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This research applies diffusion probabilistic models to generate synthetic EEG data, effectively solving the scarcity of real-world datasets for auditory attention decoding in hearing aids. By augmenting training sets with these realistic signals, the study demonstrates a measurable improvement in short-window classification performance without requiring new hardware or biological subjects. The work highlights how generative AI techniques can directly enhance the robustness and practicality of deep learning models in constrained biomedical contexts.
Addressing Limited Data in Auditory Attention Decoding with Diffusion Generative Models
arXiv:2607.18345v1 Announce Type: cross Abstract: 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.
Abstract: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.
Sound (cs.SD); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
arXiv:2607.18345 [cs.SD]
(or arXiv:2607.18345v1 [cs.SD] for this version)
https://doi.org/10.48550/arXiv.2607.18345 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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