Neural development leads to the evolution of electroencephalographic (EEG) characteristics during the third trimester of gestation. Theta activity in coalescence with slow waves (TA-SW) and delta brushes (DB) are key clinical neurobiomarkers in the evaluation of neurodevelopment in infants prior to full-term gestation. Both neurobiomarkers exhibit nested oscillations, a key feature of intrinsic spontaneous oscillatory activity, allowing the investigation of neural interaction development in the underlying circuits. In the present study, we propose an automatic approach for the detection and characterization of neurobiomarkers that (1) leverages high-density EEG (HD-EEG), (2) incorporates temporal dynamics and spatial distributions, and (3) evaluates the characteristics of nested oscillations. This method evaluates both slow and rapid neural activity, along with their cross-frequency coupling. Our results are in good agreement with those of clinical experts, achieving ROC performances and overall accuracies of 91 %/84 % and 83 %/75 % for TA-SW/DB events, respectively. Following detection and validation, we characterized and compared these two neurobiomarkers. Correlation-based spatial clustering showed that DB patterns were more symmetric and diffuse, whereas TA-SW patterns were more localized in the right and left temporal areas. Comparisons revealed (1) greater variability in spatial patterns for DB than for TA-SW, and that (2) while slow-wave coupling to fast oscillations showed similar characteristics for both neurobiomarkers, differences emerged in the amplitude and descending slope of the underlying slow waves. These findings suggested potential differences in the mechanisms underlying their generation, particularly in the modulation of slow oscillations. This approach represents a promising avenue for the quantitative evaluation of EEG signatures pertinent to early neural development in premature neonates.