The field of electroencephalography (EEG) research is moving towards the development of more advanced and efficient methods for analyzing and interpreting EEG data. One of the key areas of focus is the creation of foundation models that can be used for a variety of tasks, such as classification and regression. These models are being designed to be flexible and adaptable, allowing them to be used with different types of EEG data and in different contexts. Another area of focus is the development of new methods for tracking and estimating brain activity, including the use of Kalman filtering and kinematics modeling. Self-supervised learning is also being explored as a way to learn EEG representations from unlabeled data, reducing the need for expensive annotations. Notable papers include: STAMP: Spatial-Temporal Adapter with Multi-Head Pooling, which introduces a novel adapter that leverages univariate embeddings produced by a general time series foundation model to achieve performance comparable to state-of-the-art EEG-specific foundation models. Tracking EEG Thalamic and Cortical Focal Brain Activity using Standardized Kalman Filtering with Kinematics Modeling, which proposes a new method for estimating brain activity using EEG recordings that reduces depth bias and allows for smoother and more physically plausible estimates. Learning the relative composition of EEG signals using pairwise relative shift pretraining, which introduces a novel pretext task that predicts relative temporal shifts between randomly sampled EEG window pairs and encourages encoders to capture relative temporal composition and long-range dependencies inherent in neural signals.