  In the era of Artificial Intelligence Generated Content (AIGC), conditional
multimodal synthesis technologies (e.g., text-to-image, text-to-video,
text-to-audio, etc) are gradually reshaping the natural content in the real
world. The key to multimodal synthesis technology is to establish the mapping
relationship between different modalities. Brain signals, serving as potential
reflections of how the brain interprets external information, exhibit a
distinctive One-to-Many correspondence with various external modalities. This
correspondence makes brain signals emerge as a promising guiding condition for
multimodal content synthesis. Brian-conditional multimodal synthesis refers to
decoding brain signals back to perceptual experience, which is crucial for
developing practical brain-computer interface systems and unraveling complex
mechanisms underlying how the brain perceives and comprehends external stimuli.
This survey comprehensively examines the emerging field of AIGC-based
Brain-conditional Multimodal Synthesis, termed AIGC-Brain, to delineate the
current landscape and future directions. To begin, related brain neuroimaging
datasets, functional brain regions, and mainstream generative models are
introduced as the foundation of AIGC-Brain decoding and analysis. Next, we
provide a comprehensive taxonomy for AIGC-Brain decoding models and present
task-specific representative work and detailed implementation strategies to
facilitate comparison and in-depth analysis. Quality assessments are then
introduced for both qualitative and quantitative evaluation. Finally, this
survey explores insights gained, providing current challenges and outlining
prospects of AIGC-Brain. Being the inaugural survey in this domain, this paper
paves the way for the progress of AIGC-Brain research, offering a foundational
overview to guide future work.
