An electroencephalogram (EEG) records the spatially averaged electrical activity of neurons in the brain, measured from the human scalp. Prior studies have explored EEG-based classification of objects or concepts, often for passive viewing of briefly presented image or video stimuli, with limited classes. Because EEG exhibits a low signal-to-noise ratio, recognizing fine-grained representations across a large number of classes remains challenging; however, abstract-level object representations may exist. In this work, we investigate whether EEG captures object representations across multiple hierarchical levels, and propose episodic analysis, in which a Machine Learning (ML) model is evaluated across various, yet related, classification tasks (episodes). Unlike prior episodic EEG studies that rely on fixed or randomly sampled classes of equal cardinality, we adopt hierarchy-aware episode sampling using WordNet to generate episodes with variable classes of diverse hierarchy. We also present the largest episodic framework in the EEG domain for detecting observed text from EEG signals in the PEERS dataset, comprising $931538$ EEG samples under $1610$ object labels, acquired from $264$ human participants (subjects) performing controlled cognitive tasks, enabling the study of neural dynamics underlying perception, decision-making, and performance monitoring. We examine how the semantic abstraction level affects classification performance across multiple learning techniques and architectures, providing a comprehensive analysis. The models tend to improve performance when the classification categories are drawn from higher levels of the hierarchy, suggesting sensitivity to abstraction. Our work highlights abstraction depth as an underexplored dimension of EEG decoding and motivates future research in this direction.

翻译：脑电图（EEG）记录的是从人体头皮测量到的神经元空间平均电活动。先前的研究已探索基于EEG对物体或概念进行分类，通常用于被动观看短暂呈现的图像或视频刺激，且类别有限。由于EEG具有较低的信噪比，识别大量类别中的细粒度表征仍然具有挑战性；然而，抽象级别的物体表征可能存在。在本工作中，我们研究EEG是否捕获跨多个层次级别的物体表征，并提出情景分析，其中机器学习（ML）模型在多个相关但不同的分类任务（情景）中进行评估。与先前依赖固定或随机采样等基数类别的情景式EEG研究不同，我们采用基于WordNet的层次感知情景采样，以生成具有不同层次结构的可变类别情景。我们还提出了EEG领域中最大的情景式框架，用于从PEERS数据集的EEG信号中检测观察到的文本，该数据集包含$931538$个EEG样本，涵盖$1610$个物体标签，采集自$264$名执行受控认知任务的人类参与者（被试），从而支持研究感知、决策和绩效监控背后的神经动力学。我们考察了语义抽象级别如何影响多种学习技术和架构的分类性能，提供了全面的分析。当分类类别来自层次结构的较高层级时，模型性能倾向于提升，这表明其对抽象性具有敏感性。我们的工作强调了抽象深度作为EEG解码中一个尚未充分探索的维度，并激励未来在此方向的研究。