DenoFlow：真实生理伪影下SSVEP去噪的流匹配方法
DenoFlow: Flow Matching for SSVEP Denoising under Real Physiological Artifacts
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中文总结 AI 辅助
提出DenoFlow，利用整流流匹配将SSVEP去噪视为传输问题，通过场网络回归路径速度并积分，在真实生理伪影下优于七种基线模型，提升信号保真度和解码精度。
中文摘要 AI 辅助
基于脑电图（EEG）的脑机接口（BCI），特别是稳态视觉诱发电位（SSVEP）系统，极易受到噪声和伪影的影响，这严重降低了解码精度。尽管最近的去噪方法显示出潜力，但它们在没有配对真值的情况下进行拟合，可能会倾向于复现其输入，并且仅基于波形距离进行优化，这无法说明输出是否保持可解码性。为解决这些问题，我们提出了DenoFlow，将SSVEP去噪视为传输问题：不是学习从受污染试次到干净试次的直接映射，而是通过一个场网络回归它们之间直线路径的速度，遵循整流流（rectified-flow）公式，去噪过程从观测点向前积分该场。该场网络是一个编码器-解码器，在每一层都看到受污染的试次，并在瓶颈处看到路径位置，同时训练一个分类器对积分输出进行监督。由于观测本身既是条件输入又是积分的起点，模型永远不会从噪声中生成试次，训练简化为回归，从而消除了对抗性最小-最大博弈。为了在无真值的数据集上获得配对数据，我们在受控信噪比目标下注入了记录到的肌电（EMG）和眼电（EOG）信号的生理伪影。在两个公开SSVEP数据集上的实验，使用五种流行的SSVEP解码器，表明DenoFlow在信号保真度和下游解码精度方面均优于七种基线去噪模型。代码可在该HTTPS URL获取。
英文摘要
Electroencephalography (EEG)-based brain-computer interfaces (BCIs), particularly steady-state visual evoked potential (SSVEP) systems, are highly vulnerable to noise and artifacts, which severely degrade decoding accuracy. Although recent denoising approaches have shown promise, they are fitted without paired ground truth, can settle on reproducing their input, and are optimized on waveform distance alone, which says nothing about whether the output stays decodable. To address these issues, we propose DenoFlow, which casts SSVEP denoising as transport: instead of learning a direct map from a contaminated trial to a clean one, a field network regresses the velocity of the straight path between them, following the rectified-flow formulation, and denoising integrates that field forward from the observation. The field network is an encoder-decoder that sees the contaminated trial at every layer and the path position at its bottleneck, and a classifier trained alongside it supervises the integrated output. Because the observation itself is both the conditioning input and the starting point of the integration, the model never generates a trial from noise, and training reduces to regression, removing the adversarial min-max game. To obtain paired data on datasets with no ground truth, we injected physiological artifacts of the recorded electromyography (EMG) and electrooculography (EOG) signals under a controlled signal-to-noise target. Experiments on two public SSVEP datasets with five popular SSVEP decoders showed that DenoFlow outperformed seven baseline denoising models on both signal fidelity and downstream decoding accuracy. Code is available at https://github.com/wzwvv/DenoFlow.
发表机构
- Huazhong University of Science and Technology（华中科技大学）
机构由 AI 辅助整理，请以论文原文为准。
