基于变换数据空间的相干脑电源定位算法

不同脑区之间的相互协作对大脑完成认知任务具有重要意义.脑区电活动的相干性被认为是这种协作的表现形式.从头表脑电无创地三维定位相干源有助于了解大脑的内在机制.传统的MUSIC算法不能定位相干源.本文发展了一种在变换数据空间的MUSIC算法用于相干源定位.首先根据先验信息大致估计相干源区的范围,然后设计能压制相干源区的数据变换矩阵.最后在变换后的数据空间定位相干源.不同条件下的计算模拟实验表明,相比其它方法,这种方法具有更高的定位精度,运算速度也更快.

Coherent EEG Sources Localization in Transformation Data Space

The functional connectivity between different brain regions is of importance for human brain to accomplish cognitive tasks,and it may appear as correlated temporal behavior of neural activity.It will help to investigate the brain underlying mechanism to noninvasively localize coherent sources which underlie the scalp EEG recordings.Classical MUSIC(Multiple Signal Classification) method has difficulty on localizing coherent sources.This paper presented a class of MUSIC for coherent sources localization in transformation data space.At first,the coherent source region is coarsely estimated by a prior knowledge or other mapping methods.And then,a transformation matrix designed to suppress the source activity is constructed.At last,using the transformation matrix to project the scalp EEG recordings into a new transformation data space,where the coherent sources can be localized by classical MUSIC method.Computer simulations reveal that under different levels of noise,in contrast to other coherent source localization algorithms,the proposed method has rather less mean localization bias and run much faster.