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[1]畢曉君,喬偉征.基于改進深度學習模型C-NTM的腦電魯棒特征學習[J].哈爾濱工程大學學報,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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基于改進深度學習模型C-NTM的腦電魯棒特征學習(/HTML)
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《哈爾濱工程大學學報》[ISSN:1006-6977/CN:61-1281/TN]

卷:
40
期數:
2019年09期
頁碼:
1642-1649
欄目:
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:
畢曉君12 喬偉征2
1. 中央民族大學 信息工程學院, 北京 100081;
2. 哈爾濱工程大學 信息與通信工程學院, 黑龍江 哈爾濱 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
關鍵詞:
腦電信號魯棒特征深度學習卷積神經網絡神經圖靈機頻譜圖卷積神經圖靈機認知負載
分類號:
TP391
DOI:
10.11990/jheu.201808069
文獻標志碼:
A
摘要:
為了在腦電信號魯棒特征學習中提取更多腦電抽象和深層特征,本文在卷積長短時記憶網絡的基礎上提出一種深度學習混合網絡。采用快速傅里葉變換將多通道的腦電信號轉換為一系列具有空域、時域、頻域相關信息的頻譜圖;將改進的卷積神經網絡和神經圖靈機組合搭建完成深度學習混合模型卷積神經圖靈機C-NTM;通過認知工作負載腦電的分類任務對改進的模型進行評估。實驗結果表明:本文所提模型在相應的數據庫上取得了94.5%的準確率,優于目前在腦電分類任務中效果最好的模型。該模型能夠有效地學習不同受試者之間和同一受試者不同狀態時的腦電特征,實現更好的腦電魯棒特征學習。

參考文獻/References:

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備注/Memo

備注/Memo:
收稿日期:2018-08-31。
基金項目:國家自然科學基金項目(61175126);國家國際科技合作專項項目(2015DFG12150).
作者簡介:畢曉君,女,教授,博士生導師;喬偉征,男,博士研究生.
通訊作者:喬偉征,E-mail:qiaoweizheng@hrbeu.edu.cn.
更新日期/Last Update: 2019-09-06
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