The field of brain signal analysis is moving towards leveraging advanced neural network architectures to improve the accuracy and reliability of diagnosis and detection systems. Recent developments have focused on exploring the optimal representation of electroencephalogram (EEG) data, including time, frequency, and time-frequency domains, to enhance the predictive performance of deep learning models. Furthermore, there is a growing interest in applying graph neural networks to capture brain channel functional connectivity and developing novel biomarkers for depression diagnosis. Additionally, pretraining large language models with self-supervised paradigms has shown promise in enhancing EEG classification performance, particularly in silent speech decoding for active brain-computer interface systems. Noteworthy papers include: The use of Multi-domain Electroencephalogram Representations, which achieved detection metrics exceeding 97% for epileptic seizure detection. The Frequency Feature Fusion Graph Network For Depression Diagnosis also demonstrated improved F1 scores in both real-world and propensity score matched datasets. The Pretraining Large Brain Language Model for Active BCI: Silent Speech paper proposed a novel pretraining paradigm, Future Spectro-Temporal Prediction, which outperformed fully-supervised and pretrained baseline models in word-level and semantic-level classification tasks.