发表机构
Hanyang University（汉阳大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出SPERA，一种采用JEPA的EEG基础模型，融入勒让德多项式空间先验等组件，经8万余小时EEG预训练后，在9项下游任务中获最高平均平衡准确率，兼具参数效率与鲁棒性，可作为EEG分析通用骨干。
AI 中文摘要
脑电图（EEG）提供了对持续神经活动的非侵入式测量，但由于受试者、设备和电极导联的异质性，构建通用EEG模型仍具挑战性。现有EEG基础模型主要依赖于对观测信号定义的重建目标，而观测信号包含神经和非神经成分。我们提出SPERA（Spherical Prior EEG Representation Architecture，球形先验脑电图表示架构），这是一种采用联合嵌入预测架构（JEPA）在隐空间进行预测的EEG基础模型。SPERA引入勒让德多项式空间先验，并将其融入注意力机制以编码不同的头皮电极几何结构；另外两个组件使模型适配EEG：分解式时间与空间注意力，与周期性全注意力块交替；以及关系谱正则化器，将隐空间相似性结构与频谱视图对齐。SPERA在来自106个数据集、29048名受试者的约80000小时EEG上进行预训练，在涵盖临床、认知和BCI应用的9项下游任务中，实现了最高的平均平衡准确率。SPERA在线性探测下展现出强大的参数效率，且在不同记录条件下具有鲁棒性，表明其作为通用骨干模型用于各类EEG分析的潜力。
英文摘要
Electroencephalography (EEG) provides a non-invasive measure of ongoing neural activity, but building general-purpose EEG models remains challenging due to the heterogeneity of subjects, devices, and electrode montages. Existing EEG foundation models predominantly rely on reconstruction-based objectives defined on the observed signal, which contains both neural and non-neural components. We introduce SPERA (Spherical Prior EEG Representation Architecture), an EEG foundation model that adopts the joint-embedding predictive architecture (JEPA) to predict in latent space. SPERA introduces a Legendre-polynomial spatial prior, incorporated into attention to encode varying scalp electrode geometries. Two further components adapt the model to EEG: factorized temporal and spatial attention interleaved with periodic full-attention blocks, and a relational spectral regularizer aligning latent similarity structure with spectral views. Pretrained on approximately 80,000 hours of EEG from 29,048 subjects across 106 datasets, SPERA achieves the highest average balanced accuracy across nine downstream tasks spanning clinical, cognitive, and BCI applications. SPERA further exhibits strong parameter efficiency under linear probing and robustness across varying recording conditions, suggesting its potential as a general-purpose backbone for diverse EEG analyses.
CommentsAccepted at NeurIPS 2026
