The field of EEG-based research and brain-computer interfaces is rapidly advancing, with a focus on developing more accurate and efficient models for various applications. Recent studies have explored the use of deep learning techniques, such as CNN-LSTM models and Bi-GRU neural networks, to improve the classification of EEG signals for tasks like Parkinson's disease diagnosis and deception detection. Additionally, researchers are investigating the integration of brain foundation models with brain-computer interfaces to enable transformative applications like thought-controlled devices and neuroprosthetics. The development of novel approaches, such as spatial-temporal transformers and temporal basis function models, is also underway to address challenges in EEG-based emotion recognition and closed-loop neural stimulation. Noteworthy papers include the proposal of a fiduciary AI framework for brain foundation models, which ensures that these systems act in users' best interests, and the development of a high-density EEG system that enables fast visual brain-computer interfaces. Overall, the field is moving towards more innovative and effective solutions for EEG-based research and brain-computer interfaces.