...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Expert Systems with Application >EEG-based user identification system using 1D-convolutional long short-term memory neural networks
【24h】

EEG-based user identification system using 1D-convolutional long short-term memory neural networks

机译：使用一维卷积长短期记忆神经网络的基于EEG的用户识别系统

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Electroencephalographic (EEG) signals have been widely used in medical applications, yet the use of EEG signals as user identification systems for healthcare and Internet of Things (loT) systems has only gained interests in the last few years. The advantages of EEG-based user identification systems lie in its dynamic property and uniqueness among different individuals. However, it is for this reason that manually designed features are not always adapted to the needs. Therefore, a novel approach based on 1D Convolutional Long Short-term Memory Neural Network (1D-Convolutional LSTM) for EEG-based user identification system is proposed in this paper. The performance of the proposed approach was validated with a public database consists of EEG data of 109 subjects. The experimental results showed that the proposed network has a very high averaged accuracy of 99.58%, when using only 16 channels of EEG signals, which outperforms the state-of-the-art EEG-based user identification methods. The combined use of CNNs and LSTMs in the proposed 1D-Convolutional LSTM can greatly improve the accuracy of user identification systems by utilizing the spatiotemporal features of the EEG signals with LSTM, and lowering cost of the systems by reducing the number of EEG electrodes used in the systems. (C) 2019 Elsevier Ltd. All rights reserved.
机译：脑电图（EEG）信号已在医疗应用中广泛使用，但是将EEG信号用作医疗保健和物联网（loT）系统的用户标识系统只是在最近几年才引起人们的兴趣。基于EEG的用户识别系统的优势在于其动态属性和不同个体之间的唯一性。但是，由于这个原因，手动设计的功能并不总是适合于需求。因此，本文提出了一种基于一维卷积长短期记忆神经网络（1D-卷积LSTM）的基于EEG的用户识别系统。该方法的性能已通过一个公共数据库验证，该数据库包含109个受试者的EEG数据。实验结果表明，当仅使用16个EEG信号通道时，所提出的网络具有99.58％的非常高的平均准确度，这优于基于最新EEG的用户识别方法。拟议的一维卷积LSTM中CNN和LSTM的组合使用可以通过利用LSTM的EEG信号的时空特征极大地提高用户识别系统的准确性，并通过减少用于EEG的EEG电极的数量来降低系统成本系统。 （C）2019 Elsevier Ltd.保留所有权利。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号