One important application of independent component analysis （ICA） is in image processing. A two dimensional （2-D） composite ICA algorithm framework for 2-D image independent component analysis （2-D ICA） is propo...One important application of independent component analysis （ICA） is in image processing. A two dimensional （2-D） composite ICA algorithm framework for 2-D image independent component analysis （2-D ICA） is proposed. The 2-D nature of the algorithm provides it an advantage of circumventing the roundabout transforming procedures between two dimensional （2-D） image deta and one-dimensional （l-D） signal. Moreover the combination of the Newton （fixed-point algorithm） and natural gradient algorithms in this composite algorithm increases its efficiency and robustness. The convincing results of a successful example in functional magnetic resonance imaging （fMRI） show the potential application of composite 2-D ICA in the brain activity detection.展开更多
在脑机接口研究中,针对运动想象脑电信号的特征提取,采用了一种优化的基于小波包的ICA(独立成分分析)法,用于提取大脑在想象动作时产生事件相关去同步/同步(Event Related Desynchronization or Event Related Synchronization ERD/ERS...在脑机接口研究中,针对运动想象脑电信号的特征提取,采用了一种优化的基于小波包的ICA(独立成分分析)法,用于提取大脑在想象动作时产生事件相关去同步/同步(Event Related Desynchronization or Event Related Synchronization ERD/ERS)信号。利用小波包对脑电信号进行分解去除不同脑电信号之间的统计相关性,抽取包含ERD/ERS现象的特征频带,对每个特征频带分别进行ICA分解,获取与ERD/ERS现象相关的μ节律和β节律。最后引入ERD/ERS系数作为量化指标进行想象动作的识别。分类仿真结果表明,上述方法能够显著增强运动想象脑电信号的ERD/ERS特征信息,对比与独立使用某一种方法,两种方法结合更能有效的提取脑电信号特征波。展开更多
基金Supported by the 973 Project (No.2003CB716106), NSFC (No.90208003, 30200059), TRAPOYT, Doctor Training Fund of MOE, PRC, Key Research Project of Science and Technology of MOE, Fok Ying Tong Education Foundation (No.91041)
文摘One important application of independent component analysis （ICA） is in image processing. A two dimensional （2-D） composite ICA algorithm framework for 2-D image independent component analysis （2-D ICA） is proposed. The 2-D nature of the algorithm provides it an advantage of circumventing the roundabout transforming procedures between two dimensional （2-D） image deta and one-dimensional （l-D） signal. Moreover the combination of the Newton （fixed-point algorithm） and natural gradient algorithms in this composite algorithm increases its efficiency and robustness. The convincing results of a successful example in functional magnetic resonance imaging （fMRI） show the potential application of composite 2-D ICA in the brain activity detection.
文摘在脑机接口研究中,针对运动想象脑电信号的特征提取,采用了一种优化的基于小波包的ICA(独立成分分析)法,用于提取大脑在想象动作时产生事件相关去同步/同步(Event Related Desynchronization or Event Related Synchronization ERD/ERS)信号。利用小波包对脑电信号进行分解去除不同脑电信号之间的统计相关性,抽取包含ERD/ERS现象的特征频带,对每个特征频带分别进行ICA分解,获取与ERD/ERS现象相关的μ节律和β节律。最后引入ERD/ERS系数作为量化指标进行想象动作的识别。分类仿真结果表明,上述方法能够显著增强运动想象脑电信号的ERD/ERS特征信息,对比与独立使用某一种方法,两种方法结合更能有效的提取脑电信号特征波。
