NeuroStream: A video-inspired causal spatiotemporal encoder for streaming EEG

This paper presents a hybrid-architecture foundation model for multichannel EEG that models spatiotemporal features separately. EEG foundation models have developed rapidly in recent years, and most adopt ViT-like paradigms: they treat continuous EEG signals as static patch grids, divide the signal into patches (tokens), flatten them into a sequence, and feed the sequence into a Transformer. This approach requires users to carefully set the temporal window size for each task to obtain the best performance, and the window size cannot be changed while the model is operating. Unlike static images, EEG is structurally more similar to video, with distinct temporal and spatial dimensions. Inspired by work on streaming video understanding, we regard continuous EEG signals as short frames arriving over time and process their spatial and temporal dimensions differently: within each frame, a Transformer models spatial relationships among electrode channels, while a causal Mamba state-space module maintains cross-frame temporal states for each channel. When a new frame arrives, the model only needs to perform incremental computation. It can accommodate EEG signals of different lengths and, through learnable state-transition parameters, form task-related effective context ranges, thereby reducing reliance on preset window lengths. We construct a self-supervised pretraining task on large-scale EEG datasets: next-frame amplitude-spectrum prediction. Given historical EEG frames, the model predicts the amplitude spectrum of each channel in the next frame, frame by frame, enabling it to learn cross-frame frequency-domain changes in continuous EEG signals. We conduct experiments on seizure detection, sleep staging, and emotion recognition. The results show that the proposed pretrained model transfers effectively to different downstream tasks, achieving a **balanced accuracy above 95%** and an **AUROC of 0.99**, particularly on the cross-subject CHB-MIT seizure-monitoring task.