图 书 馆
GroupWise的稀疏强制执行估计为解决EEG/ MEG逆问题Groupwise sparsity enforcing estimators for solving the EEG/MEG inverse problem |
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| 课程网址: | http://videolectures.net/sip08_haufe_gsee/ |
| 主讲教师: | Stefan Haufe |
| 开课单位: | 柏林工业大学 |
| 开课时间: | 2008-12-18 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | Cerebral current flows are directly related to information transfer in the brain and thus an excellent means for studying the mechanisms of cogni- tive processing. Electro- and Magneto-encephalography, EEG and MEG, are noninvasive measures of these electric currents (EEG) or their respec- tive accompanying magnetic fields (MEG). The reconstruction of the cere- bral current density from EEG/MEG measurements is an ill-posed inverse problem. As the forward mapping from the current sources to the external sensors is linear, the inverse problem may be formulated as a highly un- der determined linear system of equations, which has no unique solution. The common strategy to deal with this ambiguity is regularization, i.e. fit- ting the data with an additional penalization of the sources. Both l2-norm and l1-norm based penalties have been proposed based on the neurophys- iologically motivated assumptions of smoothness and sparsity, respectively. |
| 关 键 词: | 医药; 神经科学; EEG / MEG逆问题 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2021-01-31：nkq |
| 阅读次数: | 36 |