Epileptic seizures arise from synchronous firing of multiple spatially separated neural masses; therefore, many synchrony measures are used for seizure detection and characterization. However, synchrony measures reflect only the overall interaction strength among populations of neurons but cannot reveal the coupling strengths among individual populations, which is more important for seizure control. The concepts of reachability and reachable cluster were proposed to denote the coupling strengths of a set of neural masses. Here, we describe a seizure control method based on coupling strengths using combination convolutional neural network (CCNN) modeling. The neurophysiologically based neural mass model (NMM), which can bridge signal processing and neurophysiology, was used to simulate the proposed controller. Although the adjacency matrix and reachability matrix could not be identified perfectly, the vast majority of adjacency values were identified, reaching 95.64% using the CCNN with an optimal threshold. For cases of discrete and continuous coupling strengths, the proposed controller maintained the average reachable cluster strengths at about 0.1, indicating effective seizure control.

中文翻译：

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基于组合卷积神经网络的神经质量可达性分析与癫痫控制

癫痫发作是由多个空间分离的神经块的同步放电引起的；因此，许多同步措施用于癫痫发作检测和表征。然而，同步测量仅反映神经元群体之间的整体相互作用强度，而不能揭示个体群体之间的耦合强度，这对于癫痫控制更为重要。可达性和可达簇的概念被提出来表示一组神经块的耦合强度。在这里，我们使用组合卷积神经网络 (CCNN) 建模描述了一种基于耦合强度的癫痫控制方法。可以桥接信号处理和神经生理学的基于神经生理学的神经质量模型 (NMM) 用于模拟所提出的控制器。虽然邻接矩阵和可达矩阵无法完美识别，但绝大多数邻接值都被识别出来，使用具有最佳阈值的 CCNN 达到了 95.64%。对于离散和连续耦合强度的情况，建议的控制器将平均可达簇强度保持在 0.1 左右，表明有效控制癫痫发作。