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arXiv:2608.04156v1 Announce Type: new Abstract: Electroencephalography (EEG) analysis extends beyond assigning predefined labels to recordings; it requires workflows connecting natural-language instructions, signal proce
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传统的 EEG 分析往往局限于为脑电信号片段贴上预设标签，但真实的临床与科研场景要求模型能将自然语言指令、信号处理、量化证据及科学解读串联成一个完整的工作流。来自 arXiv 的最新研究将这种端到端的处理能力定义为“全面 EEG 理解”，并指出此前缺乏统一的量化标准来衡量大语言模型在这一复杂任务上的表现 [arxiv.org]。为此，研究团队推出了 BrainBench 基准，它专为指令驱动的 EEG 综合理解而设计，涵盖了从信号分析到科学结论生成的多个环节。
该基准的构建逻辑，呼应了脑科学领域对自动化研究流程的探索。相关研究指出，语言模型在预测神经科学实验结果方面已显示出潜力，但这仅仅是多步骤研究过程中的一环，实验室特有的非书面化经验与操作惯例仍是自动化面临的挑战 [arxiv.org]。BrainBench 的出现，正是为了在更贴近真实科研推理的层面上，检验大模型能否像人类专家一样，将碎片化的信号证据转化为有意义的科学叙述。
值得注意的是，该研究目前以预印本形式发布，尚未经过同行评议。在医学循证体系中，此类基准测试属于方法学构建与性能评估研究，其结论需谨慎解读。它并不直接涉及任何临床诊断或治疗决策，但为未来开发辅助脑电分析的 AI 工具提供了标准化的“考卷”。不过，有评论也提醒，若研究过度依赖大语言模型可预测的领域，可能会窄化科学家提出的问题范围，使其偏离真正的未知领域 [facebook.com]。因此，BrainBench 的价值更在于为模型能力划定边界，而非替代人类专家的判断。
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arXiv:2608.04156v1 Announce Type: new Abstract: Electroencephalography (EEG) analysis extends beyond assigning predefined labels to recordings; it requires workflows connecting natural-language instructions, signal processing, quantitative evidence, and scientific interpretation. We term this capability . Existing evaluations, however, primarily target isolated decoding tasks or system-specific demonstrations, leaving the competence of large language models (LLMs) insufficiently quantified. We introduce , a unified benchmark for comprehensive, instruction-conditioned EEG understanding. It comprises four subsets---Foundational Analysis, Sleep Assessment, Neurocognitive Assessment, and Physiological Integration---covering 17 datasets, tasks, and over real-data instances. Given an instruction and EEG recordings with optional physiological signals, a system must perform the analysis and produce a scientifically grounded report and, when required, artifacts. Outputs are assessed through numerical, categorical, set, sequence, semantic, and artifact validation. We evaluate representative LLMs across more than 100K executions under two paradigms: autonomous code execution with CodeAct and structured agentic analysis with BrainAgent. Results vary substantially across models, subsets, difficulty levels, and execution paradigms, showing that EEG competence depends on the model and its operationalization. provides a reproducible testbed for advancing LLM-based EEG understanding. The code and benchmark will be released soon, with evaluation results continuously updated.