The interplay between human emotions, personality, and motivation results in individual specificity in neurophysiological data distributions for the same emotional category. To address this issue for building an emotion recognition system based on electroencephalogram (EEG) features, we propose a shared-subspace feature elimination (SSFE) approach to identify EEG variables with common characteristics across multiple individuals. In the SSFE framework, a low-dimensional space defined by a selected number of EEG features is created to represent the inter-emotion discriminant for different pairs of subjects evaluated based on the interclass margin. Using two public databases—DEAP and MAHNOB-HCI—the performance of the SSFE is validated according to the leave-one-subject-out paradigm. The performance of the proposed framework is compared with five other feature-selection methods. The effectiveness and computational cost of the SSFE is investigated across six machine learning models based on their optimal hyperparameters. In the end, the competitive binary classification accuracy from the SSFE of arousal and valence recognitions are determined to be 0.6521 and 0.6635, respectively, for DEAP, and 0.6520 and 0.6537, respectively for MAHNOB-HCI.

人类情绪，个性和动机之间的相互作用导致了同一情绪类别的神经生理数据分布中的个体特异性。为了解决此问题，以构建基于脑电图（EEG）特征的情绪识别系统，我们提出了一种共享子空间特征消除（SSFE）方法来识别多个个体具有共同特征的EEG变量。在SSFE框架中，创建了由选定数量的EEG特征定义的低维空间，以表示基于类间余量评估的不同对象对的情感间区别。使用两个公共数据库（DEAP和MAHNOB-HCI），SSFE的性能根据留一法则范式进行了验证。所提出的框架的性能与其他五种特征选择方法进行了比较。基于SSFE的最佳超参数，在六个机器学习模型中研究了SSFE的有效性和计算成本。最后，从觉醒和价数识别的SSFE得出的竞争性二元分类精度对于DEAP分别确定为0.6521和0.6635，对于MAHNOB-HCI分别确定为0.6520和0.6537。