KDHNet：面向少校准数据 SSVEP 解码的知识驱动混合网络
KDHNet: A Knowledge-Driven Hybrid Network for Limited-Calibration SSVEP Decoding
AI 导读
KDHNet 将知识驱动的解析处理与数据驱动的表示学习结合，用于少校准数据下的 SSVEP 脑机接口解码。它级联 TDCA 与 TRCA 提取判别性成分，经可学习子频带加权融合后由 SSVEPformer 解码器分类。在三个公开数据集上，受试者内评测的 15 个窗口设置中 14 个及全部 10 个校准比例设置下取得最高平均准确率与信息传输率；跨受试者评测获最佳或次佳，校准数据越少优势越大。
正文 · 原文
Abstract:Steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs) achieve high information transfer rates but often require sufficient subject-specific calibration data for reliable decoding. Analytical spatial filters are effective with limited data, but their linear formulations constrain representation capacity. Deep models learn more flexible representations but are prone to overfitting when calibration data are scarce. We propose KDHNet, which combines knowledge-driven analytical processing with data-driven representation learning. After filter-bank preprocessing, cascaded task-discriminant component analysis (TDCA) and task-related component analysis (TRCA) extract discriminative and trial-reproducible components. Learnable sub-band weighting then fuses spectral information, and an SSVEPformer decoder learns temporal and spectral representations for classification. For inter-subject decoding, a target-guided least-squares transformation aligns source trials using one labeled target trial per class. Extensive experiments were performed on three public datasets, covering intra- and inter-subject protocols, five analysis windows, and multiple calibration ratios. In intra-subject evaluation, KDHNet achieved the highest mean accuracy and information transfer rate in 14 of 15 window settings and all ten calibration-ratio settings. In inter-subject evaluation, KDHNet obtained the best or second-best performance, and the gains were generally larger with less calibration data. Ablation studies indicated that the estimated TDCA projection performed better when fixed than when optimized from analytical or random initialization. Combining fixed knowledge priors with trainable neural modules is effective for low-calibration SSVEP decoding. This framework supports more practical, robust, and plug-and-play BCIs. Code is available at this https URL.
| Comments: | 12 pages, 7 figures |
| Subjects: | Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2610.33058 [cs.CV] |
| (or arXiv:2610.33058v1 [cs.CV] for this version) | |
| https://doi.org/10.48550/arXiv.2610.33058 arXiv-issued DOI via DataCite |
Submission history
From: Ziwei Wang [view email]
[v1]
Mon, 19 Oct 2026 09:53:55 UTC (789 KB)
来源：arXiv cs.AI · arxiv.org