Recently, the emotional state recognition of humans via Electroencephalogram (EEG) is one of the emerging topics that grasp the attention of researchers too. This EEG based recognition is normally an effective model for many of the real-time applications, especially for disabled people. A number of researchers are in progress to make the recognition model more effective in terms of accurate emotion recognition. However, it is not so satisfactory in the precise accurate progressing. Hence this paper intends to recognize the human emotional states or affects through EEG signals by adopting advanced features and classifier models. In the first stage of recognition procedure, this paper exploits 2501 (EMCD) and Wavelet Transformation to represent the EEG signal in low dimension as well as descriptive. By EMCD, the EEG redundancy can be neglected, and the significant information can be extracted. The classification processes using the extracted features with the aid of a classifier named Deep Belief Network (DBN). The performance of the proposed Wavelet-EMCD (WE) approach is analyzed in terms of measures such as Accuracy, Sensitivity, Specificity, Precision, False positive rate (FPR), False negative rate (FNR), Negative Predictive Value (NPV), False Discovery Rate (FDR), F1Score and Mathews correlation coefficient (MCC) and proven the superiority of proposed work in recognizing the emotions more accurately.

中文翻译：

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用于情感识别的经验平均曲线分解-小波分解脑电信号的深度学习

最近，通过脑电图（EEG）对人类的情绪状态识别也是引起研究人员关注的新兴课题之一。这种基于 EEG 的识别通常是许多实时应用程序的有效模型，特别是对于残疾人。许多研究人员正在使识别模型在准确的情感识别方面更有效。然而，在精确准确的进展中并不那么令人满意。因此，本文旨在通过采用先进的特征和分类器模型，通过脑电信号来识别人类的情绪状态或影响。在识别过程的第一阶段，本文利用 2501 (EMCD) 和小波变换来表示低维和描述性的脑电信号。通过 EMCD，EEG 冗余可以忽略，并且可以提取重要信息。借助名为 Deep Belief Network (DBN) 的分类器，使用提取的特征进行分类处理。所提出的 Wavelet-EMCD (WE) 方法的性能在准确度、灵敏度、特异性、精度、假阳性率 (FPR)、假阴性率 (FNR)、负预测值 (NPV)、假等方面进行了分析发现率 (FDR)、F1Score 和 Mathews 相关系数 (MCC)，并证明了所提出的工作在更准确地识别情绪方面的优越性。