Electroencephalography (EEG) has become widely used in health monitoring and emotion recognition. Accurately capturing temporal patterns at multiple scales, together with spatial interactions across electrodes, is crucial for boosting predictive accuracy. However, current approaches rarely handle both short-term and long-term dependencies while simultaneously modeling cross-channel spatial features. To address this gap, we propose the Kolmogorov-Arnold Network-Mamba-Mixture of Experts Network (KMM-Net). Specifically, multi-scale One-Dimensional Convolutional Neural Network and Two-Dimensional Convolutional Neural Network layers independently extract temporal and spatial features. Meanwhile, Mamba modules in combination with the KANLayer capture more intricate dependencies. Finally, a gating network dynamically fuses the outputs of these two experts, ensuring an optimal balance between temporal and spatial information. Experimental results on SEED and SEED-IV datasets show that KMM-Net consistently outperforms the latest methods in Accuracy, Recall, Precision, F1 Score, and Area Under the Curve (AUC). These results demonstrate that our model more comprehensively captures EEG signals' multi-scale temporal dynamics and spatial interactions. Moreover, its design offers a novel paradigm for EEG-based emotion recognition and establishes a solid technical foundation for real-time monitoring and future applications, such as predicting mental disorders.

For a specific online optimization problem, for example, online bipartite matching (OBM), research efforts could be made in two directions before it is finally closed, i.e., the optimal competitive online algorithm is found. One is to continuously design algorithms with better performance. To this end, reinforcement learning (RL) has demonstrated great success in literature. However, little is known on the other direction: whether RL helps explore how hard an online problem is. In this paper, we study a generalized model of OBM, named online matching with stochastic rewards (OMSR, FOCS 2012), for which the optimal competitive ratio is still unknown. We adopt an adversarial RL approach that trains two RL agents adversarially and iteratively: the algorithm agent learns for algorithms with larger competitive ratios, while the adversarial agent learns to produce a family of hard instances. Through such a framework, agents converge at the end with a robust algorithm, which empirically outperforms the state of the art (STOC 2020). Much more significantly, it allows to track how the hard instances are generated. We succeed in distilling two structural properties from the learned graph patterns, which remarkably reduce the action space, and further enable theoretical improvement on the best-known hardness result of OMSR, from $0.621$ (FOCS 2012) to $0.597$. To the best of our knowledge, this gives the first evidence that RL can help enhance the theoretical understanding of an online problem.