EvoBrain: Continual Learning of EEG Foundation Models Across Heterogeneous BCI Tasks

Electroencephalography (EEG) is the predominant modality for non-invasive Brain-Computer Interfaces (BCIs) due to its high temporal resolution. However, conventional decoding approaches rely on fragmented, task-specific architectures, limiting scalability across heterogeneous BCI tasks. EEG foundation models pre-trained on large-scale corpora offer a promising solution for universal brain decoding, yet current post-training strategies remain dominated by task-isolated fine-tuning. This static paradigm restricts knowledge transfer across tasks and incurs computational and storage costs that grow linearly with the number of downstream tasks.In this paper, we formulate downstream adaptation of EEG foundation models as a cross-task continual learning problem and propose EvoBrain, a dynamic task-aware post-training framework that enables a unified foundation model to continually generalize across diverse EEG tasks. EvoBrain addresses the plasticity-stability trade-off through two complementary components. Neuro-Spectral Task Normalization (NSN) facilitates adaptation to distributional and neuro-spectral shifts by aligning task statistics and recalibrating spectral responses. Response-Affinity Distillation (RAD), together with time-dependent replay, preserves historical task knowledge while promoting selective transfer between spectrally compatible tasks, thereby mitigating catastrophic forgetting. Extensive evaluations on six distinct BCI tasks demonstrate that our approach consistently surpasses state-of-the-art methods across diverse foundation backbones, effectively balancing the plasticity-stability trade-off.