用特征融合生成少量脑电数据，提升模型泛化能力。
FusionGen: Feature Fusion-Based Few-Shot EEG Data Generation
- 通过解耦表征与特征匹配融合，生成多样化脑电信号。
- 在多个公开数据集上分类准确率显著优于现有方法。
- 适合数据稀缺场景下的脑机接口模型训练。
脑机接口（BCIs）通过脑电图（EEG）建立大脑与外部设备的直接通信路径，在医疗康复和认知状态评估等领域具有潜力。然而，基于EEG的BCIs受限于数据稀缺性和显著的个体间差异，严重制约了脑电解码模型在实际应用中的泛化能力。为应对这些挑战，我们提出FusionGen，一种基于解耦表征学习与特征融合的新型脑电数据生成框架。通过特征匹配融合模块整合不同试验的特征，并结合轻量级特征提取与重建流程，FusionGen在数据有限条件下同时保障了数据多样性与模型可训练性。在多个公开脑电数据集上的大量实验表明，FusionGen显著优于现有数据增强技术，显著提升了分类准确率。
原文摘要 · Abstract (English)
Brain-computer interfaces (BCIs) provide potential for applications ranging from medical rehabilitation to cognitive state assessment by establishing direct communication pathways between the brain and external devices via electroencephalography (EEG). However, EEG-based BCIs are severely constrained by data scarcity and significant inter-subject variability, which hinder the generalization and applicability of EEG decoding models in practical settings. To address these challenges, we propose FusionGen, a novel EEG data generation framework based on disentangled representation learning and feature fusion. By integrating features across trials through a feature matching fusion module and combining them with a lightweight feature extraction and reconstruction pipeline, FusionGen ensures both data diversity and trainability under limited data constraints. Extensive experiments on multiple publicly available EEG datasets demonstrate that FusionGen significantly outperforms existing augmentation techniques, yielding notable improvements in classification accuracy.
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