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.

由于脑电图（EEG）信号可以直接提供有关因行为变化而导致的大脑活动变化的信息，因此如何通过EEG信号评估视觉不适感吸引了研究人员的注意力。然而，基于时域脑电特征的先前评估没有充分考虑脑电信号之间的依赖性，这可能会影响对视觉不适的辨别力。尽管copula模型可以探究变量之间的依赖性，但基于EEG的copula模型仍存在以下缺陷：（1）忽略隐藏在EEG信号中的细粒度信息的方法可能会使估计的边际密度函数不正确，以及（2） ）忽略数据之间的伪相关性的方法可能会不适当地估计copula密度函数的相关矩阵参数。提出了基于EEG的copula模型的混合核密度估计（MKDE）和修正相关矩阵（RCM），以减轻上述缺点。仿真实验表明，MKDE不仅可以更好地估计边际密度函数，而且可以探索细粒度的信息。RCM可以更接近真实的相关矩阵参数。凭借所提出的基于EEG的模型的良好质量，它可用于提取时域EEG特征以进一步评估视觉不适。据我们所知，与通过最新技术方法提取的特征相比，提取的特征对视觉不适的辨别能力更好。仿真实验表明，MKDE不仅可以更好地估计边际密度函数，而且可以探索细粒度的信息。RCM可以更接近真实的相关矩阵参数。凭借所提出的基于EEG的模型的良好质量，它可用于提取时域EEG特征以进一步评估视觉不适。据我们所知，与通过最新技术方法提取的特征相比，提取的特征对视觉不适的辨别能力更好。仿真实验表明，MKDE不仅可以更好地估计边际密度函数，而且可以探索细粒度的信息。RCM可以更接近真实的相关矩阵参数。凭借所提出的基于EEG的模型的良好质量，它可用于提取时域EEG特征以进一步评估视觉不适。据我们所知，与通过最新方法提取的特征相比，提取的特征对视觉不适的辨别能力更好。