Complexity-based EEG biomarkers for early diagnosis of ADHD

Attention-Deficit/Hyperactivity Disorder (ADHD) is a common neurodevelopmental disorder marked by attentional dysregulation and executive dysfunction. Despite its prevalence, diagnosis remains primarily clinical, lacking robust, objective biomarkers.

This study introduces a non-invasive, data-driven method for identifying ADHD-related neural signatures using electroencephalography (EEG) combined with Lempel-Ziv Complexity (LZC). LZC quantifies the temporal complexity of neural signals, offering a sensitive measure of brain dynamics that may reveal subtle differences between clinical and control groups.

Our spatiotemporal analysis revealed that LZC effectively differentiates ADHD from control EEG patterns at the level of local temporal signal dynamics, with the most pronounced group separations observed at right-lateralized parietal and frontal electrodes, particularly P8, P7, and F8, suggesting altered local EEG signal dynamics over regions implicated in visuospatial attention and executive control. Additional sites, including T7, Pz, O1, and Fp1, exhibited moderate discriminative power, whereas midline and central regions showed minimal differences. Children with ADHD showed reduced nonlinear EEG complexity compared with controls, reflecting altered local temporal EEG signal dynamics during task engagement, particularly at right-lateralised electrodes. The most pronounced group differences were observed at P8 and F8 across both earlier and later segments of the recording, indicating consistently lower signal complexity in the ADHD group. This pattern suggests that right parietal-temporal and frontal regions carry the highest discriminative power for distinguishing children with ADHD from typically developing controls.

Unlike conventional EEG analyses focusing on power spectra or event-related potentials, the LZC-based method captures dynamic complexity, enabling more nuanced differentiation of neural activity patterns. This complexity metric provides complementary information that traditional frequency or time-domain approaches may overlook.

LZC analysis of EEG signals offers a promising complementary biomarker for ADHD, highlighting altered neural dynamics in regions implicated in attentional and executive processes. This complexity-based approach may enhance the objectivity and precision of neurodevelopmental disorder diagnostics.