Research on Attention Classification Based on Long Short-term Memory Network

被引：1
|
作者：
Wang Pai [1 ]
Wu Fan [1 ]
Wang Mei [1 ]
Qin Xue-Bin [1 ]
机构：
[1] Xian Univ Sci & Technol, Sch Elect & Control Engn, Xian, Shaanxi, Peoples R China
来源：
2020 5TH INTERNATIONAL CONFERENCE ON MECHANICAL, CONTROL AND COMPUTER ENGINEERING (ICMCCE 2020) | 2020年
基金：
中国国家自然科学基金;
关键词：
Attention detection; original EEG signal; LSTM; accuracy;
D O I：
10.1109/ICMCCE51767.2020.00253
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
Attention research is of great significance for driver attention monitoring. The biggest problem in existing attention research is that the recognition accuracy cannot be improved. The main reasons include the inability to extract the timing characteristics of the original EEG attention signal. Through designing experimental schemes, collecting original EEG data, training long short-term memory network (LSTM), and comparing various classic algorithms, the accuracy of attention recognition is further improved. Experiments show that LSTM can save the timing characteristics of the original EEG attention signal, which greatly improves the accuracy of attention recognition.
引用
收藏
页码：1148 / 1151
页数：4
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