通过模拟生理噪声提升脑电语音解码准确率
Physiological Noise Augmentation Improves Non-Invasive Brain-to-Speech
- 用独立成分分析分离脑信号中的生理噪声并重构训练数据
- 在12000次想象数字任务中，准确率提升4.7个百分点
- 适合研究非侵入式脑机接口与神经信号降噪的学者
非侵入式脑-语音解码旨在为神经退行性疾病患者恢复沟通能力，避免开颅手术风险。现有基于脑磁图（MEG）和脑电图（EEG）的方法虽具可扩展性，但因信噪比远低于侵入式记录，仍存在较高词错误率。本文提出生理噪声增强（PNA），一种显式训练解码器对无关生理伪迹（如眼动、心搏活动）保持不变的数据增强方法。受自动语音识别中加入环境噪声提升鲁棒性的启发，PNA利用独立成分分析（ICA）将脑记录分解为干净信号与噪声伪迹，再进行缩放与混合，生成符合生物物理规律且标签保持一致的训练样本。实验表明，PNA近似于各向异性正则化，抑制解码器在伪迹主导方向上的敏感性。在包含12000次试验的MegNIST想象数字MEG数据集上，结合10次试验平均后，使用EEGNet的解码准确率相比仅使用真实数据训练提升了4.7个百分点（绝对值）。结果表明，伪迹感知增强与试验平均是提升非侵入式语音脑机接口鲁棒性的互补工具。
原文摘要 · Abstract (English)
Non-invasive brain-to-speech decoding aims to restore communication to patients suffering from neurodegenerative disease, without the risks of neurosurgery. Existing MEG- and EEG-based methods, while scalable, continue to suffer from high word error rates driven by relatively low signal-to-noise ratios compared to invasive recordings. We propose physiological noise augmentation (PNA), a data augmentation method that explicitly trains decoders to become invariant to task-agnostic artifacts (e.g. ocular and cardiac activity). PNA draws inspiration from automatic speech recognition systems, where environmental noise (e.g. dogs barking, city traffic) is added to clean speech to improve robustness. Analogously, we decompose brain recordings into clean data and noise artifacts using independent component analysis (ICA), before scaling and remixing to generate biophysically realistic, label-preserving training examples. We show that PNA approximates anisotropic regularization, penalizing decoder sensitivity along artifact-dominated directions. On MegNIST, a 12k-trial imagined-digit MEG dataset, PNA with 10-trial averaging improves EEGNet decoding accuracy by 4.7 percentage points (absolute) over training on real data alone. Our results suggest that artifact-aware augmentation and trial averaging are complementary tools for improving robustness in non-invasive speech BCIs.
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