Ghosh Sayan, Biswas Dipayan, Rohan N R, Vijayan Sujith, Chakravarthy V Srinivasa
Indian Institute of Technology Madras, Chennai, India.
Virginia Tech, Blacksburg, VA, United States.
Front Neuroinform. 2025 May 14;19:1513374. doi: 10.3389/fninf.2025.1513374. eCollection 2025.
This study presents a general trainable network of Hopf oscillators to model high-dimensional electroencephalogram (EEG) signals across different sleep stages. The proposed architecture consists of two main components: a layer of interconnected oscillators and a complex-valued feed-forward network designed with and without a hidden layer. Incorporating a hidden layer in the feed-forward network leads to lower reconstruction errors than the simpler version without it. Our model reconstructs EEG signals across all five sleep stages and predicts the subsequent 5 s of EEG activity. The predicted data closely aligns with the empirical EEG regarding mean absolute error, power spectral similarity, and complexity measures. We propose three models, each representing a stage of increasing complexity from initial training to architectures with and without hidden layers. In these models, the oscillators initially lack spatial localization. However, we introduce spatial constraints in the final two models by superimposing spherical shells and rectangular geometries onto the oscillator network. Overall, the proposed model represents a step toward constructing a large-scale, biologically inspired model of brain dynamics.
本研究提出了一种通用的可训练霍普夫振荡器网络，用于对不同睡眠阶段的高维脑电图（EEG）信号进行建模。所提出的架构由两个主要部分组成：一层相互连接的振荡器和一个设计有或没有隐藏层的复值前馈网络。在前馈网络中加入隐藏层会比没有隐藏层的简单版本产生更低的重建误差。我们的模型能够重建所有五个睡眠阶段的EEG信号，并预测随后5秒的EEG活动。预测数据在平均绝对误差、功率谱相似性和复杂度测量方面与经验EEG数据紧密吻合。我们提出了三个模型，每个模型代表了从初始训练到有或没有隐藏层的架构，复杂度逐渐增加的一个阶段。在这些模型中，振荡器最初缺乏空间定位。然而，我们通过在振荡器网络上叠加球壳和矩形几何形状，在最后两个模型中引入了空间约束。总体而言，所提出的模型朝着构建一个大规模、受生物启发的脑动力学模型迈出了一步。
