Affective Computing and emotion recognition hold significant importance in healthcare, identity verification, human–computer interaction, and related fields. Accurate identification of emotion is crucial for applications in medicine, education, psychology, and military domains. Electroencephalographic (EEG) signals have gained widespread application in emotion recognition due to their inherent characteristics of being non-concealable and directly reflecting brain activity. In recent years, with the establishment of open datasets and advancements in deep learning, an increasing number of researchers have integrated EEG with deep learning methods for emotion recognition studies. This review summarizes commonly used deep learning models in EEG-based emotion recognition along with their applications in this field, including the design of different network architectures, optimization strategies, and model designs based on EEG signal features. We also discuss limitations from the perspectives of commonality–individuality (C-I) and suggest improvements. The review outlines future research directions and provided a minimal C-I framework to assess models. Through this review, we aim to provide researchers in this field with a comprehensive reference and approach to balance universality and personalization to promote the development of deep learning-based EEG emotion recognition methods.

Goal-directed spatial learning is crucial for the survival of animals, in which the formation of the route from the current location to the goal is one of the central problems. A distributed brain network comprising the hippocampus and prefrontal cortex has been shown to support such capacity, yet it is not fully understood how the most similar brain regions in birds, the hippocampus (Hp) and nidopallium caudolaterale (NCL), cooperate during route formation in goal-directed spatial learning. Hence, we examined neural activity in the Hp-NCL network of pigeons and explored the connectivity dynamics during route formation in a goal-directed spatial task. We found that behavioral changes in spatial learning during route formation are accompanied by modifications in neural patterns in the Hp-NCL network. Specifically, as pigeons learned to solve the task, the spectral power in both regions gradually decreased. Meanwhile, elevated hippocampal theta (5 to 12 Hz) connectivity and depressed connectivity in NCL were also observed. Lastly, the interregional functional connectivity was found to increase with learning, specifically in the theta frequency band during route formation. These results provide insight into the dynamics of the Hp-NCL network during spatial learning, serving to reveal the potential mechanism of avian spatial navigation.

A pigeon robot is an ideal experimental animal for research in flying animal robots. The majority of current research publications have entailed electrical stimulation of the motor nuclei to regulate movement forcibly, and although a “virtual fear” behavior model has been proposed, the structure, location, and function of the nuclei that generate fear emotions remain obscure. Previous studies have shown that the Stratum Griseum Periventriculare (SGP) of pigeons is homologous to the mammalian periaqueductal gray (PAG), which plays an essential role in mammalian fear. To reveal the role of fear mediated by the SGP in behavioral regulation, we evaluated the structure and location of the SGP by histologic identification combined with magnetic resonance imaging, and analyzed the behavior of the SGP by electrical stimulation. Finally, the function of the SGP was verified with escape testing and homing experiments in an open field. Our results showed that the SGP is located in the pigeon midbrain and divided into two subregions, the dorsal part of the stratum griseum periventriculare (SGPd) and the ventral part of the stratum griseum periventriculare (SGPv) (the ranges were AP1.5–4.75 mm, ML1.75–6.75 mm, and DV2.2–7.1 mm), and that wired and wireless electrical stimulation freezing was the dominant behavior. In the escape test, SGP electrical stimulation caused the pigeons to flee to a safe place, while in the open-field homing test, electrical stimulation of the SGP induced evasive behavior in pigeons away from their original homing route. These results confirm that the SGP plays a crucial role in fear, and that electrical stimulation of this nucleus induces corresponding fear behaviors.

Research in reinforcement learning indicates that animals respond differently to positive and negative reward prediction errors, which can be calculated by assuming learning rate bias. Many studies have shown that humans and other animals have learning rate bias during learning, but it is unclear whether and how the bias changes throughout the entire learning process. Here, we recorded the behavior data and the local field potentials (LFPs) in the striatum of five pigeons performing a probabilistic learning task. Reinforcement learning models with and without learning rate biases were used to dynamically fit the pigeons’ choice behavior and estimate the option values. Furthemore, the correlation between the striatal LFPs power and the model-estimated option values was explored. We found that the pigeons’ learning rate bias shifted from negative to positive during the learning process, and the striatal Gamma (31 to 80 Hz) power correlated with the option values modulated by dynamic learning rate bias. In conclusion, our results support the hypothesis that pigeons employ a dynamic learning strategy in the learning process from both behavioral and neural aspects, providing valuable insights into reinforcement learning mechanisms of non-human animals.

Navigation is a complex task in which the hippocampus (Hp), which plays an important role, may be involved in interactions between different frequency bands. However, little is known whether this cross-frequency interaction exists in the Hp of birds during navigation. Therefore, we examined the electrophysiological characteristics of hippocampal cross-frequency interactions of domestic pigeons (Columba livia domestica) during navigation. Two goal-directed navigation tasks with different locomotor modes were designed, and the local field potentials (LFPs) were recorded for analysis. We found that the amplitudes of high-frequency oscillations in Hp were dynamically modulated by the phase of co-occurring theta-band oscillations both during ground-based maze and outdoor flight navigation. The high-frequency amplitude sub-frequency bands modulated by the hippocampal theta phase were different at different tasks, and this process was independent of the navigation path and goal. These results suggest that phase–amplitude coupling (PAC) in the avian Hp may be more associated with the ongoing cognitive demands of navigational processes. Our findings contribute to the understanding of potential mechanisms of hippocampal PAC on multi-frequency informational interactions in avian navigation and provide valuable insights into cross-species evolution.

Model-based decision-making guides organism behavior by the representation of the relationships between different states. Previous studies have shown that the mammalian hippocampus (Hp) plays a key role in learning the structure of relationships among experiences. However, the hippocampal neural mechanisms of birds for model-based learning have rarely been reported. Here, we trained six pigeons to perform a two-step task and explore whether their Hp contributes to model-based learning. Behavioral performance and hippocampal multi-channel local field potentials (LFPs) were recorded during the task. We estimated the subjective values using a reinforcement learning model dynamically fitted to the pigeon’s choice of behavior. The results show that the model-based learner can capture the behavioral choices of pigeons well throughout the learning process. Neural analysis indicated that high-frequency (12–100 Hz) power in Hp represented the temporal context states. Moreover, dynamic correlation and decoding results provided further support for the high-frequency dependence of model-based valuations. In addition, we observed a significant increase in hippocampal neural similarity at the low-frequency band (1–12 Hz) for common temporal context states after learning. Overall, our findings suggest that pigeons use model-based inferences to learn multi-step tasks, and multiple LFP frequency bands collaboratively contribute to model-based learning. Specifically, the high-frequency (12–100 Hz) oscillations represent model-based valuations, while the low-frequency (1–12 Hz) neural similarity is influenced by the relationship between temporal context states. These results contribute to our understanding of the neural mechanisms underlying model-based learning and broaden the scope of hippocampal contributions to avian behavior.