Longitudinal Stability of Detrended Fluctuation Analysis in Monthly Recorded EEG Signals

Electroencephalography (EEG) is a cost-effective, noninvasive method with high temporal resolution that enables detailed assessment of neuronal activity. Detrended fluctuation analysis (DFA), a nonlinear technique for quantifying long-range temporal correlations in time-series data, has found application in EEG research across various contexts. This study investigates the temporal stability of DFA over the course of one year, during which 12 monthly EEG recordings were collected from each of nine healthy male participants. Our findings demonstrate excellent within-subject reliability, with intraclass correlation coefficients (ICCs) ranging from 0.985 to 0.997 across 30 EEG channels. These high ICCs indicate that interindividual variability exceeds intraindividual variability, supporting DFA's reliability for long-term neural monitoring. Despite considerable differences between individuals, DFA remained consistent within subjects, highlighting its potential as a subject-specific biomarker for neurological disorders such as epilepsy, depression, and Alzheimer's disease. These findings underscore the importance of accounting for individual variability in EEG measures when developing tools for early diagnosis and clinical monitoring.Clinical Relevance- Reliable biomarkers for neuronal activity must demonstrate consistent temporal stability. This study shows that DFA offers excellent stability within individuals, supported by ICC analyses, suggesting its potential as a subject-specific biomarker for early detection of disorders characterized by altered EEG dynamics.