You Only Acquire Sparse-channel (YOAS): A Unified Framework for Dense-channel EEG Generation
<|reference_start|>You Only Acquire Sparse-channel (YOAS): A Unified Framework for Dense-channel EEG Generation: High-precision acquisition of dense-channel electroencephalogram (EEG) signals is often impeded by the costliness and lack of portability of equipment. In contrast, generating dense-channel EEG signals effectively from sparse channels shows promise and economic viability. However, sparse-channel EEG poses challenges such as reduced spatial resolution, information loss, signal mixing, and heightened susceptibility to noise and interference. To address these challenges, we first theoretically formulate the dense-channel EEG generation problem as by optimizing a set of cross-channel EEG signal generation problems. Then, we propose the YOAS framework for generating dense-channel data from sparse-channel EEG signals. The YOAS totally consists of four sequential stages: Data Preparation, Data Preprocessing, Biased-EEG Generation, and Synthetic EEG Generation. Data Preparation and Preprocessing carefully consider the distribution of EEG electrodes and low signal-to-noise ratio problem of EEG signals. Biased-EEG Generation includes sub-modules of BiasEEGGanFormer and BiasEEGDiffFormer, which facilitate long-term feature extraction with attention and generate signals by combining electrode position alignment with diffusion model, respectively. Synthetic EEG Generation synthesizes the final signals, employing a deduction paradigm for multi-channel EEG generation. Extensive experiments confirmed YOAS's feasibility, efficiency, and theoretical validity, even remarkably enhancing data discernibility. This breakthrough in dense-channel EEG signal generation from sparse-channel data opens new avenues for exploration in EEG signal processing and application.<|reference_end|>
@article{chen2024you,
    title={You Only Acquire Sparse-channel (YOAS): A Unified Framework for
  Dense-channel EEG Generation},
    author={Hongyu Chen, Weiming Zeng, Luhui Cai, Lei Wang, Jia Lu, Yueyang Li,
  Hongjie Yan, Wai Ting Siok, Nizhuan Wang},
    journal={arXiv preprint arXiv:2406.15269},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.15269},
    primaryClass={cs.CV}
}