基于正交回归和特征加权的脑电情感特征选择方法

The volume conduction effects of the human head result in highly correlated information among most EEG features. These highly correlated EEG features cannot provide additional useful information for emotion recognition and may reduce efficiency. This paper proposes a novel EEG emotional feature selection method called feature selection with orthogonal regression (FSOR) to reduce redundant information and select discriminative EEG features. Compared to common feature selection approaches, FSOR can utilize orthogonal regression to keep more discriminative information in the projection subspace for nonlinear and non-stationary EEG signals. To demonstrate the performance of our approach, we collected multichannel EEG recordings for emotion recognition and compared FSOR with four classical EEG feature selection approaches. The experimental results confirmed that the FSOR method outperformed the others in removing redundant features from the original EEG features. Furthermore, we found that the frequency at maximum power spectral density is the most discriminative EEG emotional feature. This discovery will inspire future studies on EEG emotional feature extraction.