用动态模态分解法从脑电图中捕捉高频异常信号，识别酒精依赖者特征。
Detecting high-frequency brain disorder signals using dynamic mode decomposition from EEG
- 用DMD分析脑电信号高频段的持续动力学变化
- 70%样本在特定通道表现出一致的高频动态
- 可区分酒精依赖者与健康对照组，适合神经疾病研究
近期研究发现，在特定刺激（如视觉或听觉输入）或脑部疾病（如癫痫发作）期间，脑电图（EEG）信号在高频范围存在可辨识的动力学变化。本研究利用动态模态分解（Dynamic Mode Decomposition, DMD）从神经相关脑电通道信号中提取高频带中的一致且持久的动力学特征，并构建特征表。通过后处理中的随机分布检验发现，约70%的样本在特定通道信号中表现出一致的高频动力学特征。此外，分类实验表明，通过检验的特征表主成分构成了一致模式，能有效区分酒精依赖组与对照组。
原文摘要 · Abstract (English)
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.
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