...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Neurocomputing >Decoding of motor imagery EEG based on brain source estimation
【24h】

Decoding of motor imagery EEG based on brain source estimation

机译：基于脑源估计的运动图像脑电信号解码

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

The decoding of Motor Imagery EEG (MI-EEG) is the most crucial part of biosignal processing in the Brain-computer Interface (BCI) system. The traditional recognition mode is always devoted to extracting and classifying the spatiotemporal feature information of MI-EEG in the sensor domain, but these brain dynamic characteristics, which are derived from the cerebral cortical neurons, are reflected more immediately and obviously with high spatial resolution in the source domain. With the development of neuroscience, the state-of-the-art EEG Source Imaging (ESI) technology converts the scalp signals into brain source space and excavates the way for source decoding of MI-EEG. Minimum Norm Estimate (MNE) is a classical and original EEG inverse transformation. Due to the lack of depth weighting of dipoles, it may be more suitable for the estimation of superficial dipoles and will be slightly insufficient for further source classification. In addition, the selection of a Region of Interest (ROI) is usually an essential step in the source decoding of MI-EEG by Independent Component Analysis (ICA), and the most relevant independent component of original EEG signals is transformed into the equivalent current dipoles to obtain the ROI by ESI. Although the excellent results of this method can be obtained for unilateral limb motor imaging EEG signals, which shows more distinct phenomena of event-related desynchronization (ERD), the decoding accuracy may be restricted for more complex multi-limb motor imagery tasks, whose ERD is no longer evident. Therefore, in this paper, we propose a novel brain source estimation to decode MI-EEG by applying Overlapping Averaging (OA) in the temporal domain and Weighted Minimum Norm Estimate (WMNE), which overcomes the limitations of general ROI-based decoding methods and introduces weighting factors to complement the estimation of deep dipoles. Its advantages will be evaluated on a public dataset with five subjects by comparing it with MNE, WMNE, sLORETA, OA-MNE and ICA-WMNE. The proposed method reaches a higher average decoding accuracy of 81.32% compared to other methods by 10-fold cross-validation at the same chance level. This study will increase the universality of the source decoding and facilitate the development of a BCI system in the source domain. (C) 2019 Elsevier B.V. All rights reserved.
机译：运动图像脑电图（MI-EEG）的解码是脑机接口（BCI）系统中生物信号处理的最关键部分。传统的识别模式始终致力于在传感器域中提取和分类MI-EEG的时空特征信息，但是这些源自大脑皮层神经元的大脑动态特征在空间分辨率较高的情况下得到了更直接，更明显的反映。源域。随着神经科学的发展，最先进的EEG源成像（ESI）技术将头皮信号转换为脑源空间，并为MI-EEG的源解码开辟了道路。最小范数估计（MNE）是经典且原始的EEG逆变换。由于缺乏偶极子的深度加权，它可能更适合于浅层偶极子的估计，并且对于进一步的声源分类将略微不足。此外，选择感兴趣区域（ROI）通常是通过独立分量分析（ICA）对MI-EEG进行源解码的必不可少的步骤，并且原始EEG信号中最相关的独立分量会转换为等效电流偶极子以通过ESI获得ROI。尽管对于单侧肢体运动成像EEG信号可以获得这种方法的出色结果，这显示出事件相关失步（ERD）更加明显的现象，但是对于更复杂的多肢体运动成像任务（其ERD），解码精度可能受到限制不再明显。因此，在本文中，我们提出了一种新颖的脑源估计方法，该方法通过在时域中应用重叠平均（OA）和加权最小范数估计（WMNE）来解码MI-EEG，从而克服了常规基于ROI的解码方法的局限性，引入加权因子来补充深偶极子的估计。通过将其与MNE，WMNE，sLORETA，OA-MNE和ICA-WMNE进行比较，将在具有五个主题的公共数据集上评估其优势。与其他方法相比，该方法通过在相同的机会水平下进行10倍交叉验证，可以达到81.32％的平均解码精度。这项研究将增加源解码的通用性，并促进源域中BCI系统的开发。 （C）2019 Elsevier B.V.保留所有权利。

著录项

  • 来源
    《Neurocomputing》 |2019年第28期|182-193|共12页
  • 作者

    Li Ming-Ai; Wang Yi-Fan; Jia Song-Min; Sun Yan-Jun; Yang Jin-Fu;

  • 作者单位

    Beijing Univ Technol, Fac Informat Technol, Beijing 100124, Peoples R China|Beijing Key Lab Computat Intelligence & Intellige, Beijing, Peoples R China;

    Beijing Univ Technol, Fac Informat Technol, Beijing 100124, Peoples R China;

    Beijing Univ Technol, Fac Informat Technol, Beijing 100124, Peoples R China|Beijing Key Lab Computat Intelligence & Intellige, Beijing, Peoples R China;

    Beijing Univ Technol, Fac Informat Technol, Beijing 100124, Peoples R China;

    Beijing Univ Technol, Fac Informat Technol, Beijing 100124, Peoples R China|Beijing Key Lab Computat Intelligence & Intellige, Beijing, Peoples R China;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    MI-EEG; Dipole source estimation; Overlapping averaging; Weighted minimum norm estimate; Time of interest; Source decoding;

    机译：MI-EEG;偶极子源估计;重叠平均;加权最小范数估计;关注时间;源解码;

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