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.

翻译：我们提出了一种新颖的计算框架，利用字符串学（研究字符串处理高效算法的学科）中的方法分析脑电图时间序列，以系统性地识别和表征神经信号中的重复性时间模式。其主要目标是引入量化指标以理解神经信号动力学，当前研究结果可作为概念验证。该框架采用保序匹配和笛卡尔树匹配技术，检测能保持相对顺序和层次结构、同时对幅度缩放具有不变性的时间基序。这种方法提供了脑电图动力学的时间精确表征，是对传统频谱分析和全局复杂性分析的有效补充。为评估其效用，我们应用该框架分析了来自公开数据集中注意缺陷多动障碍患者与匹配对照者的多通道脑电图记录。通过OPM和CTM方法提取并量化了高度重复且具有组别特异性的基序。ADHD组表现出显著更高的基序出现频率，表明神经活动重复性增强。OPM分析显示ADHD组基序长度更短且梯度不稳定性更大，这体现在更大的样本间幅度变化均值与极值。CTM分析进一步表明ADHD组的层次复杂性降低，其特征表现为在基序长度相近的情况下，树结构更浅且层次更少。这些发现提示ADHD相关的脑电图改变涉及重复性时间模式在结构、稳定性和层次组织方面的系统性差异。所提出的基于字符串学的基序框架提供了一种互补的计算工具，在神经发育障碍客观生物标志物开发方面具有潜在应用价值。