Spectral power analysis plays a predominant role in electroencephalogram-based emotional recognition. It can reflect activity differences among multiple brain regions. In addition to activation difference, different emotions also involve different large-scale network during related information processing. In this paper, both information propagation patterns and activation difference in the brain were fused to improve the performance of emotional recognition. We constructed emotion-related brain networks with phase locking value and adopted a multiple feature fusion approach to combine the compensative activation and connection information for emotion recognition. Recognition results on three public emotional databases demonstrated that the combined features are superior to either single feature based on power distribution or network character. Furthermore, the conducted feature fusion analysis revealed the common characters between activation and connection patterns involved in the positive, neutral, and negative emotions for information processing. The proposed feasible combination of both information propagation patterns and activation difference in the brain is meaningful for developing the effective human-computer interaction systems by adapting to human emotions in the real world applications.

The importance of self-feedback autaptic transmission in modulating spike-time irregularity is still poorly understood. By using a biophysical model that incorporates autaptic coupling, we here show that self-innervation of neurons participates in the modulation of irregular neuronal firing, primarily by regulating the occurrence frequency of burst firing. In particular, we find that both excitatory and electrical autapses increase the occurrence of burst firing, thus reducing neuronal firing regularity. In contrast, inhibitory autapses suppress burst firing and therefore tend to improve the regularity of neuronal firing. Importantly, we show that these findings are independent of the firing properties of individual neurons and as such can be observed for neurons operating in different modes. Our results provide an insightful mechanistic understanding of how different types of autapses shape irregular firing at the single-neuron level and they highlight the functional importance of autaptic self-innervation in taming and modulating neurodynamics.