用扩散模型生成脑电数据，提升助听器中注意力解码性能
Addressing Limited Data in Auditory Attention Decoding with Diffusion Generative Models
- 用扩散模型生成逼真的语音诱发脑电信号
- 合成数据使注意力分类准确率显著提高（p<0.05）
- 适合需要小样本训练的助听器实时注意力追踪场景
听力辅助设备中的听觉注意力解码（AAD）受限于训练数据不足。AAD利用脑电图（EEG）数据解码听众注意力，实现对特定声源的实时跟踪。然而，在助听器典型短时窗（≤1秒）下实现高精度仍具挑战，因真实语音诱发的EEG数据稀缺。为此，本文研究扩散概率模型（DPMs）生成合成语音诱发EEG数据的可行性。DPM通过去噪过程学习复杂数据结构，可生成适用于数据增强的逼真样本。实验评估了合成数据在注意力位置（LoA）分类任务中的效果。结果表明，DPM能生成真实感强的EEG信号，且加入合成数据后，模型性能显著优于仅使用实测数据训练的模型（p<0.05）。这证明基于扩散的数据增强可缓解训练数据不足问题，提升助听器应用中短时窗AAD模型的鲁棒性。
原文摘要 · Abstract (English)
Limited training data constrains deep learning models for Auditory Attention Decoding (AAD) in hearing aids (HAs). AAD uses electroencephalogram (EEG) data to decode listener's attention, enabling real-time tracking of specific sound sources. However, achieving high AAD performance with short time windows typical in HAs (<=1s) is challenging due to the scarcity of real-world speech-evoked EEG data. To address this issue, we investigate diffusion probabilistic models (DPMs) for generating synthetic speech-evoked EEG data. DPMs learn the underlying complex data structure through a denoising process and can generate realistic samples suitable for data augmentation. We evaluate the use of synthetic EEG data for augmenting datasets in locus-of-attention (LoA) classification tasks. Our experiments demonstrate that DPMs can generate realistic EEG signals and that incorporating synthetic data significantly improves AAD performance compared to models trained solely on measured EEG data (p<0.05). These results highlight the potential of diffusion-based data augmentation to mitigate training data limitations and improve the robustness of short-window AAD models in HA applications.
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