tcchriszhao/eeg-feature-extraction-toolbox

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

Jx-EEGT : Electroencephalogram ( EEG ) Feature Extraction Toolbox

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"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	-