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

被引:0
作者
Lin, Sheng-Kai [1 ]
Lo, Rong-Chin [1 ]
Lee, Ren-Guey [1 ]
机构
[1] Natl Taipei Univ Technol, Inst Elect Engn, 1,Sec 3,Zhongxiao E Rd, Taipei 10608, Taiwan
来源
BIOMEDICAL ENGINEERING-APPLICATIONS BASIS COMMUNICATIONS | 2020年 / 32卷 / 03期
关键词
Magnetoencephalography; electroencephalography; three-dimensional generalized Hough transform; CURVES;
D O I
10.4015/S1016237220500246
中图分类号
R318 [生物医学工程];
学科分类号
0831 ;
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.
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页数:8
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