Journal of neuroscience methods · 2026
EEG-AI: An agentic system for AI-assisted semi-automated EEG preprocessing and artifact removal.
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Abdou Abdelrahman, Ivanov Martin, Shaya Sarmed, Rueda Alice, Nezhad Fatemeh Gholamali, Demchenko Ilya, Kamaleddin Mohammad Amin, Frewen Paul A, Dunkley Benjamin T, Brady Brendan, Nicholson Andrew, Jetly Rakesh, Sharma Divya, Krishnan Sridhar, Bhat Venkat
Аннотация
BACKGROUND: EEG is widely used to identify neural markers, personalize treatments, and evaluate interventions. However, low signal-to-noise ratio and mixing of artifactual distorted interpretation. Traditional preprocessing approaches, including Independent Component Analysis (ICA), require extensive manual inspection and are laborious. This study aims to develop a human-in-the-loop preprocessing framework, in which AI agents assist experts in identifying, labeling, and iteratively refining artifact removal decisions.
NEW METHOD: The proposed framework integrates a large language model (LLM)-driven decision-making agent that calls EEG analysis tools to manage complex signal mixtures. The system combines standard preprocessing with an iterative reasoning loop, where the agent interprets probabilistic outputs from multiple classifiers to decide which components to retain or reject, and whether rerun is needed. Each iteration is re-evaluated through a closed-loop policy. This structure enables adaptive artifact correction guided by interpretable reasoning steps and continuous feedback from both model ensemble and human expert, ensuring reproducibility, auditability, and improved preprocessing efficiency.
RESULTS: Evaluations were conducted using both synthetic EEG data and expert-annotated empirical datasets to assess artifact detection, ICA classification accuracy, and reconstruction quality.
COMPARISON WITH EXISTING METHODS: Across these tests, the AI agent system has consistently outperformed conventional preprocessing pipelines, achieving equivalent or better signal cleaning of artifactual components achieving a Person's r of 0.666 ± 0.188, and RMSE of 5 × 10-6 ± 1 × 10-6 relative to expert-labeled baselines.
CONCLUSIONS: The AI agent framework streamlines preprocessing while maintaining expert oversight via a closed-loop policy that removes high-confidence artifacts, reconstructs the signal, and re-runs analysis with fixed hyperparameters.
NEW METHOD: The proposed framework integrates a large language model (LLM)-driven decision-making agent that calls EEG analysis tools to manage complex signal mixtures. The system combines standard preprocessing with an iterative reasoning loop, where the agent interprets probabilistic outputs from multiple classifiers to decide which components to retain or reject, and whether rerun is needed. Each iteration is re-evaluated through a closed-loop policy. This structure enables adaptive artifact correction guided by interpretable reasoning steps and continuous feedback from both model ensemble and human expert, ensuring reproducibility, auditability, and improved preprocessing efficiency.
RESULTS: Evaluations were conducted using both synthetic EEG data and expert-annotated empirical datasets to assess artifact detection, ICA classification accuracy, and reconstruction quality.
COMPARISON WITH EXISTING METHODS: Across these tests, the AI agent system has consistently outperformed conventional preprocessing pipelines, achieving equivalent or better signal cleaning of artifactual components achieving a Person's r of 0.666 ± 0.188, and RMSE of 5 × 10-6 ± 1 × 10-6 relative to expert-labeled baselines.
CONCLUSIONS: The AI agent framework streamlines preprocessing while maintaining expert oversight via a closed-loop policy that removes high-confidence artifacts, reconstructs the signal, and re-runs analysis with fixed hyperparameters.