Mixture Kernel Density Estimation and Remedied Correlation Matrix on the EEG-Based Copula Model for the Assessment of Visual Discomfort

Yawen Zheng,Li Yao,Xiaojie Zhao

doi:10.1007/s12559-020-09780-y

Abstract

Since electroencephalogram (EEG) signals can directly provide information on changes in brain activity due to behaviour changes, how to assess visual discomfort through EEG signals attracts researchers’ attention. However, previous assessments based on time-domain EEG features lack sufficient consideration of the dependence among EEG signals, which may affect the discrimination to visual discomfort. Although the copula model can explore the dependence among variables, the EEG-based copula models still have the following deficiencies: (1) the methods ignoring the fine-grained information hidden in EEG signals could make the estimated marginal density function improper, and (2) the approaches neglecting the pseudo-correlation among data may inappropriately estimate the correlation matrix parameter of the copula density function. The mixture kernel density estimation (MKDE) and remedied correlation matrix (RCM) on the EEG-based copula model are proposed to mitigate the mentioned shortcomings. The simulation experiments show that MKDE can not only better estimate the marginal density function but also explore fine-grained information. The RCM can be closer to the real correlation matrix parameter. With the favourable quality of the proposed EEG-based model, it is used to extract time-domain EEG features to assess visual discomfort further. To our best knowledge, the extracted features present better discrimination to visual discomfort compared with the features extracted by the state-of-the-art method.

Highlights

Stereoscopic displays are prevalent in our daily lives, such as entertainment [1, 2], education [3] and medical treatment [4]
Because this paper focuses on better extracting the timedomain EEG features by the EEG-based copula model, estimating the correlation matrix Σ is crucial
For mixture kernel density estimation (MKDE), three parameters are needed to tune: the number of clusters C, the number of smoothing parameters m, and the coefficient j

Summary

Introduction

Stereoscopic displays are prevalent in our daily lives, such as entertainment [1, 2], education [3] and medical treatment [4]. The mixture kernel density estimation (MKDE) and remedied correlation matrix (RCM) on the EEG-based copula model are proposed. – The RCM estimated by correlation analysis based on the remedied data obtained by the dictionary learning may contain less pseudo-dependence, which could be closer to the real correlation matrix parameter of the copula density function. KDE combined with binning strategy [26] or with K-means [27], reduced set density estimator [28], finite mixture model [29], etc., are proposed to reduce the scale of data to mitigate the mentioned problem These methods or models ignore the fine-grained information hidden in the overall samples, affecting the estimated marginal density function. To dig out the fine-grained information, KDE with different smoothing parameters, hereinafter multiscale strategy, Fig. 1 The illustration of the proposed EEG-based copula model is used to estimate the marginal density function of each cluster. The parameters C and gamma of SVM are determined by the GridSearch algorithm

Experimental Results

Experiments on Assessing Visual Discomfort

Method of feature extraction AR model

Conclusions