%0 Journal Article
%T Variance Entropy: A Method for Characterizing Perceptual Awareness of Visual Stimulus
%A Meng Hu
%A Hualou Liang
%J Applied Computational Intelligence and Soft Computing
%D 2012
%I Hindawi Publishing Corporation
%R 10.1155/2012/525396
%X Entropy, as a complexity measure, is a fundamental concept for time series analysis. Among many methods, sample entropy (SampEn) has emerged as a robust, powerful measure for quantifying complexity of time series due to its insensitivity to data length and its immunity to noise. Despite its popular use, SampEn is based on the standardized data where the variance is routinely discarded, which may nonetheless provide additional information for discriminant analysis. Here we designed a simple, yet efficient, complexity measure, namely variance entropy (VarEn), to integrate SampEn with variance to achieve effective discriminant analysis. We applied VarEn to analyze local field potential (LFP) collected from visual cortex of macaque monkey while performing a generalized flash suppression task, in which a visual stimulus was dissociated from perceptual experience, to study neural complexity of perceptual awareness. We evaluated the performance of VarEn in comparison with SampEn on LFP, at both single and multiple scales, in discriminating different perceptual conditions. Our results showed that perceptual visibility could be differentiated by VarEn, with significantly better discriminative performance than SampEn. Our findings demonstrate that VarEn is a sensitive measure of perceptual visibility, and thus can be used to probe perceptual awareness of a stimulus. 1. Introduction Over the past decades, entropy [1] has been widely used for analysis of dynamic systems. Among many measures, sample entropy (SampEn) is thought of as an effective, robust method due to its insensitivity to data length and its immunity to noise [2]. Until now, SampEn has been successfully applied for discriminant analysis of cardiovascular data [3], electroencephalogram data [4], and many others [5]. In addition, SampEn has been used in multiscale analysis for computing entropy over multiple time scales inherent in time series. For example, multiscale entropy [6] and adaptive multiscale entropy (AME) [7] both use SampEn to estimate entropy over multiple scales of time series. Despite its popularity, it is not well recognized that there is an inherit drawback of SampEn used for discriminant analysis, that is, the calculation of SampEn is routinely based on the normalized data where the variance of data that may provide additional information for discrimination is discarded [8]. The normalization is essentially to rescale the data, which is appropriate if the analysis is driven by the search for order in the dynamics, but is otherwise inappropriate for discriminant analysis of two data
%U http://www.hindawi.com/journals/acisc/2012/525396/