You Only Acquire Sparse-channel (YOAS): A Unified Framework for Dense-channel EEG Generation

• URL: http://arxiv.org/abs/2406.15269v2
• Date: Mon, 5 Aug 2024 13:23:17 GMT
• Title: You Only Acquire Sparse-channel (YOAS): A Unified Framework for Dense-channel EEG Generation
• Authors: Hongyu Chen, Weiming Zeng, Luhui Cai, Lei Wang, Jia Lu, Yueyang Li, Hongjie Yan, Wai Ting Siok, Nizhuan Wang,
• Abstract summary: We develop a framework for generating dense-channel data from sparse-channel EEG signals. YOAS consists of four sequential stages: Data Preparation, Data Preprocessing, Biased-EEG Generation, and Synthetic EEG Generation. This breakthrough in dense-channel EEG signal generation from sparse-channel data opens new avenues for exploration in EEG signal processing and application.
• Score: 7.507775056200206
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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