Robust Space-Time Signal Processing for MEG/EEG

描述(申请人提供)：该项目的目标是开发信号处理方法，以扩大脑磁图/脑电功能成像作为诊断大脑功能障碍的临床工具和研究大脑发育和功能的科学工具的作用。该研究计划引入了一种新的方法，在该方法中，测量数据和底层皮质表面都使用多分辨率基函数展开来表示。通过开发利用基函数展开的信号处理、源定位和成像算法来获得改进的性能。所提出的研究的具体目标是：1)开发基函数展开，共同表示传感器和皮层表面上的大脑活动的时空分布。我们提出的多分辨率方法使用了不同时间和空间尺度上的大脑活动的简约表示，这是有效解决反问题的各个方面的关键性质。2)开发稳健的信号处理算法，利用基函数表示来执行多个源的检测、定位和监测，以及对皮层表面上的活动进行成像。与现有方法相比，提出的算法降低了算法的复杂度，提高了算法的性能。3)利用非参数统计分析和计算机模拟来表征在目标1和目标2下开发的算法的性能。4)获得人体受试者试验数据，以证明在目标1和2下开发的算法的有效性。

DESCRIPTION (provided by applicant): The goal of this program is development of signal processing methods that will expand the role MEG/EEG functional brain imaging as a clinical tool for diagnosis of brain dysfunction and as a scientific tool for the study of brain development and function. The research program introduces a new approach in which both the measured data and underlying cortical surface are represented using multiresolution basis function expansions. Improved performance is obtained by developing signal processing, source localization, and imaging algorithms that exploit the basis function expansion. The specific aims of the proposed studies are: 1) To develop basis function expansions that jointly represent the spatio-temporal distribution of brain activity at the sensors and on the cortical surface. Our proposed multiresolution approach employs a parsimonious representation for brain activity at different temporal and spatial scales, a critical property for effective solution of various aspects of the inverse problem. 2) To develop robust signal processing algorithms that exploit the basis function representation to perform detection, localization and monitoring of multiple sources, and imaging of the activity on the cortical surface. The proposed algorithms will have reduced complexity and improved performance relative to existing methods. 3) To characterize the performance of the algorithms developed under Aims 1 and 2 using nonparametric statistical analysis and computer simulation. 4) To obtain human subject pilot data demonstrating the effectiveness of the algorithms developed under Aims 1 and 2.