EEG-PRIME：面向脑电信号解码的多层级条件原型对齐表示学习

中文摘要 AI 辅助

脑电（EEG）解码模型常因采集协议和个体神经生理学的域偏移，在不同数据集与被试间泛化能力较差。本文提出EEG-PRIME，一种用于跨数据集多任务解码的两阶段脑电基础模型，结合掩码预训练与原型对齐指令调优，以实现针对不同BCI范式的指令感知与被试不变解码。预训练阶段，脑电编码器通过带频率截止谱增强的掩码重构学习可迁移表示；指令调优阶段，EEG-PRIME整合任务语义、数据集特定及被试不变的条件信号，该信号通过分层查询调制（Layer-wise Query Modulation）调控Q-Former，同时冻结的类别标签文本嵌入作为原型，用于跨异构标签空间的余弦相似度预测。在覆盖运动想象、情绪识别、ADHD检测、内隐言语及心理负荷的16个数据集上的实验显示，EEG-PRIME在跨被试设置下，较现有最优基线模型及此前的脑电基础模型均取得一致提升；在两个额外保留数据集上，无需目标域优化、校准或线性探测，EEG-PRIME即可达到与会话内校准模型相当的平衡准确率，展现出良好的零样本迁移能力。

英文摘要

Electroencephalography (EEG) decoding models often generalize poorly across datasets and subjects due to domain shifts in acquisition protocols and individual neurophysiology. We propose EEG-PRIME, a two-stage EEG foundation model for cross-dataset multi-task decoding. EEG-PRIME combines masked pretraining with prototype-aligned instruction tuning to enable instruction-aware and subject-invariant decoding across diverse BCI paradigms. During pretraining, an EEG encoder learns transferable representations through masked reconstruction with frequency-cutoff spectral augmentation. During instruction tuning, EEG-PRIME incorporates task-semantic, dataset-specific, and subject-invariant conditioning. The resulting conditioning signal modulates the Q-Former through Layer-wise Query Modulation, while frozen text embeddings of class labels serve as prototypes for cosine-similarity-based prediction across heterogeneous label spaces. Experiments on sixteen datasets covering motor imagery, emotion recognition, ADHD detection, covert speech, and mental workload show consistent improvements over state-of-the-art baselines and prior EEG foundation models under cross-subject settings. On two additional held-out datasets, EEG-PRIME achieves balanced accuracy comparable to within-session calibration models without target-domain optimization, calibration, or linear probing, demonstrating promising zero-shot transfer capability.

发表机构

• College of Computing and Data Science, Nanyang Technological University（南洋理工大学计算与数据科学学院）
• Centre for AI in Medicine, Nanyang Technological University（南洋理工大学医学人工智能中心）
• Southeast University（东南大学）

机构由 AI 辅助整理，请以论文原文为准。