Experimental Data | To determine construct angles, we adapted a previous MATLAB code that detects ridges of a fingerprint [20, 25, 26]. |
Implementing COOP Calculation | To facilitate the calculation of the COOP we created a custom MATLAB code. |
Synthetic Data | 2) was generated using a random number generator (rand) that provides a uniform distribution of at least 106 random values in MATLAB . |
Synthetic Data | 3) contained 108 random numbers ( MATLAB function normrnd) that were normally distributed With the specified mean and standard deviation. |
Synthetic Results | We constructed a custom MatLab code that could be interfaced with experimental or synthetic (computer generated) data. |
Image Analysis and Computational Tools | We use custom MATLAB code to perform all image preprocessing and feature extraction steps and enable the construction and testing of our classification schemes. |
Image Analysis and Computational Tools | To implement discrete classification steps using support vector machines, we use the LIBSVM library, which is freely available for multiple platforms including MATLAB [37]. |
Supporting Information | All times are based on performance on MATLAB 2013b running on a quad core |
Supporting Information | All times are based on performance on MATLAB 2013b running on a quad core processor at 3.50 GHz 81 Table. |
Human EEG network analysis | Bandpass filtering with the fifth-order Butterworth filter was applied to EEG forward and backward, correcting the potential phase shifting after bandpass filtering (“butterworth.m”, and “filtfilt.m” in Matlab ; MathWorks, Natick, MA). |
Human EEG network analysis | For power spectrum density, a Hamming window and a modified periodogram were used for each 10 sec EEG segment (in “pwelch.m”, in Matlab ). |
Human EEG network analysis | The spearman correlation coefficient was used for evaluating the correlations among node degree, amplitude and dPLI of the 64 channels (“corr.m” in Matlab ). |