Electroencephalogram (EEG) signals accumulate the brain’s spiking activities using standardized electrodes placed at the scalp. These cumulative brain signals are chaotic in nature and vary depending upon current physical and/or mental activities. The anatomy of the brain is altered when dopamine releasing neurons die because of Parkinson Disease (PD), a neurodegenerative disorder. The resulting alterations force synchronized neuronal activity in $\beta$ frequency components deep within motor region of the brain. This synchronization in the motor region affects the dynamical behavior of the brain activities, which induce motor related impairments in patient’s limbs. Identification of reliable bio-markers for PD is active research area since there are no tests or scans to diagnose PD. We use embedding reconstruction, a tool from chaos theory, to highlight PD-related alterations in dynamical properties of EEG and present it as a potentially reliable bio-marker for PD related classification. We use Individual Component Analysis (ICA) to demonstrate that the strengthened synchronizations can be cumulatively collected from EEG channels over the motor region of the brain. We use this information to select the 12 EEG channels for classification of On and Off medication PD patients. Additionally, there is the strong synchronization between amplitude of higher frequency components and phase of $\beta$ components for PD patients. This information is used to improve the performance of this classification. We apply embedding reconstruction to design a new architecture of a deep neural network called Dynamical system Generated Hybrid Network. We report that this network outperforms the state of the art in terms of classification accuracy of $99.2\text{\%}(+0.52\text{\%})$ with approximately 24% of the computational resources. Apart from classification accuracy, we use well known statistical measures like specificity, sensitivity, Matthews Correlation Coefficient (MCC), F1 score, and Cohen Kappa score for the analysis and comparison of classification performances.

脑电图（EEG）信号使用放置在头皮上的标准化电极来累积大脑的尖峰活动。这些累积的脑信号本质上是混乱的，并且根据当前的身体和/或精神活动而变化。当释放多巴胺的神经元由于帕金森氏病（PD）（一种神经退行性疾病）而死亡时，大脑的解剖结构发生了变化。产生的变化迫使同步神经元活动$\beta$大脑运动区域深处的频率成分。运动区域的这种同步影响大脑活动的动力学行为，这会导致患者四肢运动相关的损伤。由于没有用于诊断PD的测试或扫描，因此鉴定PD的可靠生物标记物是活跃的研究领域。我们使用嵌入重构（一种来自混沌理论的工具）来突出脑电动力学特性中与PD相关的变化，并将其作为PD相关分类的潜在可靠生物标记。我们使用个体成分分析（ICA）来证明可以从大脑运动区域的EEG通道中累积收集到增强的同步性。我们使用此信息来选择12个EEG通道，以对开和关药物PD患者进行分类。另外，$\beta$PD患者的组件。此信息用于改进此分类的性能。我们应用嵌入重建来设计一种新的深度神经网络架构，称为动态系统生成的混合网络。我们报告说，该网络在分类准确度方面优于最新技术$99。2\text{％}（+0。52\text{％}）$约有24％的计算资源。除了分类的准确性外，我们还使用众所周知的统计指标，如特异性，敏感性，马修斯相关系数（MCC），F1得分和Cohen Kappa得分来分析和比较分类性能。