EEG Source Estimation using Overlapping-Sphere Forward Model and Hierarchical-Search Beamforming

• Main Authors: Zhong-Kai Yang, 楊仲凱
• Other Authors: Yong-Sheng Chen
• Format: Others
• Language: en_US
• Published: 2005
• Online Access: http://ndltd.ncl.edu.tw/handle/69366193387668346426

碩士 === 國立交通大學 === 資訊工程系所 === 93 === Brain is the most important and complicated apparatus of human beings. EEG has been widely applied in functional brain studies due to its high temporal resolution and low cost. In this work, we focus on the development of an accurate and efficient EEG forward model as well as the inverse solution for neuronal source estimation from the EEG measurements. Our forward model successfully gains its accuracy by fitting an overlapping sphere for each EEG sensor. The computation of the overlapping sphere requires only the multi-shell geometry, instead of boundary element method, thus the proposed forward model is easy to compute. Based on the proposed forward model, the beamforming technique is applied to calculate the distributed sources in the brain space. We maximize the power contrast between active state and control state of EEG recorded data to improve the accuracy of inverse solution. Hierarchical search in the solution space is applied to save the amount of computation by searching grid point level by level instead of searching the whole brain space. According to our experiments using phantom data and visual-evoked potential data, the proposed forward model and inverse solution can efficiently and accurately estimate the source of brain activation. A quick and reliable source localization technique for EEG is successfully developed which can be applied on applications when MRI is not available, such as fundamental brain research and brain-computer interface.