To achieve a highly efficient brain-computer interface (BCI) system regarding emotion recognition from electroencephalogram (EEG) signal, the most crucial issues are feature extractions and classifier selection. This work proposes an innovative method that hybridizes the principal component analysis (PCA) and t-statistics for feature extraction. This work contributes to successfully implement spatial PCA to reduce signal dimensionality and to select the suitable features based on the t-statistical inferences among the classes. The proposed method has been applied on the SEED dataset (SJTU Emotion EEG Dataset) that yielded significant channels and features for getting higher classification accuracy. With extracted features, four classifiers– support vector machine (SVM), artificial neural network (ANN), linear discriminant analysis (LDA), and k-nearest neighbor (kNN) method were applied to classify the emotional states. The classifiers showed slightly different classification accuracies compared to each other. ANN and SVM showed the highest classification accuracy (86.57 ± 4.08 and 85.85 ± 5.72) in case of subject dependent approach. On the other hand, the proposed method provides 84.3% and 77.1% classification accuracy with ANN and SVM, respectively in case of subject independent approach. Eventually, the proposed method and its outcomes demonstrate that this proposal is better than the several existing methods in emotion recognition.

为了实现有关脑电图（EEG）信号情感识别的高效脑机接口（BCI）系统，最关键的问题是特征提取和分类器选择。这项工作提出了一种创新的方法，该方法将主成分分析（PCA）和t统计信息进行混合以提取特征。这项工作有助于成功实现空间PCA，以降低信号维数并基于t来选择合适的特征。类之间的统计推断。所提出的方法已应用于SEED数据集（SJTU Emotion EEG Dataset），该数据集产生了重要的渠道和功能，从而获得了更高的分类精度。通过提取特征，使用四个分类器-支持向量机（SVM），人工神经网络（ANN），线性判别分析（LDA）和k最近邻（kNN）方法对情绪状态进行分类。分类器之间的分类精度略有不同。在依赖对象的情况下，ANN和SVM显示出最高的分类精度（86.57±4.08和85.85±5.72）。另一方面，在独立于主题的情况下，所提方法利用ANN和SVM分别提供84.3％和77.1％的分类精度。最终，