发表机构
University of Maryland, College Park; Hanbat National University（马里兰大学帕克分校; 韩巴国立大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究利用动态模式分解（DMD）提取EEG高频带动力学变化，经随机分布测试后用PCA成分作为特征，成功区分酒精依赖组与对照组，为检测高频脑疾病信号提供方法。
AI 中文摘要
近期研究报告称，在特定刺激（如视觉或听觉输入）期间记录的EEG信号高频范围，或癫痫发作等脑疾病病例中，存在清晰可识别的动力学变化。本研究利用动态模式分解（Dynamic Mode Decomposition, DMD）从神经相关EEG通道的信号中提取高频带内一致且持续的动力学变化。高频DMD模式被用作特征，构成特征表。通过后处理，进行随机分布测试，结果显示约70%的样本在特定通道信号中表现出一致的高频动力学。此外，分类实验证实，通过测试的特征表的主成分分析（PCA）成分形成了一致模式，可区分酒精依赖组与对照组。
英文摘要
Recent studies have reported clearly identifiable dynamical changes in the high-frequency range of EEG signals recorded during specific stimuli, such as visual or auditory inputs, or in cases of brain disorders like epileptic seizures. In this study, we utilized Dynamic Mode Decomposition (DMD) to extract consistent and persistent dynamical changes in the high-frequency band from the signals of neurologically relevant EEG channels. High-frequency DMD modes were employed as features, composing a feature table. Through post-processing, a random distribution test was performed, revealing that approximately 70% of the samples exhibited consistent high-frequency dynamics within the signal of a specific channel. Furthermore, classification experiments confirmed that the PCA components of the feature table that passed the test formed a consistent pattern that distinguished the alcohol-dependent group from the control group.
Comments15 pages, 6 figures, 3 tables
