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This toolbox offers 30 types of EEG feature extraction methods (HA, HM, HC, and etc.) for Electroencephalogram (EEG) applications.

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Jx-EEGT : Electroencephalogram ( EEG ) Feature Extraction Toolbox

View EEG Feature Extraction Toolbox on File Exchange License GitHub release


"Toward Talent Scientist: Sharing and Learning Together" --- Jingwei Too


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Introduction

  • This toolbox offers 30 types of EEG features
  • The A_Main file shows how the feature extraction methods can be applied using generated sample signal.

Input

  • X : signal ( 1 x samples )
  • opts : parameter settings ( some methods have parameters: refer here )

Output

  • feat : feature vector ( you may use other name like f2 or etc. )

Usage

The main function jfeeg is adopted to perform feature extraction. You may switch the method by changing the 'me' to other abbreviations

  • If you wish to extract mean energy ( ME ) then you may write
feat = jfeeg('me', X);
  • If you want to extract hjorth activity ( HA ) then you may write
feat = jfeeg('ha', X);

Example 1 : Extract 3 normal features ( without parameter )

% Generate a sample random signal X
fs = 500;             % Sampling frequency 
Ts = 1 / fs;          % Period
t  = 0 : Ts : 0.25; 
X  = 0.01 * (sin(2 * pi * fs * t) + randn(1, length(t)));

% Plot sample signal
plot(t,X);  grid on
xlabel('Number of samples');
ylabel('Amplitude');

% Hjorth Activity
f1 = jfeeg('ha', X); 
% Hjorth Mobility
f2 = jfeeg('hm', X); 
% Hjorth Complexity
f3 = jfeeg('hc', X); 

% Feature vector
feat = [f1, f2, f3];

% Display features
disp(feat)

Example 2 : Extract 2 features with parameter

% Generate a sample random signal X
fs = 500;             % Sampling frequency 
Ts = 1 / fs;          % Period
t  = 0 : Ts : 0.25; 
X  = 0.01 * (sin(2 * pi * fs * t) + randn(1, length(t)));

% Band Power Alpha
opts.fs = 500;
f1 = jfeeg('bpa', X, opts); 
% Tsallis Entropy
opts.alpha = 2;
f2 = jfeeg('te', X, opts);

% Feature vector
feat = [f1, f2];

% Display features
disp(feat)

List of available feature extraction methods

  • Some methods comprise parameter to be adjusted. If you do not set the parameter then the feature will be extracted using default setting
  • For convenience, you may extract the feature with parameter using default setting as following. In this way, you do not need to set the opts
feat = jfeeg('ar', X);
  • Note : You must set the sampling frequency ( fs ) since there is no default setting for it
  • You can use opts to set the parameter
    • alpha : constant
    • order : the number of orders
    • fs : sampling frequency
No. Abbreviation Name Parameter ( default )
30 'rba' Ratio of Band Power Alpha to Beta opts.fs =
29 'bpg' Band Power Gamma opts.fs =
28 'bpb' Band Power Beta opts.fs =
27 'bpa' Band Power Alpha opts.fs =
26 'bpt' Band Power Theta opts.fs =
25 'bpd' Band Power Delta opts.fs =
24 'ha' Hjorth Activity -
23 'hm' Hjorth Mobility -
22 'hc' Hjorth Complexity -
21 'skew' Skewness -
20 'kurt' Kurtosis -
19 '1d' First Difference -
18 'n1d' Normalized First Difference -
17 '2d' Second Difference -
16 'n2d' Normalized Second Difference -
15 'mcl' Mean Curve Length -
14 'me' Mean Energy -
13 'mte' Mean Teager Energy -
12 'lrssv' Log Root Sum of Sequential Variation -
11 'te' Tsallis Entropy opts.alpha = 2
10 'sh' Shannon Entropy -
09 'le' LogEnergyEntropy -
08 're' RenyiEntropy opts.alpha = 2
07 'am' Arithmetic Mean -
06 'sd' Standard Deviation -
05 'var' Variance -
04 'md' Median Value -
03 'ar' Auto-Regressive Model opts.order = 4
02 'max' Maximum Value -
01 'min' Minimum Value -

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This toolbox offers 30 types of EEG feature extraction methods (HA, HM, HC, and etc.) for Electroencephalogram (EEG) applications.

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