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gui_help.m
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gui_help.m
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function gui_help(which_help)
%
% function gui_help()
% Used by GUI_CALLBACK
%
switch which_help
case 'gui_cb_about'
helptitle = 'About EEG_TF';
helptext =[ ...
'EEG_TF for Matlab 9 '
'Version 0.1, May 2021 '
'Copyright (c) Alina Beliakova. '
' '
'For more information please visit: '
'https://github.com/AlinaBeliakova/EEG_TF '];
case 'gui_cb_help'
helptitle = 'EEG_TF GUI';
helptext = [...
'The guide to the EEG_TF App '
'Read the detailed guide EEG_TF.PDF for more info '
' '
'The EEG_TF is a MATLAB toolbox designed to visualize time-frequency '
'maps (spectrograms) of the EEG (or any other 3D or 2D) signals. '
'The app uses MATLAB and EEGLAB to operate through the GUI. '
' '
'Requirements: '
'- MATLAB R2018a and newer '
'- MATLAB Signal processing toolbox (for Short-Time Fourier Transform, '
'wavelets do not require any toolbox) '
'- EEGLAB for .set files processing '
'- Before starting ensure that the EEG_TF is in the MATLAB path '
' '
'Basic functionality: '
'- Type ''eeg_tf()'' in the MATLAB command line to launch the GUI '
'without any input data. Alternatively, it is also possible to pass input '
'arguments from the current MATLAB workspace by typing '
'''eeg_tf(data, fs, time)'', where data is 3D signal with dimentions '
'[channels, time, trials], fs is the sampling frequency in Hz (e.g. 128 Hz),'
'time is start and end times in seconds (e.g. [-0.5, 1] sec), or full time '
'vector in seconds. If you pass the dataset as an argument, skip the next '
'step LOAD .SET DATA '
'- Click LOAD .SET DATA to upload the existing dataset, you can choose '
'several datasets but ensure they have the same time range, conditions and '
'sampling frequency, otherwise launch analysis for each dataset separately. '
'In the EEG data region you will see the information about the (first) '
'dataset uploaded: number of channels, number of time points, time range '
'and sampling rate. If the (ICA) weights for back projection are included in'
'the EEGLAB dataset you will see the corresponding message in front of the '
'BSS/gBSS weights line '
'- Click LOAD BSS/GBSS WEIGHTS to load the pre-saved weights, calculated not'
' in the framework of EEGLAB. This function is useful if the (g)BSS was done'
'using other tools than EEGLAB. If there are no weights available in the '
'dataset and no weights uploaded, the analysis will be done in the electrode'
' space data. '
'- Check ''Keep electrode space'' to avoid automatic projection data to the '
'source space. It is needed if there are weights in the dataset but you '
'do not want to use them. '
' '
'Set parameters of the analysis '
'- Click CHOOSE EVENTS to choose the condition(s) of interest (if unset, '
'all conditions will be included) '
'- Choose COMPARE (2 conditions only) or MERGE (any number of conditions) '
'the data corresponding to the conditions '
'- Choose INDIVIDUAL or GROUP to perform analysis on each dataset or on the '
'datasets grouped together (datasets have to be compatible, e.g. to have the'
'same sampling rate, time range and conditions) '
'- Set which channels to take for analysis (e.g. 1,2,3 or 1:3), each channel'
'is treated independently and will be plotted in the separate figure '
'- Set frequency range of interest (e.g. 1:45) '
'- Set time range of interest '
' '
'Set the parameters of the spectral decomposition (the default values in '
'this section are calculated automatically and can be kept for the fast '
'start :) ) '
'- Choose the method of the spectra decomposition: Short-Time Fourier '
'Transform (uses the MATLAB function spectrogram(), '
' check https://www.mathworks.com/help/signal/ref/spectrogram.html for more '
'info) or wavelets (based on the code from Mike X Cohen (2014) Fundamentals '
'of Time-Frequency Analyses in Matlab/Octave. 1st ed, sinc(x) Press, '
'check https://www.sincxpress.com for more info) '
'- Check if you want to transform data to dB (this function is useful to '
'compensate for the 1/f law, e.g. unify the resolution across frequencies) '
'- Set the baseline for dB transform (this applies to the wavelets only) '
' '
'Set advanced parameters of spectral decomposition '
'Short-Time Fourier Transform '
'- Uncheck the ''Set default values'' option to be able to change the settings'
'- Choose the window function: Hanning (default), Blackman or none. It is '
'recommended to use window function to avoid artifacts on the tf-maps '
'- Set the window length, note that the larger the window the better is the '
'frequency resolution but worse is the time resolution, plus the signal is '
'more cut on the sides '
'- Set the step with witch the window will be moving, smaller step increase '
'the time resolution but may result in the longer computation time '
'Wavelet Transform '
'Note: read the EEG_TF.PDF guide for detailed explanation of these settings '
'- Uncheck the ''Set default values'' option to be able to change the settings'
'- Change the type of correction of non-linearity: const (default) means '
'that wavelets with the same number of cycles will be applied at each '
'frequency, linear means that the the number of cycles will change linearly '
'in the range indicated below, log leads to logarithmic change of the number'
'of cycles in the range '
'- Set the number of cycles as a single integer number or as a range '
' '
'Set parameters of plotting '
'- Set at which time points you prefer to have landmarks (for example at the'
'time of stimulus presentation, e.g. 0ms), can be left blank '
'- Set at which frequecnies you prefer to have landmarks (for example to '
'separate the alpha and beta activity, etc.), can be left blank '
' '
'- Click PLOT SPECTROGRAM(S) to get the graphical results of the analysis '
'- Click SAVE IMAGES to automatically save images in .fig and .jpg formats '
'in the directory which you choose at demand (names are automatically '
'generated in the form ''EEG_TF_Image1.jpg'') '
' '];
end
helpwin(helptext, helptitle);