how to extract non-linear features like Lyapunov exponent or Fractal or correlation dimensions from an EEG signal?

Hi everyone,

i just started preprocessing process for applying on my EEG signal in matlab, i denoised it with 50Hz notch and [0.4 45] Hz bandpass filter, so actually, my signal has been denoised and now, its time to do feature extraction.

since its my graduation thesis, i need non-linear feature extraction such as Lyapunov exponent or Fractal or correlation dimensions and entropy but i've searched alot in youtube and learning websites but i couldn't find anything related.

is there someone who could help me?

I will be very thankful if someone could help me,

i will write my denoising code in here...

clc

clear all

close all

% -------------------------------------------------------------

% displaying the original Signal without preprocessing

% -------------------------------------------------------------

load('C:\Users\Alireza\Desktop\alireza\signal processing- payan name\data.mat')

signal1=c_memory(2,:);

clear c_baste_1; clear c_baz_1;

Fc=50;

Fs=256;

L=length(signal1);

T=1/Fs;

k=floor(L/256);

t=(0:L-1)/256;

NFFT = 2^nextpow2(L); % Next power of 2 from length of y

f = Fs/2*linspace(0,1,NFFT/2);

fft_signal= fft(signal1,NFFT)/L;

figure;plot(f,2*abs(fft_signal(1:NFFT/2)))

title ('Noisy Signal');

xlabel('Frequency(Hz)'); ylabel('Voltage(mV)');

% ------------------------------------------------------------

% Denoising 50 Hz of Artifact

% ------------------------------------------------------------

wo = 50/(Fs/2); bw = wo/10;

[cf5,cf6] = iirnotch(wo,bw);

signal1_2=filtfilt(cf5,cf6,signal1);

fft_signal2= fft(signal1_2,NFFT)/L;

figure(2);plot(f,2*abs(fft_signal2(1:NFFT/2)))

title ('Denoising 50 Hz');

xlabel('Frequency(Hz)'); ylabel('Voltage(mV)');

% ------------------------------------------------------------

% applying [0.4 45]Hz Bandpass Filter

% ------------------------------------------------------------

f_filter = fdesign.bandpass('n,fc1,fc2',6,0.5,45,256);

Hd = design(f_filter, 'butter');

signal_filter_1=filter(Hd,signal1_2);

fft_signal_3= fft(signal_filter_1,NFFT)/L;

figure(3);plot(f,2*abs(fft_signal_3(1:NFFT/2)));

title ('Denoised Signal');

xlabel('Frequency(Hz)'); ylabel('Voltage(mV)');

clc;

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