Identification of EEG-Based Music Emotion Using Hybrid COA Features and t-SNE-Reference-Cited by-同舟云学术

Identification of EEG-Based Music Emotion Using Hybrid COA Features and t-SNE

Published:2019 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 2019 9th International Conference on Biomedical Engineering and Technology - ICBET' 19
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Author:

He Hong¹,Zhao Wenxiu¹,Fujimoto Ken'ichi²

Affiliation:

1. College of Information, Mechanical and Electrical Engineering, Shanghai Normal University, Shanghai, China

2. Kagawa University, Kagawa, Japan

Funder

the Project of the Science and Technology Commission of Shanghai,China

Industry-education-research Project of Shanghai Normal University

National Natural Science Foundation of China

Publisher

ACM Press

Reference28 articles.

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2. U. Rajendra Acharya, H. Fujita, Vidya K. Sudarshan a, Shreya Bhat e, Joel E.W. Koh. 2015. Application of entropies for automated diagnosis of epilepsy using EEG signals: A review. Knowledge-Based Systems. 88 (Aug. 2015), 85--96.

3. WL. Zheng, JY. Zhu, BL. Lu. 2017. Identifying stable patterns over time for emotion recognition from EEG, IEEE Transactions on Affective Computing. (June 2017), DOI=http://doi.acm.org/ 10.1109/TAFFC.2017.2712143

4. Robert Jenke, Angelika Peer, Martin Buss. 2014. Feature Extraction and Selection for Emotion Recognition from EEG, IEEE Transactions on Affective Computing. 5, 3 (Sept. 2014), 327--339.

5. CA Torres-Valencia, HF Garcia-Arias, MA Alvarez Lopez, AA Orozco-Gutierrez.2014. Comparative analysis of physiological signals and Electroencephalogram (EEG) for multimodal emotion recognition using generative models. In Proceedings of 2014 XIX Symposium on Image, Signal Processing and Artificial Vision, (Armenia, Colombia, 17-19 Sept. 2014), 1--5. DOI=http://doi.acm.org/10.1109/STSIVA.2014.7010181.

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