Emotion Recognition From Multimodal Physiological Signals via Discriminative Correlation Fusion With a Temporal Alignment Mechanism.

Modeling correlations between multimodal physiological signals [e.g., canonical correlation analysis (CCA)] for emotion recognition has attracted much attention. However, existing studies rarely consider the neural nature of emotional responses within physiological signals. Furthermore, during fusion space construction, the CCA method maximizes only the correlations between different modalities and neglects the discriminative information of different emotional states. Most importantly, temporal mismatches between different neural activities are often ignored; therefore, the theoretical assumptions that multimodal data should be aligned in time and space before fusion are not fulfilled. To address these issues, we propose a discriminative correlation fusion method coupled with a temporal alignment mechanism for multimodal physiological signals. We first use neural signal analysis techniques to construct neural representations of the central nervous system (CNS) and autonomic nervous system (ANS). respectively. Then, emotion class labels are introduced in CCA to obtain more discriminative fusion representations from multimodal neural responses, and the temporal alignment between the CNS and ANS is jointly optimized with a fusion procedure that applies the Bayesian algorithm. The experimental results demonstrate that our method significantly improves the emotion recognition performance. Additionally, we show that this fusion method can model the underlying mechanisms in human nervous systems during emotional responses, and our results are consistent with prior findings. This study may guide a new approach for exploring human cognitive function based on physiological signals at different time scales and promote the development of computational intelligence and harmonious human-computer interactions.

STSNet: a novel spatio-temporal-spectral network for subject-independent EEG-based emotion recognition.

How to use the characteristics of EEG signals to obtain more complementary and discriminative data representation is an issue in EEG-based emotion recognition. Many studies have tried spatio-temporal or spatio-spectral feature fusion to obtain higher-level representations of EEG data. However, these studies ignored the complementarity between spatial, temporal and spectral domains of EEG signals, thus limiting the classification ability of models. This study proposed an end-to-end network based on ManifoldNet and BiLSTM networks, named STSNet. The STSNet first constructed a 4-D spatio-temporal-spectral data representation and a spatio-temporal data representation based on EEG signals in manifold space. After that, they were fed into the ManifoldNet network and the BiLSTM network respectively to calculate higher-level features and achieve spatio-temporal-spectral feature fusion. Finally, extensive comparative experiments were performed on two public datasets, DEAP and DREAMER, using the subject-independent leave-one-subject-out cross-validation strategy. On the DEAP dataset, the average accuracy of the valence and arousal are 69.38% and 71.88%, respectively; on the DREAMER dataset, the average accuracy of the valence and arousal are 78.26% and 82.37%, respectively. Experimental results show that the STSNet model has good emotion recognition performance.