MindFormer: Semantic Alignment of Multi-Subject fMRI for Brain Decoding

TL;DR

MindFormer is a novel model that aligns multi-subject fMRI signals to improve brain decoding, enabling more accurate fMRI-to-image and fMRI-to-text generation across individuals.

Contribution

The paper introduces MindFormer, a semantic alignment method with subject-specific tokens and a new embedding scheme, advancing multi-subject fMRI decoding capabilities.

Findings

Generates semantically consistent images and texts across subjects

Surpasses existing models in multi-subject brain decoding accuracy

Maintains high semantic fidelity across diverse subjects

Abstract

Research efforts for visual decoding from fMRI signals have attracted considerable attention in research community. Still multi-subject fMRI decoding with one model has been considered intractable due to the drastic variations in fMRI signals between subjects and even within the same subject across different trials. To address current limitations in multi-subject brain decoding, here we introduce a novel semantic alignment method of multi-subject fMRI signals using so-called MindFormer. This model is specifically designed to generate fMRI-conditioned feature vectors that can be used for conditioning Stable Diffusion model for fMRI- to-image generation or large language model (LLM) for fMRI-to-text generation. More specifically, MindFormer incorporates two key innovations: 1) a subject specific token that effectively capture individual differences in fMRI signals while synergistically combines multi subject fMRI data for training, and 2) a novel feature embedding and training scheme based on the IP-Adapter to extract semantically meaningful features from fMRI signals. Our experimental results demonstrate that MindFormer generates semantically consistent images and text across different subjects. Since our MindFormer maintains semantic fidelity by fully utilizing the training data across different subjects by significantly surpassing existing models in multi-subject brain decoding, this may help deepening our understanding of neural processing variations among individuals.