Automatic EEG Signal Preprocessing And Linear Nonlinear FeatureExtraction

In this Script a suitable Butterworth band-pass filter (0.5–60 Hz) was employed to eliminate out-of-band noise. In addition, a 50 Hz notch filter was utilized to eliminate the remaining powerline noise. To make it easier to track future results, we normalized the entire database.
In the step of feature extraction, linear and nonlinear univariate features, as well as nonlinear multivariate features, were extracted from EEG signals. Individual recording channels and five frequency sub-bands (Delta,Theta, Alpha , Beta and Gamma) underwent spectral analysis of average power. On the basis of the Kaiser window, five Finite Impulse Response (FIR) filters were created to split the original signals into five subbands.

Linear Features:

Delta Average Band Power , Theta Average Band Power , Alpha Average Band Power , Beta Average Band Power , Gamma Average Band Power Theta To Beta Ratio(TBR)

Nonliner Features:

Sample Entropy , Shannon Entropy , Dispersion Entropy , MultiScale Sample Entropy

Environment Variables

To run this Code, you will need to add the functions folder to your MATLAB path

And then run the following script Main.m

Note : WorkSpace.mat is result of run.

License

Version 1.0 August 2022 | Copyright (c) 2022 | All rights reserved

Farhad Abedinzadeh torghabeh | Master Student of Biomdeical Engineering
farhaad.abedinzade@gmail.com

Cite as

Farhad Abedinzade (2022). Auto EEG Signal Preprocessing and Feature Extraction (https://github.com/farhadabedinzadeh/AutomaticEEGSignalPreprocessingAndLinearNonlinearFeatureExtraction/releases/tag/1.0.0), GitHub. Retrieved August 12, 2022.