The gated recurrent unit (GRU) network can effectively capture temporal information for 1D signals, such as electroencephalography and event-related brain potential, and it has been widely used in the field of EEG emotion recognition. However, multi-domain features, including the spatial, frequency, and temporal features of EEG signals, contribute to emotion recognition, while GRUs show some limitations in capturing frequency–spatial features. Thus, we proposed a hybrid architecture of convolutional neural networks and GRUs (CGRU) to effectively capture the complementary temporal features and spatial–frequency features hidden in signal channels. In addition, to investigate the interactions among different brain regions during emotional information processing, we considered the functional connectivity relationship of the brain by introducing a phase-locking value to calculate the phase difference between the EEG channels to gain spatial information based on functional connectivity. Then, in the classification module, we incorporated attention constraints to address the issue of the uneven recognition contribution of EEG signal features. Finally, we conducted experiments on the DEAP and DREAMER databases. The results demonstrated that our model outperforms the other models with remarkable recognition accuracy of 99.51%, 99.60%, and 99.59% (58.67%, 65.74%, and 67.05%) on DEAP and 98.63%, 98.7%, and 98.71% (75.65%, 75.89%, and 71.71%) on DREAMER in a subject-dependent experiment (subject-independent experiment) for arousal, valence, and dominance.

Aiming at the shortcomings of EEG emotion recognition models in feature representation granularity and spatiotemporal dependence modeling, a multimodal emotion recognition model integrating multi-scale feature representation and attention mechanism is proposed. The model consists of a feature extraction module, feature fusion module, and classification module. The feature extraction module includes a multi-stream network module for extracting shallow EEG features and a dual-scale attention module for extracting shallow EOG features. The multi-scale and multi-granularity feature fusion improves the richness and discriminability of multimodal feature representation. Experimental results on two datasets show that the proposed model outperforms the existing model.

Speech signals are nonlinear chaotic time series. This paper proposes a novel speech signal nonlinear prediction model with the hidden phase space reconstruction method. The parameters, embedding dimension m, time delay τ and model structure are solved simultaneously, breaking the restriction of phase space, which needs to be reconstructed before modeling for the existing prediction method. Subsequently, an explicit speech signal prediction model is generated. Meanwhile, the introduction of the frame length parameter k effiectively extends the prediction length. Experimental results show that the values of m and τ solved by the proposed method are consistent with the values addressed by the Cao method and mutual information method, respectively. In addition, the optimal value of k is further discussed. The prediction results obtained using the proposed model are more accurate than those of linear prediction coding, the radial basis function neural network model and the long short-term memory network.