Fuzzy entropy

https://opus.lib.uts.edu.au/bitstream/10453/87097/4/publication%2Bversion.pdf

This paper proposes using inherent fuzzy entropy (Inherent FuzzyEn) and its multi-scale version, which employs empirical mode decomposition (EMD) and fuzzy membership function (exponential function) to address the dynamic complexity in electroencephalogram (EEG) data.

EEG Processing

The general scheme of the EEG analysis is illustrated in Fig. 2. The acquired EEG data were processed and analyzed using EEGLAB (http://www.sccn.ucsd.edu/eeglab/, an open-source EEG toolbox for MATLAB) during the EEG processing and complexity calculation steps. For the part of EEG processing, the raw EEG signals were subjected to a 1-Hz high-pass and 30- Hz low-pass infinite impulse response filter, and then downsampled to 250 Hz from the sample recording rate of 500 Hz. For the artifact rejection, apparent eye contaminations in EEG signals were manually removed by visual inspection. Then, Independent Component Analysis (ICA) was applied to the EEG signals and the components responsible for the eye movements and blinks were rejected. Finally, the EEG signals without these artifact components was reconstructed using the back-projection method. The EEG data were segmented into eyes-open (EO) and eyes-closed (EC) epochs for further complexity analysis. The EEG complexity of EO and EC conditions were calculated and compared by the entropy evaluation (ApEn, SampEn, FuzzyEn and Inherent FuzzyEn).