[1]毕晓君,乔伟征.基于改进深度学习模型C-NTM的脑电鲁棒特征学习[J].哈尔滨工程大学学报,2019,40(09):1642-1649.[doi:10.11990/jheu.201808069]
 BI Xiaojun,QIAO Weizheng.Learning robust features from EEG based on improved deep-learning model C-NTM[J].hebgcdxxb,2019,40(09):1642-1649.[doi:10.11990/jheu.201808069]
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基于改进深度学习模型C-NTM的脑电鲁棒特征学习(/HTML)
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《哈尔滨工程大学学报》[ISSN:1006-6977/CN:61-1281/TN]

卷:
40
期数:
2019年09期
页码:
1642-1649
栏目:
出版日期:
2019-09-05

文章信息/Info

Title:
Learning robust features from EEG based on improved deep-learning model C-NTM
作者:
毕晓君12 乔伟征2
1. 中央民族大学 信息工程学院, 北京 100081;
2. 哈尔滨工程大学 信息与通信工程学院, 黑龙江 哈尔滨 150001
Author(s):
BI Xiaojun12 QIAO Weizheng2
1. College of Information Engineering, Minzu University of China, Beijing 100081, China;
2. College of Information and Communication Engineering, Harbin Engineering University, Harbin 150001, China
关键词:
脑电信号鲁棒特征深度学习卷积神经网络神经图灵机频谱图卷积神经图灵机认知负载
分类号:
TP391
DOI:
10.11990/jheu.201808069
文献标志码:
A
摘要:
为了在脑电信号鲁棒特征学习中提取更多脑电抽象和深层特征,本文在卷积长短时记忆网络的基础上提出一种深度学习混合网络。采用快速傅里叶变换将多通道的脑电信号转换为一系列具有空域、时域、频域相关信息的频谱图;将改进的卷积神经网络和神经图灵机组合搭建完成深度学习混合模型卷积神经图灵机C-NTM;通过认知工作负载脑电的分类任务对改进的模型进行评估。实验结果表明:本文所提模型在相应的数据库上取得了94.5%的准确率,优于目前在脑电分类任务中效果最好的模型。该模型能够有效地学习不同受试者之间和同一受试者不同状态时的脑电特征,实现更好的脑电鲁棒特征学习。

参考文献/References:

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备注/Memo

备注/Memo:
收稿日期:2018-08-31。
基金项目:国家自然科学基金项目(61175126);国家国际科技合作专项项目(2015DFG12150).
作者简介:毕晓君,女,教授,博士生导师;乔伟征,男,博士研究生.
通讯作者:乔伟征,E-mail:qiaoweizheng@hrbeu.edu.cn.
更新日期/Last Update: 2019-09-06