跨cVAE-GAN:基于变压器的cVAE-GAN用于高保真性EEG信号生成
Yiduo Yao1, Xiao Wang1, Xudong Hao1
1School of Information and Control Engineering, Qingdao University of Technology, Qingdao 266520, China.
Bioengineering (Basel, Switzerland)
|October 29, 2025
概括
这项研究介绍了一种基于变压器的新型生成模型,用于现实的脑电图 (EEG) 信号合成,提高情绪识别精度,并为大脑与计算机接口提供可扩展的解决方案.
科学领域:
- 神经科学是一个神经科学.
- 人工智能的人工智能
- 信号处理 信号处理
背景情况:
- 电脑电图 (EEG) 信号生成是复杂的,因为非静止性和时空合.
- 像VAE和GAN这样的现有生成模型在EEG忠实性,光谱稳定性和语义控制方面扎.
研究的目的:
- 开发一种先进的生成模型,用于高保真度EEG合成.
- 增强用于情感计算的生成EEG信号的可控性和稳定性.
主要方法:
- 提出了一个基于变压器的有条件变化的自编码器生成对抗网络 (Trans-cVAE-GAN).
- 该模型结合了变压器驱动的时间建模,标签条件潜在推理和对抗性学习.
- 采用多维结构损失来保持时间,光谱和统计属性.
主要成果:
- 跨cVAE-GAN在多个数据集中实现了与真实EEG信号的高度相似性 (皮尔森相关性≈0.84).
- 生成的信号表现出较低的光谱分歧 (KL分歧≈0.39) 和比基线GAN更好的稳定性.
- 通过生成EEG来增强情绪识别任务,精度从86.9%提高到91.8%.
结论:
- 拟议的Trans-cVAE-GAN有效地产生高保真度,稳定和可控制的EEG信号.
- 这种方法为情感计算和脑机接口应用提供了可扩展的解决方案.
- 该模型显示了对EEG的传统生成方法的显著改进.
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