Hi Alireza,
To perform non-linear feature extraction on your EEG signal in MATLAB, you can use several methods present in various MATLAB toolboxes to obtain Lyapunov exponent, fractal dimension, correlation dimension, and entropy measures.
To extract Lyapunov experiment, you can use the Predictive Maintenance Toolbox’s ‘lyapunovExponent’ method to characterize the rate of separation of infinitesimally close trajectories.
% Example code to calculate Lyapunov Exponent
signal = signal_filter_1; % Your preprocessed signal
lyapExp = lyapunovExponent(signal, Fs);
disp(['Lyapunov Exponent: ', num2str(lyapExp)]);
To extract Fractal Dimension, you can use the ‘boxcount’ function from MATLAB file exchange to determine fractal properties of a 1D segment, 2D image or a 3D array.
% Example code to calculate Fractal Dimension
signal = signal_filter_1; % Your preprocessed signal
fd = boxcount(signal);
Similarly, the correlation dimension can be obtained by using the ‘correlationDimension’ function from the predictive maintenance toolbox to obtain a measure of chaotic signal complexity. Entropy can be obtained by using the ‘wentropy’ function from the signal processing toolbox. This function returns the normalized Shannon wavelet entropy of the signal.
Make sure to install any required toolboxes or functions from the MATLAB File Exchange if they are not already available in your MATLAB environment. For more information regarding the functions used, refer the following documentations:
- lyapunovExponent: https://www.mathworks.com/help/predmaint/ref/lyapunovexponent.html
- boxcount: https://www.mathworks.com/matlabcentral/fileexchange/13063-boxcount
- correlationDimension: https://www.mathworks.com/help/predmaint/ref/correlationdimension.html
- wentropy: https://www.mathworks.com/help/wavelet/ref/wentropy.html
