takkodama / openvibeldacalc Goto Github PK

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% :-:-:-:-:-:-:-:-:-: Up to date INFO :-:-:-:-:-:-:-:-:
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% (25/Feb/2016) Caution! Function LDAfuncex_P300 and LDAfuncex_SSVEP has not been
% public so far because of the author rights. Those of codes are modified
% version (implemented shrinkage algorhythm or so) of the LDA algorithm
% function which released in MATLAB central. Please, refer to the following link.
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% http://www.mathworks.com/matlabcentral/fileexchange/29673-lda--linear-discriminant-analysis
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% :-:-:-:-:-:-:-:-:-: Instruction :-:-:-:-:-:-:-:-:-:
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% main_LDAcalc(ARG_1(char), ARG_2(char), ARG_3(double), ARG_4(double))
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% === Input ===
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% ARG_1 directory_Training(char): File directory location which has training csv files generated by OpenViBE
% ARG_2 directory_Trial(char): File directory location which has trial csv files generated by OpenViBE
% ARG_3 savePNG(double): If you want to save the result of the graph, this value should be set to 1, othewise 0
% ARG_4 identifier(double): Value of file identifier, if(SSVEP) value = 1; elseif(P300) value = 2;
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% === Output ===
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% Figure: Probability of each duration versus each targets (Grid)
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% === Example ===
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% MATLAB > main_LDAcalc('../User/DirectoryName/Target', '../User/DirectoryName/Trial', 1, 1)
% MATLAB > main_LDAcalc('../User/DirectoryName/Target', '../User/DirectoryName/Trial', 0, 0)
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% :-:-:-:-: (C) Takumi Kodama, University of Tsukuba, Japan :-:-:-:-: