# BrainFLORA: Uncovering Brain Concept Representation via Multimodal Neural Embeddings

arXiv: https://arxiv.org/abs/2507.09747 · HTML: https://mlsanity.com/paper/2507.09747/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Jul 2025.

## Built on by (most cited first, 6 of 6)

- [Unveiling Deep Semantic Uncertainty Perception for Language-Anchored Multi-modal Vision-Brain Alignment](https://mlsanity.com/paper/2511.04078/) (2025)
- [Moving Beyond Diffusion: Hierarchy-to-Hierarchy Autoregression for fMRI-to-Image Reconstruction](https://mlsanity.com/paper/2510.22335/) (2025)
- [Neural Visual Decoding via Cognitive guided Adaptive Blurring and Information Constrained Alignment](https://mlsanity.com/paper/2605.16418/) (2026)
- [BrainJanus: A Unified Model for Understanding and Generation across Brain, Vision, and Language](https://mlsanity.com/paper/2606.30319/) (2026)
- [FSDBN: Foreground-Aware EEG-Visual Alignment via Dynamic Brain Networks](https://mlsanity.com/paper/2607.18344/) (2026)
- [Replaying the Movie in Your Mind Decoding Dynamic Visual Perception and Recall from EEG](https://mlsanity.com/paper/2610.13120/) (2026)

## Abstract (arXiv)

Understanding how the brain represents visual information is a fundamental challenge in neuroscience and artificial intelligence. While AI-driven decoding of neural data has provided insights into the human visual system, integrating multimodal neuroimaging signals, such as EEG, MEG, and fMRI, remains a critical hurdle due to their inherent spatiotemporal misalignment. Current approaches often analyze these modalities in isolation, limiting a holistic view of neural representation. In this study, we introduce BrainFLORA, a unified framework for integrating cross-modal neuroimaging data to construct a shared neural representation. Our approach leverages multimodal large language models (MLLMs) augmented with modality-specific adapters and task decoders, achieving state-of-the-art performance in joint-subject visual retrieval task and has the potential to extend multitasking. Combining neuroimaging analysis methods, we further reveal how visual concept representations align across neural modalities and with real world object perception. We demonstrate that the brain's structured visual concept representations exhibit an implicit mapping to physical-world stimuli, bridging neuroscience and machine learning from different modalities of neural imaging. Beyond methodological advancements, BrainFLORA offers novel implications for cognitive neuroscience and brain-computer interfaces (BCIs). Our code is available at https://github.com/ncclab-sustech/BrainFLORA.