使用色彩與深度感測器之磁振造影與腦磁波圖自動化對位系統

Abstract

In this study, we developed an automatic system to co-register EEG and MRI data that used for the source estimation of electric brain activity. Integration of EEG and MRI brain data could obtain high temporal and spatial resolution that can localize sources of rapid changes of cortical activation. In the conventional method, there are still some drawbacks such as the need of additional markers and the manual error. Thus, by using Kinect for Windows which extracts color and depth information at the same time as interface, we aligned MRI coordinate system with digitizer coordinate system to resolve problems. In the proposed method, a complete EEG-MRI co-registration would be achieved in two steps: (i) Aligned Kinect coordinate system with MRI coordinate system using facial information. (ii) The transformation is based on matching of corresponding markers pasted on the face during acquisition of both in the Kinect and digitizer coordinate systems. By our system, the location of electrodes which were recorded by digitization device would be found in the same coordinate system. Due to the thickness of electrodes on the EEG cap, we adjusted them with thickness compensation. Finally, using three error estimations to evaluate the results of co-registration and compared with different methods made before. The experiment was performed with ten subjects, and each one done twice. The mean residual error of sensors was 1.67 mm, and the mean residual error of cross was 1.83 mm. These results compared with other methods show that our system is sufficiently accurate, repeatable, efficient and labor-intensive to be used to assist neuroscience studies.

In this study, we developed an automatic system to co-register EEG and MRI data that used for the source estimation of electric brain activity. Integration of EEG and MRI brain data could obtain high temporal and spatial resolution that can localize sources of rapid changes of cortical activation. In the conventional method, there are still some drawbacks such as the need of additional markers and the manual error. Thus, by using Kinect for Windows which extracts color and depth information at the same time as interface, we aligned MRI coordinate system with digitizer coordinate system to resolve problems. In the proposed method, a complete EEG-MRI co-registration would be achieved in two steps: (i) Aligned Kinect coordinate system with MRI coordinate system using facial information. (ii) The transformation is based on matching of corresponding markers pasted on the face during acquisition of both in the Kinect and digitizer coordinate systems. By our system, the location of electrodes which were recorded by digitization device would be found in the same coordinate system. Due to the thickness of electrodes on the EEG cap, we adjusted them with thickness compensation. Finally, using three error estimations to evaluate the results of co-registration and compared with different methods made before. The experiment was performed with ten subjects, and each one done twice. The mean residual error of sensors was 1.67 mm, and the mean residual error of cross was 1.83 mm. These results compared with other methods show that our system is sufficiently accurate, repeatable, efficient and labor-intensive to be used to assist neuroscience studies.