EEGNetT: EEG-based neural network for emotion recognition in real-world applications

被引：0
|
作者：
Zhu, Yuxuan [1 ]
Ozawa, Kenji [1 ]
Kong, Wanzeng [2 ]
机构：
[1] Univ Yamanashi, Grad Sch Comp Sci, Kofu, Yamanashi, Japan
[2] Hangzhou Dianzi Univ, Sch Comp, Hangzhou, Zhejiang, Peoples R China
来源：
2021 IEEE 3RD GLOBAL CONFERENCE ON LIFE SCIENCES AND TECHNOLOGIES (IEEE LIFETECH 2021) | 2021年
关键词：
electroencephalogram; emotion recognition; deep learning; network parameter;
D O I：
10.1109/LIFETECH52111.2021.9391941
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
In this study, we propose an electroencephalogram (EEG)-based emotion recognition (ER) system with a newly developed network, EEGNetT. In addition to the recognition accuracy, some indicators, including the scale of the training dataset, the number of network parameters, and the time consumption of convergence were taken into account for the evaluation of the system performance. The results obtained for the ER task indicated that EEGNetT achieves superior performance than EEGNet, especially with small training datasets. Additionally, the number of network parameters and the average epoch time indicate that EEGNetT is a promising network for real-world applications.
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页码：376 / 378
页数：3
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