Weighted-permutation entropy as complexity measure for electroencephalographic time series of different physiological states

摘要: An electroencephalographic (EEG) waveform could be denoted by a series of ordinal patterns called motifs which are based on the ranking values subsequence time series. Permutation entropy (PE) has been developed to describe relative occurrence each these motifs. However, PE few limitations, mainly its inability differentiate between distinct certain motif, and sensitivity noise. To minimize those Weighted-Permutation Entropy (WPE) was proposed as modification version improve complexity measuring for times This paper presents an approach incorporating WPE into analysis different physiological states namely EEG Three states, eye-closed (EC), eye-open (EO), visual oddball task (VOT) were included examine ability identify discriminate states. The classification using achieved results with accuracy 87% EC EO 83% VOT, respectively, linear discrimination analysis. showed potential promising feature nonlinear in brain. It also observed that used marker large artifact low frequency such eye-blink.