Event-related potential (ERP), a specialized paradigm of electroencephalographic (EEG), reflects neurological responses to external stimuli or events, generally associated with the brain's processing of specific cognitive tasks. ERP plays a critical role in cognitive analysis, the detection of neurological diseases, and the assessment of psychological states. Recent years have seen substantial advances in deep learning-based methods for spontaneous EEG and other non-time-locked task-related EEG signals. However, their effectiveness on ERP data remains underexplored, and many existing ERP studies still rely heavily on manually extracted features. In this paper, we conduct a comprehensive benchmark study that systematically compares traditional manual features (followed by a linear classifier), deep learning models, and pre-trained EEG foundation models for ERP analysis. We establish a unified data preprocessing and training pipeline and evaluate these approaches on two representative tasks, ERP stimulus classification and ERP-based brain disease detection, across 12 publicly available datasets. Furthermore, we investigate various patch-embedding strategies within advanced Transformer architectures to identify embedding designs that better suit ERP data. Our study provides a landmark framework to guide method selection and tailored model design for future ERP analysis. The code is available at https://github.com/DL4mHealth/ERP-Benchmark.

翻译：事件相关电位（ERP）作为脑电图（EEG）的一种专门范式，反映了神经系统对外部刺激或事件的响应，通常与大脑对特定认知任务的处理相关联。ERP在认知分析、神经系统疾病检测及心理状态评估中发挥着关键作用。近年来，基于深度学习的自发EEG及其他非锁时任务相关EEG信号分析方法取得了显著进展。然而，这些方法在ERP数据上的有效性仍未得到充分探索，现有许多ERP研究仍严重依赖手工提取的特征。本文开展了一项综合性基准研究，系统比较了传统手工特征（结合线性分类器）、深度学习模型以及预训练EEG基础模型在ERP分析中的性能。我们建立了统一的数据预处理与训练流程，并在12个公开数据集上针对两项代表性任务——ERP刺激分类与基于ERP的脑疾病检测——评估了这些方法。此外，我们在先进的Transformer架构中探究了多种片段嵌入策略，以识别更适配ERP数据的嵌入设计方案。本研究为未来ERP分析的方法选择与定制化模型设计提供了里程碑式的指导框架。代码发布于 https://github.com/DL4mHealth/ERP-Benchmark。