Transfer components between subjects for EEG-based emotion recognition

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.