M²-EEG: A Macro–Micro Pretraining Paradigm for Expanding the Temporal Scope of EEG Foundation Models

Electroencephalography (EEG) foundation models have advanced transferable representation learning, yet pretraining and evaluation remain centered largely on short local segments. For recording-scale decisions, aggregating independently encoded windows leaves local representations unaware of context elsewhere in the recording. We introduce M²-EEG, a Macro–Micro pretraining paradigm built on the principle that waveform resolution and temporal reach should be modeled separately. A high-resolution Micro pathway preserves local waveform evidence, while a lower-bandwidth Macro pathway models recording-wide context. Aligned fusion produces context-aware temporal and recording-level representations within a single whole-recording backbone pass. Using a 5,300-hour quality-controlled EEG corpus, M²-EEG is progressively pretrained from local representation learning to long-context modeling and joint Macro–Micro adaptation using a unified family of multi-scale spectral prediction objectives. Across six long-context EEG datasets, frozen M²-EEG representations outperform those of the compared EEG foundation models under aligned downstream readouts, while retaining strong short-window transfer with the same complete backbone. Controlled context-span experiments further show that expanding contextual support improves predictions even when local EEG evidence is held fixed. Together, these results support Macro–Micro pretraining as a scalable approach to extending EEG foundation learning from local segments toward recording-scale interpretation.