Abstract
In disease detection, generative models for data augmentation offer a potential solution to the challenges posed by limited high-quality electroencephalogram (EEG) data. The study proposes a temporal-spatial feature-aware denoising diffusion probabilistic model (DDPM), termed TF-DDPM, as an EEG time-series augmentation framework for autism research. The module for predicting noise is CCA-UNet based on the channel correlation-based attention (CCA) mechanism, which considers the spatial and temporal correlation between channels, and uses depthwise separable convolution instead of traditional convolution, thereby suppressing the interference from irrelevant channels. Visualization and binary classification results on synthetic signals indicate that proposed method generates higher quality synthetic data compared to Generative Adversarial Networks (GAN) and DDPM.
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Acknowledgements
This work was supported by the National Natural Science Foundations of China under Grant 62172403, the Distinguished Young Scholars Fund of Guangdong under Grant 2021B1515020019, the Excellent Young Scholars of Shenzhen under Grant RCYX20200714114641211.
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Li, Y., Liao, I.Y., Zhong, N., Toshihiro, F., Wang, Y., Wang, S. (2023). Generative AI Enables the Detection of Autism Using EEG Signals. In: Jia, W., et al. Biometric Recognition. CCBR 2023. Lecture Notes in Computer Science, vol 14463. Springer, Singapore. https://doi.org/10.1007/978-981-99-8565-4_35
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