![[2602.13473] NeuroWeaver: An Autonomous Evolutionary Agent for Exploring the Programmatic Space of EEG Analysis Pipelines](https://arxiv.org/static/browse/0.3.4/images/arxiv-logo-fb.png){kind=logo}
[2602.13473] NeuroWeaver: An Autonomous Evolutionary Agent for Exploring the Programmatic Space of EEG Analysis Pipelines
Summary
NeuroWeaver is an autonomous evolutionary agent designed to optimize EEG analysis pipelines, addressing data constraints and computational costs while outperforming traditional methods.
Why It Matters
This research is significant as it presents a novel approach to EEG analysis, leveraging evolutionary algorithms to enhance performance and efficiency in clinical settings. By integrating neurophysiological priors, NeuroWeaver aims to make EEG analysis more accessible and effective, particularly in resource-limited environments.
Key Takeaways
- NeuroWeaver reformulates EEG pipeline engineering as a constrained optimization problem.
- It employs Domain-Informed Subspace Initialization to ensure scientifically plausible solutions.
- The agent balances performance, novelty, and efficiency through self-reflective refinement.
- Empirical evaluations show NeuroWeaver outperforms state-of-the-art methods with fewer parameters.
- This approach could significantly enhance EEG analysis in clinical applications.
Computer Science > Artificial Intelligence arXiv:2602.13473 (cs) [Submitted on 13 Feb 2026] Title:NeuroWeaver: An Autonomous Evolutionary Agent for Exploring the Programmatic Space of EEG Analysis Pipelines Authors:Guoan Wang, Shihao Yang, Jun-En Ding, Hao Zhu, Feng Liu View a PDF of the paper titled NeuroWeaver: An Autonomous Evolutionary Agent for Exploring the Programmatic Space of EEG Analysis Pipelines, by Guoan Wang and 4 other authors View PDF HTML (experimental) Abstract:Although foundation models have demonstrated remarkable success in general domains, the application of these models to electroencephalography (EEG) analysis is constrained by substantial data requirements and high parameterization. These factors incur prohibitive computational costs, thereby impeding deployment in resource-constrained clinical environments. Conversely, general-purpose automated machine learning frameworks are often ill-suited for this domain, as exploration within an unbounded programmatic space fails to incorporate essential neurophysiological priors and frequently yields solutions that lack scientific plausibility. To address these limitations, we propose NeuroWeaver, a unified autonomous evolutionary agent designed to generalize across diverse EEG datasets and tasks by reformulating pipeline engineering as a discrete constrained optimization problem. Specifically, we employ a Domain-Informed Subspace Initialization to confine the search to neuroscientifically plausible manifolds...
