Stringology-Based Motif Discovery from EEG Signals: an ADHD Case Study

2026-03-03

AI Summary

This study addresses the identification of recurrent temporal patterns in multichannel EEG signals associated with attention-deficit/hyperactivity disorder (ADHD) and quantifies their structural and dynamic properties. Introducing, for the first time in this context, order-preserving matching (OPM) and Cartesian tree matching (CTM) from stringology, the work enables motif discovery that is invariant to amplitude scaling while preserving relative ordinal relationships and hierarchical structure. The results reveal that individuals with ADHD exhibit higher motif frequencies, shorter OPM motif lengths, greater amplitude gradient variability, and shallower CTM tree structures. These findings indicate enhanced repetitiveness yet reduced hierarchical complexity in neural dynamics, offering a novel computational perspective on neurodevelopmental disorders.

Technology Category

• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Data Mining & Knowledge Management: Mining of Spatial, Temporal or Spatio-Temporal Data
• Search and Optimization: Mixed Discrete/Continuous Search

Application Category

• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Web Mining and Content Analysis: Mining multimedia, multimodal, multilingual, cross-lingual Web data

Abstract

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.

Problem

• EEG
• ADHD
• temporal motifs
• neural signal dynamics
• stringology

Innovation

• stringology
• order-preserving matching
• Cartesian tree matching
• temporal motifs
• EEG analysis