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用于从 fMRI 信号中实时进行无主体和主体相关的脑状态分类的工具箱。

A toolbox for real-time subject-independent and subject-dependent classification of brain states from fMRI signals.

机构信息

Institute of Medical Psychology and Behavioral Neurobiology, University of Tübingen Tübingen, Germany ; Graduate School of Neural & Behavioural Sciences International Max Planck Research School, University of Tübingen Tübingen, Germany ; Department of Biomedical Engineering, University of Florida Gainesville, FL, USA.

出版信息

Front Neurosci. 2013 Oct 17;7:170. doi: 10.3389/fnins.2013.00170. eCollection 2013.

Abstract

There is a recent increase in the use of multivariate analysis and pattern classification in prediction and real-time feedback of brain states from functional imaging signals and mapping of spatio-temporal patterns of brain activity. Here we present MANAS, a generalized software toolbox for performing online and offline classification of fMRI signals. MANAS has been developed using MATLAB, LIBSVM, and SVMlight packages to achieve a cross-platform environment. MANAS is targeted for neuroscience investigations and brain rehabilitation applications, based on neurofeedback and brain-computer interface (BCI) paradigms. MANAS provides two different approaches for real-time classification: subject dependent and subject independent classification. In this article, we present the methodology of real-time subject dependent and subject independent pattern classification of fMRI signals; the MANAS software architecture and subsystems; and finally demonstrate the use of the system with experimental results.

摘要

近年来,多元分析和模式分类在从功能成像信号预测和实时反馈大脑状态以及对大脑活动的时空模式进行映射方面的应用越来越多。在这里,我们介绍了 MANAS,这是一个用于执行 fMRI 信号在线和离线分类的通用软件工具箱。MANAS 是使用 MATLAB、LIBSVM 和 SVMlight 包开发的,以实现跨平台环境。MANAS 基于神经反馈和脑-机接口 (BCI) 范式,针对神经科学研究和脑康复应用。MANAS 为实时分类提供了两种不同的方法:基于个体的分类和独立于个体的分类。在本文中,我们介绍了 fMRI 信号实时基于个体的和独立于个体的模式分类的方法;MANAS 软件架构和子系统;最后,通过实验结果演示了该系统的使用。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7ba4/3798026/8cf770c649e7/fnins-07-00170-g0001.jpg

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