Hands-free motor imagery EEG classification via LLM multi-agents

Motor imagery (MI) brain-computer interfaces (BCI) rely on precise electroencephalogram (EEG) classification. However, issues such as the reliance on extensive manual experience for MI-EEG model design, parameter tuning, and optimization directions, along with the poor task flexibility of foundation models and state degradation during long-term multi-agent iterations, severely restrict the state-of-the-art (SOTA) efficiency of MI-EEG.

To address these challenges, we propose AutoMI, a novel framework that uses multi-agent automated rapid iterations to construct SOTA MI-EEG models. AutoMI introduces a hybrid decision mechanism that tightly couples Q-learning strategies with deterministic rules. By integrating planning, execution, and output agents with predefined tools, AutoMI ensures broad general applicability across various hyperparameter optimizations and structural improvements. Furthermore, AutoMI integrates experience tracking and rollback mechanisms to prevent ambiguous optimization.

In evaluations on the IV2a, OpenBMI, and ECUST-MI datasets, the SOTA models finally constructed through AutoMI iterations achieve accuracies of 77.62%, 78.08%, and 83.02%, with maximum improvement reaching 24.69%, 23.35%, and 23.28% respectively. Furthermore, the average time per iteration for a single subject on the OpenBMI dataset is approximately 500 s.

Compared with automated optimization algorithms, the accuracies increase by 18.42%, 9.27%, and 19.25% respectively, demonstrating the effectiveness of the proposed AutoMI framework and proving that its optimization capability reaches SOTA.

Experimental results indicate that AutoMI provides a novel perspective and framework design reference for future BCI model optimization.