...
首页> 外文期刊>Biomedical Engineering: Applications, Basis and Communications >MAGNETOENCEPHALOGRAPHY-ELECTROENCEPHALOGRAPHY CO-REGISTRATION USING 3D GENERALIZED HOUGH TRANSFORM
【24h】

MAGNETOENCEPHALOGRAPHY-ELECTROENCEPHALOGRAPHY CO-REGISTRATION USING 3D GENERALIZED HOUGH TRANSFORM

机译:使用3D广义霍夫变换的磁性脑扫描 - 脑电图共同注册

获取原文
获取原文并翻译 | 示例

摘要

This study proposes an advanced co-registration method for an integrated high temporal resolution electroencephalography (EEG) and magnetoencephalography (MEG) data. The MEG has a higher accuracy for source localization techniques and spatial resolution by sensing magnetic fields generated by the entire brain using multichannel superconducting quantum interference devices, whereas EEG can record electrical activities from larger cortical surface to detect epilepsy. However, by integrating the two modality tools, we can accurately localize the epileptic activity compared to other non-invasive modalities. Integrating the two modality tools is challenging and important. This study proposes a new algorithm using an extended three-dimensional generalized Hough transform (3D GHT) to coregister the two modality data. The pre-process steps require the locations of EEG electrodes, MEG sensors, head-shape points of subjects and fiducial landmarks. The conventional GHT algorithm is a well-known method used for identifying or locating two 2D images. This study proposes a new co-registration method that extends the 2D GHT algorithm to a 3D GHT algorithm that can automatically co-register 3D image data. It is important to study the prospective brain source activity in bio-signal analysis. Furthermore, the study examines the registration accuracy evaluation by calculating the root mean square of the Euclidean distance of MEG-EEG co-registration data. Several experimental results are used to show that the proposed method for co-registering the two modality data is accurate and efficient. The results demonstrate that the proposed method is feasible, sufficiently automatic, and fast for investigating brain source images.
机译:本研究提出了一种用于集成的高时间分辨率脑电图(EEG)和磁性脑图(MEG)数据的高级共同登记方法。通过使用多通道超导量子干扰装置感测整个大脑产生的磁场,MEG具有更高的精度,以便通过使用多通道超导量子干扰装置感测整个大脑产生的磁场,而脑电图可以从较大皮质表面记录电气活动以检测癫痫。然而,通过整合两种模态工具,我们可以将癫痫活动准确地定位与其他非侵入方式相比。整合两个模态工具具有挑战性和重要性。本研究提出了一种新的算法,使用扩展的三维广义霍夫变换(3D GHT)来重铸两个模态数据。预处理步骤要求EEG电极,MEG传感器,主题的头部形状点和基准地标的位置。传统的GHT算法是用于识别或定位两个2D图像的公知方法。本研究提出了一种新的共同登记方法,将2D GHT算法扩展到可以自动共同登记3D图像数据的3D GHT算法。研究生物信号分析中的前瞻性脑源活动非常重要。此外,该研究通过计算MEG-EEG共登记数据的欧几里德距离的根均线来检查登记准确性评估。若干实验结果用于表明该方法的共同记录两个模态数据的方法是准确和高效的。结果表明,所提出的方法是可行的,充分自动,并且用于研究脑源图像。

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