Classification of human emotions based on electroencephalography (EEG) is a very popular topic nowadays in the provision of human health care and well-being. Fast and effective emotion recognition can play an important role in understanding a patient’s emotions and in monitoring stress levels in real-time. Due to the noisy and non-linear nature of the EEG signal, it is still difficult to understand emotions and can generate large feature vectors. In this article, we have proposed an efficient spatial feature extraction and feature selection method with a short processing time. The raw EEG signal is first divided into a smaller set of eigenmode functions called (IMF) using the empirical model-based decomposition proposed in our work, known as intensive multivariate empirical mode decomposition (iMEMD). The Spatio-temporal analysis is performed with Complex Continuous Wavelet Transform (CCWT) to collect all the information in the time and frequency domains. The multiple model extraction method uses three deep neural networks (DNNs) to extract features and dissect them together to have a combined feature vector. To overcome the computational curse, we propose a method of differential entropy and mutual information, which further reduces feature size by selecting high-quality features and pooling the k-means results to produce less dimensional qualitative feature vectors. The system seems complex, but once the network is trained with this model, real-time application testing and validation with good classification performance is fast. The proposed method for selecting attributes for benchmarking is validated with two publicly available data sets, SEED, and DEAP. This method is less expensive to calculate than more modern sentiment recognition methods, provides real-time sentiment analysis, and offers good classification accuracy.

基于脑电图（EEG）的人类情绪分类是当今提供人类保健和福祉的一个非常热门的话题。快速有效的情绪识别可以在了解患者情绪和实时监测压力水平方面发挥重要作用。由于脑电信号的噪声和非线性特性，仍然难以理解情绪并且可以生成大的特征向量。在本文中，我们提出了一种处理时间短的高效空间特征提取和特征选择方法。使用我们工作中提出的基于经验模型的分解，称为密集多元经验模式分解 (iMEMD)，首先将原始 EEG 信号划分为一组称为 (IMF) 的较小特征模式函数。使用复杂连续小波变换 (CCWT) 执行时空分析，以收集时域和频域中的所有信息。多模型提取方法使用三个深度神经网络 (DNN) 来提取特征并将它们分解在一起以获得组合特征向量。为了克服计算诅咒，我们提出了一种微分熵和互信息方法，该方法通过选择高质量特征和汇集 k-means 结果以产生更少维度的定性特征向量来进一步减小特征大小。该系统看起来很复杂，但是一旦使用该模型训练网络，具有良好分类性能的实时应用程序测试和验证就会很快。使用两个公开可用的数据集验证了用于选择基准测试属性的建议方法，种子和DEAP。这种方法比更现代的情感识别方法计算成本更低，提供实时情感分析，并提供良好的分类准确性。