brain
To synchronize NIRS recording and your stimuli (visual, auditory, button press etc), your presentation program needs to talk to the NIRS machine. Hitachi ETG4000 allows serial communication between the NIRS machine and an external computer. You can u
Both are on NIRS (Near Infrared Spectroscopy). The first one is on how to detect NIRS activity earlier using multivariate (SVM) method; the 2nd one is a comprehensive comparison between NIRS and fMRI. Cui, Bray, Reiss (2010) Speeded Near Infrared Spe
1. plotTraces, plot a time series, or multiple time series on one plot, with vertical lines indicating the markers (events). Can be used for data quality check and global signal detection. 2. plotTopoMap, plot a map of activation. 3. plot2, scatter p
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
