One Brain, Omni Modalities: Towards Unified Non-Invasive Brain Decoding with Large Language Models

TL;DR

NOBEL introduces a unified large language model that integrates EEG, MEG, and fMRI signals for comprehensive, multimodal brain decoding, improving accuracy and interpretability of neural activity and sensory stimuli.

Contribution

This work presents a novel architecture that unifies heterogeneous brain signals within an LLM framework, enabling holistic and multimodal brain decoding beyond isolated analysis.

Findings

Fusion of electromagnetic and metabolic signals improves decoding accuracy.

NOBEL effectively interprets visual semantics from fMRI data.

The model demonstrates strong stimulus-aware decoding capabilities.

Abstract

Deciphering brain function through non-invasive recordings requires synthesizing complementary high-frequency electromagnetic (EEG/MEG) and low-frequency metabolic (fMRI) signals. However, despite their shared neural origins, extreme discrepancies have traditionally confined these modalities to isolated analysis pipelines, hindering a holistic interpretation of brain activity. To bridge this fragmentation, we introduce \textbf{NOBEL}, a \textbf{n}euro-\textbf{o}mni-modal \textbf{b}rain-\textbf{e}ncoding \textbf{l}arge language model (LLM) that unifies these heterogeneous signals within the LLM's semantic embedding space. Our architecture integrates a unified encoder for EEG and MEG with a novel dual-path strategy for fMRI, aligning non-invasive brain signals and external sensory stimuli into a shared token space, then leverages an LLM as a universal backbone. Extensive evaluations demonstrate that NOBEL serves as a robust generalist across standard single-modal tasks. We also show that the synergistic fusion of electromagnetic and metabolic signals yields higher decoding accuracy than unimodal baselines, validating the complementary nature of multiple neural modalities. Furthermore, NOBEL exhibits strong capabilities in stimulus-aware decoding, effectively interpreting visual semantics from multi-subject fMRI data on the NSD and HAD datasets while uniquely leveraging direct stimulus inputs to verify causal links between sensory signals and neural responses. NOBEL thus takes a step towards unifying non-invasive brain decoding, demonstrating the promising potential of omni-modal brain understanding.