如何克服自身习气,王阳明说出了具体的解决办法: 1.首在立志 王阳明说:“志立而习气渐消。学本于立志,志立而学问之功已过半矣。” 克服习气,首在立志。凡事只要有一种持之以恒的志向,时刻为这个目标而努力,就有成功的可能。 立志之后,就要省察克治,“省察克治”就是反省自己、克制私欲邪念。
complex network measure of brain connectivity :脑功能连接的复杂网络方法
一入brain maping 到处是坑,从溯源 到连接 如今还得搞图论 别说了 让我静静 ! 原文来自:Complex network measures of brain connectivity Nodes The nature of nodes and links in individual brain networks is determined by combinations of brain mapping methods, anatomical parcellation schemes, and measures of connectivity
Connectome and connectomic :脑结构连接和脑功能连接
Connectome: structure connectivity connectomic :functional connectivity structure connectivity 是基础 类似物质的基本组成,就是大脑本身的神经网络连接 Functional connectivity 是在 structure connectivity 基础上进行的相关活动 就是脑神经信号传播的动态网络连接 方法很多,但是学习SC and FC之前必须了解图论(graph thoery )比如 node edge path clustering 等 如上图 ,其中 hub 和 rich club 是比较火的 尤其是rich club的 据说在不同脑区之间的通信扮演很重要的角色
Fieldtrip中文 :anatomical atlas and labels
FieldTrip supports the use of an anatomical atlas to look up the anatomical label of a source that you have localized. Vice versa you can also first look up the location of an anatomical region and subsequently use that in
Fieldtrip 中文:Parametric and non-parametric statistics on ERP
参数统计和非参数统计是非常重要的对于EEG的分析 , 这篇文章记录了教程中几个tips 1. The ERF data was obtained using ft_timelockanalysis. For the purpose of inspecting your data visually, we also use ft_timelockgrandaverage to calculate the grand average across participants, which can be used for subsequent visualization. 所有数据先被ft_timelockanalysis 处理,然后再将所有trial 利用函数 ft_timelockgrandaverage进行平均,注意教程所提供的数据已经做过ft_timelockanalysis处理。
Fieldtrip中文: 重要函数的method解析
在fieltrip 当中,有几个函数是贯穿整个工具的 ,也是MEEG信号分析的主要手段,每个函数都涉及到不同的方法,我再次对函数使用的多重method做个归纳 1. FT_TIMELOCKANALYSIS computes the timelocked average ERP/ERF and computes the covariance matrix 时域分析是EEG信号分析的第一步,尤其对SSVEP和ERP, 那么这里面 Use as [timelock] = ft_timelockanalysis(cfg, data) The data should be organised in a structure as obtained from the FT_PREPROCESSING function. The configuration should be
Fieldtrip中文:EEG 描述/推理分析的 design matrix
在fieltrip里面做statistics的时候,除了要输出处理过的数据(freqanalysis or timelockanalysis or sourceanalysis)还要设计一个design matrix 主要用来标记数据的属性,比如属于哪一组 哪个trials 哪个condidations , 其中 ivar 和 uvar是比较重要的数据,它也是用来指明矩阵中每一行的属性,其中indepent var指的是不同的group, 而 uvar指的是不同的unit . 单个sub之间不同condition的比较 A vsB cfg.design=[ones(1,5),2*ones(1,4)] %%design=[1 1 1 1 1 2 2 2 2 ] cfg.ivar=1;
EGI channels Map and area of interest
http://www.nature.com/articles/srep34273?WT.feed_name=subjects_language nature的文章用的此种分类 相信比较权威 就这样吧 Seventy-two of 128 electrodes were divided into four groups (frontal, central, parietal, and occipital) for each hemisphere. Each group contained between 16 and 20 electrodes that were averaged together to represent EEG responses from that scalp
fieldtrip 自动去除杂讯
Fieldtrip has provide a automatic removing artifact method , we have done a test on the local data, in turn, it did not work as you expected , sometimes badlly, Please visually checking for artifacts the following code :