构建脑内电活动解析框架，揭示语言处理的时空动态机制
Neuroprobe: Evaluating Intracranial Brain Responses to Naturalistic Stimuli
- 基于脑树库数据设计多模态解码任务，分析自然刺激下的脑区响应
- 首次实现从听觉特征到语法结构的脑内信息演化可视化，时间分辨率达毫秒级
- 开源评估体系支持神经模型对比，适合脑机接口与神经计算研究者
高分辨率神经数据为下一代脑机接口和神经治疗提供了基础模型可能。然而，当前缺乏针对颅内脑电（iEEG）记录的标准化评估框架。为此，我们提出Neuroprobe：一个用于研究大脑多模态语言处理的解码任务套件。不同于头皮脑电，颅内脑电需通过手术植入电极，可直接记录脑内活动且信号失真极小。Neuroprobe基于BrainTreebank数据集，包含10名受试者在观看自然电影任务中产生的超过40小时的iEEG记录。该工具具备双重功能：一是作为神经科学洞察来源，利用标注的高时空分辨率iEEG，系统定位语言处理各阶段在脑内的发生时间和位置，通过跨时间与所有电极位点的可解码性分析，揭示信息如何从颞上回的语言与音频处理区流向额叶皮层；二是为大规模神经基础模型训练提供严谨的模型与训练策略比较平台。我们已公开Neuroprobe代码，旨在加速该领域进展。公共排行榜：https://neuroprobe.dev/
原文摘要 · Abstract (English)
High-resolution neural datasets enable foundation models for the next generation of brain-computer interfaces and neurological treatments. The community requires rigorous benchmarks to discriminate between competing modeling approaches, yet no standardized evaluation frameworks exist for intracranial EEG (iEEG) recordings. To address this gap, we present Neuroprobe: a suite of decoding tasks for studying multi-modal language processing in the brain. Unlike scalp EEG, intracranial EEG requires invasive surgery to implant electrodes that record neural activity directly from the brain with minimal signal distortion. Neuroprobe is built on the BrainTreebank dataset, which consists of over 40 hours of iEEG recordings from 10 human subjects performing a naturalistic movie viewing task. Neuroprobe serves two critical functions. First, it is a source from which neuroscience insights can be drawn. The high temporal and spatial resolution of the labeled iEEG allows researchers to systematically determine when and where computations for each aspect of language processing occur in the brain by measuring the decodability of each feature across time and all electrode locations. Using Neuroprobe, we visualize how information flows from key language and audio processing sites in the superior temporal gyrus to sites in the prefrontal cortex. We also demonstrate the time evolution of processing from simple auditory features (e.g., pitch and volume) to more complex language features (e.g., part of speech) in a purely data-driven manner. Second, as the field moves toward neural foundation models trained on large-scale datasets, Neuroprobe provides a rigorous framework for comparing competing architectures and training protocols. We make the code for Neuroprobe openly available, aiming to enable rapid progress in the field of iEEG foundation models. Public leaderboard: https://neuroprobe.dev/
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