Transforming spontaneous premature neonatal EEG to spontaneous fetal MEG using a novel machine learning approach

The spontaneous neural activity of premature neonates has been characterized with electroencephalography (EEG). However, evaluation of normal and pathological fetal brain development is still largely unknown. Fetal magnetoencephalography (fMEG) is currently the only available technique to record fetal neural activity. Benefiting from progress in machine learning and artificial intelligence, we aimed to transfer premature EEG to fMEG, to characterize the manifestation of spontaneous activity using the knowledge obtained from premature EEG.

In this study, 30 high-resolution EEG recordings from premature newborns and 44 fMEG recordings were used to develop a transfer function to predict the spontaneous neural activity of the fetus. After preprocessing, bursts of spontaneous activity were detected using the non-linear energy operator. Next, we proposed a CycleGAN-based model to transform the premature EEG to fMEG and evaluated its performance with both time and frequency measurements.

In the time domain, the values were similar for the mean square error (< 5 %) and correlation (0.91 ± 0.05 and 0.89 ± 0.08) for both transformations between the original data and that generated by CycleGAN. However, considering the frequency content, the CycleGAN-based model modulated the frequency content of EEG to MEG transformed signals relative to the original signals by increasing the power, on average, in all frequency bands, except for the slow delta frequency band.

Our developed model showed promising potential to generate a priori signatures of fMEG manifestations related to spontaneous neural activity. Collectively, this study represents the first steps toward identifying neurobiomarkers of fetal brain development.