The exploration of brain activity and its decoding from fMRI data has been a longstanding pursuit, driven by its potential applications in brain-computer interfaces, medical diagnostics, and virtual reality. Previous approaches have primarily focused on individual subject analysis, highlighting the need for a more universal and adaptable framework, which is the core motivation behind our work. In this work, we propose fMRI-PTE, an innovative auto-encoder approach for fMRI pre-training, with a focus on addressing the challenges of varying fMRI data dimensions due to individual brain differences. Our approach involves transforming fMRI signals into unified 2D representations, ensuring consistency in dimensions and preserving distinct brain activity patterns. We introduce a novel learning strategy tailored for pre-training 2D fMRI images, enhancing the quality of reconstruction. fMRI-PTE's adaptability with image generators enables the generation of well-represented fMRI features, facilitating various downstream tasks, including within-subject and cross-subject brain activity decoding. Our contributions encompass introducing fMRI-PTE, innovative data transformation, efficient training, a novel learning strategy, and the universal applicability of our approach. Extensive experiments validate and support our claims, offering a promising foundation for further research in this domain.

翻译：从fMRI数据中探索大脑活动及其解码一直是长期追求的目标，其潜在应用涵盖脑机接口、医学诊断和虚拟现实等领域。以往方法主要聚焦于单个被试分析，凸显了对更通用且适应性框架的需求，这正是我们工作的核心动机。本文提出fMRI-PTE——一种创新的fMRI预训练自编码器方法，旨在解决因个体大脑差异导致的fMRI数据维度变化问题。我们的方法通过将fMRI信号转化为统一的二维表征，在保证维度一致性的同时保留独特的大脑活动模式。我们引入了一种专为预训练二维fMRI图像设计的新型学习策略，提升了重建质量。fMRI-PTE与图像生成器的适配性使其能够生成高质量表征的fMRI特征，从而支持各类下游任务，包括被试内与跨被试的大脑活动解码。我们的贡献包括：提出fMRI-PTE框架、创新性数据转换方法、高效训练策略、新型学习机制，以及本方法的普适性。大量实验验证并支撑了我们的论断，为该领域的进一步研究奠定了坚实基础。