Evaluating Complexity of Fetal MEG Signals: A Comparison of Different Metrics and Their Applicability.
Frontiers in systems neuroscience January 1, 2019 Julia Moser, Siouar Bensaid, Eleni Kroupi et al.
Information-based metrics of neural activity can help quantify consciousness before and shortly after birth. Using fetal magnetoencephalography (fMEG) in human fetuses and neonates, researchers evaluated measures of entropy, compressibility, and fractality. Lempel-Ziv-Complexity (LZC) was the most practical metric because it is unequivocal and requires low computational effort, whereas fractality and entropy measures need more parameter adjustments. Comparing a brain-activity channel with a control channel in neonates showed significant differences in most complexity metrics, serving as proof of concept. For fetal data, results were less clear, possibly due to leftover maternal signals. The inconsistency across metrics highlights challenges in using complexity metrics as neural correlates of consciousness.