通过身份条件网络提升脑机接口跨被试泛化能力
Explicit modelling of subject dependency in BCI decoding
- 用轻量卷积网络显式建模被试差异，以身份信息作为条件输入
- 在少量校准数据下实现更优分类性能，改善跨被试泛化性
- 适合需要快速个性化校准的临床脑机接口应用
脑机接口面临高个体差异和标注数据有限的问题，常需长时间校准。本文提出一种端到端方法，通过轻量卷积神经网络（CNN）结合被试身份信息，显式建模个体差异。该方法融合超参数优化策略以应对类别不平衡，并评估两种条件机制，使预训练模型能以最少校准数据适配未见被试。我们在时间调制事件相关电位（ERP）分类任务上对比三种轻量架构，提供可解释的评估指标与学习表征的可视化。结果表明，该方法提升了泛化能力并实现了数据高效的校准，凸显了主体自适应脑机接口的可扩展性与实用性。
原文摘要 · Abstract (English)
Brain-Computer Interfaces (BCIs) suffer from high inter-subject variability and limited labeled data, often requiring lengthy calibration phases. In this work, we present an end-to-end approach that explicitly models the subject dependency using lightweight convolutional neural networks (CNNs) conditioned on the subject's identity. Our method integrates hyperparameter optimization strategies that prioritize class imbalance and evaluates two conditioning mechanisms to adapt pre-trained models to unseen subjects with minimal calibration data. We benchmark three lightweight architectures on a time-modulated Event-Related Potentials (ERP) classification task, providing interpretable evaluation metrics and explainable visualizations of the learned representations. Results demonstrate improved generalization and data-efficient calibration, highlighting the scalability and practicality of subject-adaptive BCIs.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。