One Brain, Omni Modalities: Towards Unified Non-Invasive Brain Decoding with Large Language Models
This work addresses the fragmentation in brain decoding for neuroscience and AI researchers by proposing a unified approach, though it appears incremental as it builds on existing LLM frameworks and multimodal fusion techniques.
The paper tackled the problem of unifying non-invasive brain decoding from complementary high-frequency electromagnetic (EEG/MEG) and low-frequency metabolic (fMRI) signals, which are traditionally analyzed in isolation, by introducing NOBEL, a neuro-omni-modal brain-encoding large language model that integrates these heterogeneous signals into a shared semantic embedding space, resulting in higher decoding accuracy than unimodal baselines and strong capabilities in stimulus-aware decoding on datasets like NSD and HAD.
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.