用轻量低秩适配器解决脑电解码中个体差异问题
Mitigating Subject Dependency in EEG Decoding with Subject-Specific Low-Rank Adapters
- 将模型权重分解为通用部分和个体特异修正项
- 参数减半仍优于基线，跨被试任务表现更优
- 适合构建通用脑信号解码模型的科研与工程人员
个体间分布差异是脑信号解码领域构建基础模型的核心障碍。本文提出主体特异性低秩适配器（SuLoRA），作为标准线性或卷积层的即插即用替代方案，通过将权重分解为共享的、主体无关成分与每个主体独有的轻量低秩修正项，显式建模个体差异。该方法使现有架构在无需重构的前提下具备抗主体漂移能力。我们在MEG语音感知和EEG运动想象任务上评估了SuLoRA，涵盖CNN与Transformer架构。在语音解码任务中，参数量减半仍优于基线；在运动想象数据集上，性能超越无主体特异性模型及独立训练的主体专属模型。SuLoRA为脑信号应用中的有效跨主体基础模型提供了可行路径。
原文摘要 · Abstract (English)
Subject-specific distribution shifts represent a fundamental obstacle to developing foundation models for brain decoding. We propose the Subject-Specific Low-Rank Adapter (SuLoRA), a drop-in replacement for standard linear or convolutional layers that captures inter-subject variability by decomposing weights into a shared, subject-invariant component and a lightweight, low-rank correction unique to each subject. This explicit separation enables existing architectures to become robust to subject shifts without architectural redesign. We evaluate SuLoRA on MEG speech perception and EEG motor imagery tasks across CNN and transformer architectures. In the speech decoding task, SuLoRA exceeds the baseline performance with half of the parameters. On motor imagery dataset, SuLoRA outperforms both subject-agnostic models and independently trained subject-specific models. SuLoRA offers a practical path towards effective cross-subject foundation models for brain signal applications.
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