Аннотация
Electroencephalography (EEG) provides a non-invasive means to investigate the dynamic behavior of the brain through electrical activity. Traditional linear analysis methods tend to be insufficient in capturing the complexity of neural signals. This study applies nonlinear dynamic analysis - the Largest Lyapunov Exponent (LLE) - to EEG data recorded during an emotional conflict task in children and young adults. The aim of the study was to define age and sex differences during emotional conflict task performance in Lyapunov exponent of EEG signal. EEG were recorded as participants performed task with viewing facial expressions and hearing auditory words corresponding to four emotions: angry, sad, happy and fearful. EEG preprocessing included filtering, artifact removal by using Independent Component Analysis (ICA). Electrodes were grouped into functional brain networks: anterior, posterior, executive, z-network and left and right hemispheres. LLE was computed from each epoch for broadband to quantify the underlying chaotic dynamics of brain activity, and separately for alpha and theta bands as emotional features. Repeated-measures ANOVA revealed significant differences across brain networks, and interactions with age, sex, and emotions. Machine learning classification found that sex-stratified emotion decoding revealed band-dependent asymmetries: in broadband features, male participants showed higher four-way emotion decoding accuracy (~30.1%) than female participants (~23.5%), while the pattern reversed in the alpha band, where females outperformed males (~27.6% vs. ~19.8%). These interactions suggest that sex differences in LLE-based emotion separability are spectrally specific.