Brain Foundation Models: A Survey on Advancements in Neural Signal Processing and Brain Discovery

Brain foundation models (BFMs) represent a transformative paradigm in computational neuroscience that leverages large-scale pretraining on diverse neural signals to achieve robust generalization across tasks, modalities, and experimental contexts. This survey article establishes the first comprehensive definition and framework for BFMs, systematically examining their construction, core methodologies, and applications. We present key approaches for data processing and training strategies alongside diverse applications spanning brain decoding and scientific discovery. Through critical analysis of recent methodological innovations, we identify fundamental challenges that must be addressed to realize the full potential of BFMs, including advancing data quality and standardization, optimizing model architectures, improving training efficiency, and enhancing interpretability. By bridging the gap between neuroscience and artificial intelligence (AI), BFMs present unprecedented opportunities to revolutionize brain research, clinical diagnostics, and therapeutic interventions. This article serves as a foundational reference for researchers and practitioners seeking to understand and advance this emerging field.