[21]
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.
Index Terms
- Identification of Emotional Valences via Memory-Informed Deep Neural Network with Entropy Features
Recommendations
Entropies based detection of epileptic seizures with artificial neural network classifiers
Computer assisted automated detection is highly inevitable for recognizing neurological disorders, as it involves continuous monitoring of Electroencephalogram (EEG) signal. Being a non-stationary signal, suitable analysis is essential for EEG to ...
Characterizing dynamics of absence seizure EEG with spatial-temporal permutation entropy
Characterizing transient brain dynamics prior to seizures is a main challenge in absence epilepsy study. As brain is a chaos dynamical system, many complexity based methods have been used to track the dynamical changes of absence seizure EEG. However, ...
Disabled person emotion recognition in EEG signal using deep neural network
Emotion recognition is an important field of research in brain-computer interactions. As technology and the understanding of emotions are advancing, there are growing opportunities for automatic emotion recognition systems. The brain uses the ...
Comments
Information & Contributors
Information
Published In
May 2019
213 pages
ISBN:9781450371711
DOI:10.1145/3330393
Copyright © 2019 ACM.
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]
In-Cooperation
- Shenzhen University: Shenzhen University
- Sun Yat-Sen University
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 10 May 2019
Check for updates
Author Tags
Qualifiers
- Research-article
- Research
- Refereed limited
Funding Sources
- Shenzhen Fundamental Research Projects
Conference
ICMSSP 2019
ICMSSP 2019: 2019 4th International Conference on Multimedia Systems and Signal Processing
May 10 - 12, 2019
Guangzhou, China
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 80Total Downloads
- Downloads (Last 12 months)2
- Downloads (Last 6 weeks)1
Reflects downloads up to 05 Mar 2025
Other Metrics
Citations
View Options
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign in