brain
If you want to control ETG-4000 with your program through the serial port, and at the same time your experiment requires realtime feedback from the ETG4000, you can either merge the RealtimeOT script and your presentation script (which will be very c
Hitachi provides a Matlab script (RealtimeOT.m) which can deliver real-time feedback from ETG-4000. This script is installed in the computer (Windows 2000) in the NIRS room. One can modify the script to deliver the real-time signal in the desired for
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
