## brain

Quite often you need to convert an image (or multiple images) to a MatLab matrix for further analysis and visualization (e.g. extracting time series, multivariate pattern analysis, etc). SPM provides handy functions for this: P = spm_select; % select
Wavelet transform coherence (WTC) is a method for analyzing the coherence and phase lag between two time series as a function of both time and frequency (Chang and Glover 2010). Here I played with it using the MatLab toolbox provided by Grinsted et a
Standard deviation (std): standard deviation of the sample Standard error, or standard error of the mean (sem), is the standard deviation of the mean. \(sem=std/\sqrt{N}\) Most errorbars in scientific publications refer to standard error. Quite often
If variable X and Y has correlation 0.1, how much does it help to predict Y based on X? In the simplest binary case, the probability (p) to correctly predict Y based on X is a linear function of correlation (c), i.e. $$p=\frac{c+1}{2}$$ That means, a
Tools: SPM, cor2mni Assume the image is “a.img”, do v = spm_vol('a.img'); v.mat If v.mat is a diagonal matrix, you can simply read the number and they are the voxel size in mm. If not, a trick is to calculate the distance between adjacent
SVM is mostly commonly used for binary classifications. But one branch of SVM, SVM regression or SVR, is able to fit a continuous function to data. This is particularly useful when the predicted variable is continuous. Here I tried some very simple c
This is an example of brain activation plotted on surface. In many circumstances surface view is much more straightforward than a slice view. Here is how I created such a plot using MatLab and SPM. Environment and Tools: Windows XP MatLab (v7.6) SPM
We use TCP/UDP/IP Toolbox 2.0.5 to read and write data from/to a TCPIP port. It’s fast and reliable. The version we use is 2.0.5. Below is a matlab sample script showing how to connect to another computer (called ETG-4000) with TCPIP : %Connect
Noise removal methods in NIRS can be divided into 4 categories: reducing noise based on its temporal characteristics: The instrument noise is usually in the high frequency band and thus can be removed by band pass filtering. Band pass filtering can a