Deep feature pyramid network for EEG emotion recognition

Abstract

In this work, the Feature Pyramid Network (FPN) is proposed for improving the performance of electroencephalography (EEG) emotion recognition. Differential Entropy (DE) is extracted from each EEG channel as the basic feature. Then the feature matrix was constructed through biharmonic spline interpolation to obtain the correlation information between EEG channels. Next, the proposed FPN is applied to fuse multiscale EEG spatial information for obtaining deeper semantic features. FPN integrates the semantic information of low-level and high-level feature maps through up-sampling and lateral connections. It can reduce the semantic gap between feature maps and increase the receptive field of low-resolution feature maps. Finally, a linear Support Vector Machine (SVM) is utilized for emotion recognition. The experiment results of the proposed method have a state-of-the-art performance of valence (94.29%) and arousal (96.97%) by 70–30 cross-validation on the Database for Emotion Analysis using Physiological Signals (DEAP). And the accuracy of 97.12% was obtained on the SJTU Emotion EEG Dataset (SEED). Moreover, we carried out the LOSO CV experiment on DEAP. The accuracy of valence and arousal reached 80.38% and 82.33%, respectively.