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Transfer components between subjects for EEG-based emotion recognition

机译：基于EEG的情感识别科目之间的转移组件

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Addressing the structural and functional variability between subjects for robust affective brain-computer interface (aBCI) is challenging but of great importance, since the calibration phase for aBCI is time-consuming. In this paper, we propose a subject transfer framework for electroencephalogram (EEG)-based emotion recognition via component analysis. We compare two state-of-the-art subspace projecting approaches called transfer component analysis (TCA) and kernel principle component analysis (KPCA) for subject transfer. The main idea is to learn a set of transfer components underlying source domain (source subjects) and target domain (target subject). When projected to this subspace, the difference of feature distributions of both domains can be reduced. From the experiments, we show that the two proposed approaches, TCA and KPCA, can achieve an improvement on performance with the best mean accuracies of 71.80% and 79.83%, respectively, in comparison of the baseline of 58.95%. The significant improvement shows the feasibility and efficiency of our approaches for subject transfer emotion recognition from EEG signals.
机译：解决强大的情感脑电脑界面（ABCI）的科目之间的结构和功能可变性是具有挑战性的，但重要性非常重要，因为ABCI的校准阶段是耗时的。在本文中，我们提出了一种通过组件分析为基于脑电图（EEG）的脑电图（EEG）的主题转移框架。我们比较两个称为转移组件分析（TCA）和内核原理分析（KPCA）的最先进的子空间突出方法进行主题转移。主要思想是学习源域（源主题）和目标域（目标主题）的一组传输组件。投射到该子空间时，可以减少两个域的特征分布差异。从实验中，我们表明，两种拟议的方法，TCA和KPCA，可以分别为58.95％的基线进行71.80％和79.83％的最佳平均准确性的性能。重大改进表明了我们对EEG信号的主题转移情感识别的方法的可行性和效率。

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