基于字符串学的EEG信号中的动机发现：一项ADHD案例研究
Stringology-Based Motif Discovery from EEG Signals: an ADHD Case Study
AI总结：
本文提出基于字符串学的EEG信号模式发现方法，用于分析ADHD患者的神经活动特征，揭示其在结构、稳定性和层次组织上的系统性差异。
摘要：
我们提出了一种新的计算框架，利用字符串学中的方法来分析脑电图（EEG）时间序列，以系统地识别和表征神经信号中的重复时间模式。主要目的是引入定量指标来理解神经信号动态，当前的研究结果作为概念验证。该框架采用顺序保持匹配（OPM）和笛卡尔树匹配（CTM）来检测保持相对顺序和层次结构但不受幅度缩放影响的时间模式。这种方法提供了一种时间精确的EEG动态表示，补充了传统的频谱和全局复杂性分析。为了评估其效用，我们将该框架应用于来自注意缺陷/多动障碍（ADHD）患者和匹配对照组的多通道EEG记录，使用公开数据集。通过OPM和CTM提取并量化了高度重复的组特异性模式。ADHD组表现出显著更高的模式频率，表明神经活动的重复性增加。OPM分析揭示了ADHD中模式长度更短且梯度不稳定性更大，表现为更大的均值和最大样本幅度变化。CTM分析进一步表明ADHD中层次复杂性降低，表现为树结构更浅和层次更少，尽管模式长度相当。这些发现表明，ADHD相关的EEG改变涉及重复时间模式的结构、稳定性和层次组织的系统性差异。所提出的基于字符串学的模式框架提供了一种互补的计算工具，具有在神经发育障碍中开发客观生物标志物的潜在应用。
英文摘要：
We propose a novel computational framework for analyzing electroencephalography (EEG) time series using methods from stringology, the study of efficient algorithms for string processing, to systematically identify and characterize recurrent temporal patterns in neural signals. The primary aim is to introduce quantitative measures to understand neural signal dynamics, with the present findings serving as a proof-of-concept. The framework adapts order-preserving matching (OPM) and Cartesian tree matching (CTM) to detect temporal motifs that preserve relative ordering and hierarchical structure while remaining invariant to amplitude scaling. This approach provides a temporally precise representation of EEG dynamics that complements traditional spectral and global complexity analyses. To evaluate its utility, we applied the framework to multichannel EEG recordings from individuals with attention-deficit/hyperactivity disorder (ADHD) and matched controls using a publicly available dataset. Highly recurrent, group-specific motifs were extracted and quantified using both OPM and CTM. The ADHD group exhibited significantly higher motif frequencies, suggesting increased repetitiveness in neural activity. OPM analysis revealed shorter motif lengths and greater gradient instability in ADHD, reflected in larger mean and maximal inter-sample amplitude changes. CTM analysis further demonstrated reduced hierarchical complexity in ADHD, characterized by shallower tree structures and fewer hierarchical levels despite comparable motif lengths. These findings suggest that ADHD-related EEG alterations involve systematic differences in the structure, stability, and hierarchical organization of recurrent temporal patterns. The proposed stringology-based motif framework provides a complementary computational tool with potential applications for objective biomarker development in neurodevelopmental disorders.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
