EFiMAS: An Evidence-Guided Multi-Agent System for Task-Conditioned EEG–fNIRS Fusion

While EEG and fNIRS provide complementary views of neural processes, the cross-modal relationships of interest vary depending on the task and available measurement information. Existing methods typically rely on predefined computational structures, limiting their ability to adapt fusion design choices to the current task and recorded data. To address this, we investigate EEG–fNIRS fusion as a task-conditioned problem of program construction and revision. We introduce EFiMAS, an evidence-guided multi-agent system. Using dual-index Retrieval-Augmented Generation (RAG), role-specific agents in EFiMAS retrieve neuroscience evidence to propose and independently verify a modeling relationship, while utilizing method evidence to construct and dynamically revise the executable pathway based on observed outputs. Empirically, evaluations on ten public EEG–fNIRS tasks, along with comprehensive component ablations and execution trace analyses, demonstrate that EFiMAS effectively synthesizes task-specific programs and outperforms both dedicated fusion networks and foundation models.