The functional development of the fetal brain is critical for long-term health, yet current diagnostic tools lack the capability to examine it effectively. This paper presents a non-invasive method for extracting fetal brain signals using abdominal recordings obtained before elective cesarean sections, with neonatal brain activity recorded post-delivery serving as a reference. The recorded abdominal signals were preprocessed using digital filters and separated into independent components using the blind source separation technique: Independent component analysis. These components were analyzed to identify potential fetal brain signals based on their similarity to postnatal brain activity and dissimilarity to maternal cardiac signals, which are the primary source of interference in abdominal recordings. Features related to time and frequency characteristics were extracted, and a feature selection was conducted to identify the most informative ones. Preliminary results using simulated data demonstrated effective signal separation, with some segments closely resembling postnatal brain activity in spectral features. Low-frequency bands showed the strongest potential for distinguishing fetal brain activity from maternal interference. This approach demonstrates a feasible pathway for non-invasive fetal brain monitoring with implications for early detection of neurological development issues.

Fetal electroencephalogram (fEEG) recording could address the gap of functional fetal brain testing and enable direct assessment of fetal brain status during pregnancy and labor. However, the fetal EEG signal (~1 µV) is masked by roughly 80 dB (about 10,000×) of maternal ECG and other bioelectric interferences. This synthetic-data feasibility study presents whether the fetal EEG can be recovered from the maternal abdomen surface by using the following three sensing architectures on physiologically scaled synthetic data: single electrode, dual electrode, and a concentric 28-electrode array centered over the fetal head. We show that exploiting a known geometric attenuation steering vector with a minimum-variance distortionless-response (MVDR) beamformer raises recovery correlation from ~0 to ~0.28 and improves signal-to-noise ratio (SNR) by ~67 dB. A follow-up validation replaces the synthetic EEG generator with actual neonatal EEGs recorded from the scalp, obtained from OpenNeuro ds004577 and the Helsinki Zenodo corpus. Comparable recovery performance across both datasets confirms that the synthetic feasibility conclusion generalizes to real neonatal EEG composition and behavioral characteristics. The primary evaluation is based on waveform-level engineering metrics (correlation, SNR, and RMSE), while exploratory secondary analyses assess aEEG envelopes, band-power trends, and burst detection. These analyses are not intended as clinical validation. Independent per-electrode sensor noise remains the dominant residual limiter under the homogeneous geometric model. The reported r ≈ 0.28 should not be interpreted as expected in vivo performance at all gestational ages: an intact vernix layer can add approximately 35 dB of attenuation and makes the required noise floor substantially more stringent. This analysis adds a five-layer volume-conductor analysis, misalignment, depth and impedance-drift stress tests, recording-level statistics, comparisons with classical extraction families (PCA, ICA, adaptive cancelation, and multichannel Wiener filtering), an interference-alignment sensitivity analysis, and exploratory biomarker-level analyses, which together bound the idealizations of the forward model.