PNN for EEG-based Emotion Recognition

The effort to integrate emotions into human computer interaction (HCI) system has attracted broad attentions. Automatic emotion recognition enables the HCI to become more intelligent and user friendly. Although numerous studies have been performed in this field, emotion recognition is still an extremely challenging task, especially in real-world practice usage. In this work, probabilistic neural network (PNN), with advantage of simple, efficient, and easy to train, was employed to recognize emotions elicited by watching music videos from scalp EEG. The publicly available DEAP emotion database was used to validate our algorithms. The powers of 4 frequency bands of EEG were extracted as features. The results show that the mean classification accuracy of PNN is 81.21% for valence(>= 5 and <5) and 81.26% for arousal(>= 5 and <5) across 32 subjects, similar with the results of SVM. In addition, they demonstrate that higher frequency bands (beta and gamma) play more important role in emotion classification than lower ones (theta and alpha). For the purpose of practical emotion recognition system, we proposed a ReliefF-based channel selection algorithm to reduce the number of used channels for convenience in practical usage. The results show that while using PNN, the 98% of the maximum classification accuracy can be obtained with only 9 (for valence) and 8 (for arousal) best channels, however, 19 (for valence) and 14 (for arousal) channels are needed while using SVM.