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FeatureFinder 2.4.1

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FeatureFinder 2.4.1



20 Aug 2012 (Updated )

A user-friendly tool for signal filtering and feature extraction. Well-suited to large data sets!

%   This template will help you create your own features!  Just follow 
%   instructions 1?4 below, and you'll be on your way.  (Note that you must
%   have at least some MATLAB programming experience in order to create
%   your own features.)
%   Input arguments:
%       DATA - The data from which you'll extract a single feature.
%       FS - The sampling rate of the data.
%       BL - The samples associated with the baseline window.
%       TARGET - The samples associated with the target window.
%   Output values:
%       FEATURE - The calculated feature.  It must be scalar (e.g., 
%           1.2 is OK, but [2 4 3] isn't) and numeric (e.g., not 'mouse').
%        -- OR --
%       FEATURE - The feature description.  This is outputted if the
%           function receives no input arguments.
% Written by Alex Andrews, 2011.

%   1) Select "File" --> "Save As..." and enter the feature name (e.g.,
%      "BrandNewFeature.m").  You can also update "FeatureTemplate" in the
%      function header below to the new name (optional).
function nFeature=DifferenceInAbsMax(nData,Fs,nBL_Range,nTarg_Range)

%   2) Enter the feature description below, ensuring that it begins with a
%      single quote (') and ends with a single quote and semicolon (';).
%      (Watch that you don't edit any of the other three lines.)
if nargin==0
    nFeature='The difference between the absolute maxima of the target and baseline regions.';        

%   3) Write the code to calculate your feature. Remember that nTarg_Range
%   and nBL_Range contain the sample numbers corresponding to the target
%   and baseline windows.  That said, use of this information is optional.
%   (This example calculates the difference between window averages.)

%   4)  Put the feature value in the nFeature variable
%   (This example calculates the difference between window averages.)

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