Centre for Modern Interdisciplinary Technologies, Nicolaus Copernicus University, Wileńska 4, Toruń, 87-100, Poland.
Faculty of Mathematics and Computer Science, Nicolaus Copernicus University, Chopina 12/18, Toruń, 87-100, Poland.
Neuroinformatics. 2021 Jan;19(1):107-125. doi: 10.1007/s12021-020-09464-w.
Brain activity pattern recognition from EEG or MEG signal analysis is one of the most important method in cognitive neuroscience. The SUPFUNSIM library is a new MATLAB toolbox which generates accurate EEG forward model and implements a collection of spatial filters for EEG source reconstruction, including the linearly constrained minimum-variance (LCMV), eigenspace LCMV, nulling (NL), and minimum-variance pseudo-unbiased reduced-rank (MV-PURE) filters in various versions. It also enables source-level directed connectivity analysis using partial directed coherence (PDC) measure. The SUPFUNSIM library is based on the well-known FIELDTRIP toolbox for EEG and MEG analysis and is written using object-oriented programming paradigm. The resulting modularity of the toolbox enables its simple extensibility. This paper gives a complete overview of the toolbox from both developer and end-user perspectives, including description of the installation process and use cases.
脑电或脑磁图信号分析中的脑活动模式识别是认知神经科学中最重要的方法之一。SUPFUNSIM 库是一个新的 MATLAB 工具箱,它生成精确的脑电正向模型,并实现了一系列用于脑电源重建的空间滤波器,包括线性约束最小方差 (LCMV)、特征空间 LCMV、零化 (NL) 和最小方差伪无偏降秩 (MV-PURE) 滤波器的各种版本。它还使用部分定向相干 (PDC) 度量值进行源级定向连通性分析。SUPFUNSIM 库基于著名的用于 EEG 和 MEG 分析的 FIELDTRIP 工具箱,并使用面向对象的编程范例编写。该工具箱的模块化使其易于扩展。本文从开发人员和最终用户的角度全面介绍了该工具箱,包括安装过程和用例的描述。