提出无需调参的脑电跨数据集解码框架，提升实际应用可行性。
AFPM: Alignment-based Frame Patch Modeling for Cross-Dataset EEG Decoding
- 基于脑区先验筛选关键电极，统一布局并对齐不同数据分布。
- 将多数据集信号建模为统一时空块，实现跨域解码性能提升4.40%。
- 首个免校准框架，适合真实场景下快速部署的脑机接口应用。
脑机接口中的脑电（EEG）解码模型在跨数据集学习与泛化方面面临挑战，主要源于电极布局不一致、信号分布非平稳以及神经生理先验整合不足。为此，我们提出一种即插即用的对齐式帧块建模（AFPM）框架，包含两个核心组件：1）空间对齐，依据脑区先验选择任务相关电极，对齐跨域脑电分布，并将选中电极重映射至统一布局；2）帧块编码，将多数据集信号建模为统一时空块以支持解码。相比需针对各数据集调参的17种先进方法，所提无校准的AFPM在运动想象任务上最高提升4.40%，在事件相关电位任务上提升3.58%。据我们所知，这是首个免校准的跨数据集脑电解码框架，显著提升了脑机接口在真实场景中的实用性。
原文摘要 · Abstract (English)
Electroencephalogram (EEG) decoding models for brain-computer interfaces (BCIs) struggle with cross-dataset learning and generalization due to channel layout inconsistencies, non-stationary signal distributions, and limited neurophysiological prior integration. To address these issues, we propose a plug-and-play Alignment-Based Frame-Patch Modeling (AFPM) framework, which has two main components: 1) Spatial Alignment, which selects task-relevant channels based on brain-region priors, aligns EEG distributions across domains, and remaps the selected channels to a unified layout; and, 2) Frame-Patch Encoding, which models multi-dataset signals into unified spatiotemporal patches for EEG decoding. Compared to 17 state-of-the-art approaches that need dataset-specific tuning, the proposed calibration-free AFPM achieves performance gains of up to 4.40% on motor imagery and 3.58% on event-related potential tasks. To our knowledge, this is the first calibration-free cross-dataset EEG decoding framework, substantially enhancing the practicalness of BCIs in real-world applications.
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。