[Article] MindLLM: A Subject-Agnostic and Versatile Model for fMRI-to-Text Decoding

Summary: In this seminar, we will explore a novel fMRI-to-text decoding framework named MindLLM. It combines a neuroscience-informed, subject-agnostic fMRI encoder with an off-the-shelf large language model to translate brain activity into coherent text. It introduces Brain Instruction Tuning (BIT), which enriches the model’s capacity to extract and represent diverse semantic information from fMRI signals, enabling versatile decoding across different tasks and subjects. We will discuss how to implement these techniques in our study. Qiu, Weikang, et al. “MindLLM: A Subject-Agnostic and Versatile Model for fMRI-to-Text Decoding.” arXiv preprint arXiv:2502.15786 (2025).