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 token-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）的一种特殊范式，反映了大脑对外部刺激或事件的神经响应，通常与特定认知任务的处理过程相关。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性脑电及其他非锁时任务相关脑电信号处理方法取得了显著进展。然而，这些方法在ERP数据上的有效性尚未得到充分探索，且现有诸多ERP研究仍严重依赖手工特征提取。本文开展了一项全面的基准研究，系统比较了传统手工特征（后续接线性分类器）、深度学习模型以及预训练脑电基础模型在ERP分析中的性能。我们构建了统一的数据预处理与训练流程，并在两项代表性任务（ERP刺激分类与基于ERP的脑疾病检测）中，利用12个公开数据集对上述方法进行了评估。此外，我们进一步研究了先进Transformer架构中的各类令牌嵌入策略，以识别更适配ERP数据的嵌入设计。本研究为未来ERP分析中的方法选择与定制化模型设计提供了标杆性框架。代码开源地址：https://github.com/DL4mHealth/ERP-Benchmark