腦電逆問題的一種分域迭代聚焦算法

腦電逆問題是一個(gè)欠定問題，引入適當(dāng)?shù)募s束或迭代技巧是改善解的性能的有效途徑。該文提出了一種將自相干增強(qiáng)算法(SCEA)和迭代聚焦算法(FOCUSS)相結(jié)合的新算法。該方法以最小模解為出發(fā)點(diǎn)，對(duì)其用聚類算法進(jìn)行分區(qū)和自相干增強(qiáng)，然后進(jìn)行迭代聚焦。針對(duì)皮層源的仿真計(jì)算表明，該方法可實(shí)現(xiàn)對(duì)不同強(qiáng)度的多源成像。

Electroencephalogram (EEG) inverse is an underdetermined problem. Various constraints or iteration strategies are being considered to improve the performance of the inverse. In this work, a new approach integrated Self-coherence Enhancement Algorithm (SECA) and Focal Underdetermined System Solver (FOCUSS) approach is proposed. Based on a minimum norm solution, this approach applies SECA to the multiple sub-regions of the solution space obtained, and then FOCUSS is utilized to further focalize the solution. The simulations for cortex sources confirm that this new approach may be applied to map a few un-equal strength cortical electric