Composing Graphical Models with Generative Adversarial Networks for EEG Signal Modeling
Composing Graphical Models with Generative Adversarial Networks for EEG Signal Modeling
复制标题
使用生成对抗网络构建图形模型进行脑电图信号建模
DOI：
10.1109/icassp43922.2022.9747783
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发表时间：
2022
期刊：
影响因子：
--
通讯作者：
Cao, Hung
中科院分区：
文献类型：
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作者：
Vo, Khuong;Vishwanath, Manoj;Srinivasan, Ramesh;Dutt, Nikil;Cao, Hung
脑电图（EEG）形式的神经振荡可以揭示潜在的大脑功能，如认知，记忆，感知和意识。一个全面的EEG计算模型不仅提供了一个直接生成数据的随机过程，而且还提供了进一步理解神经机制的见解。在这里，我们提出了一种生成和推理方法，该方法结合了概率图形模型和生成对抗网络（GAN）的互补优势，用于EEG信号建模。我们调查的方法的能力，共同学习相干生成和逆推理模型的CHI-MIT癫痫多通道EEG数据集。我们进一步研究了在癫痫发作检测制定为一个无监督学习问题的学习表示的功效。定量和定性的实验结果证明了我们的方法的有效性和效率。
Neural oscillations in the form of electroencephalogram (EEG) can reveal underlying brain functions, such as cognition, memory, perception, and consciousness. A comprehensive EEG computational model provides not only a stochastic procedure that directly generates data but also insights to further understand the neurological mechanisms. Here, we propose a generative and inference approach that combines the complementary benefits of probabilistic graphical models and generative adversarial networks (GANs) for EEG signal modeling. We investigate the method’s ability to jointly learn coherent generation and inverse inference models on the CHI-MIT epilepsy multi-channel EEG dataset. We further study the efficacy of the learned representations in epilepsy seizure detection formulated as an unsupervised learning problem. Quantitative and qualitative experimental results demonstrate the effectiveness and efficiency of our approach.