Despite the growing interest in the use of electroencephalogram (EEG) signals as a potential biometric for subject identification and the recent advances in the use of deep learning (DL) models to study neurological signals, such as electrocardiogram (ECG), electroencephalogram (EEG), electroretinogram (ERG), and electromyogram (EMG), there has been a lack of exploration in the use of state-of-the-art DL models for EEG-based subject identification tasks owing to the high variability in EEG features across sessions for an individual subject. In this paper, we explore the use of state-of-the-art DL models such as ResNet, Inception, and EEGNet to realize EEG-based biometrics on the BED dataset, which contains EEG recordings from 21 individuals. We obtain promising results with an accuracy of 63.21%, 70.18%, and 86.74% for Resnet, Inception, and EEGNet, respectively, while the previous best effort reported accuracy of 83.51%. We also demonstrate the capabilities of these models to perform EEG biometric tasks in real-time by developing a portable, low-cost, real-time Raspberry Pi-based system that integrates all the necessary steps of subject identification from the acquisition of the EEG signals to the prediction of identity while other existing systems incorporate only parts of the whole system.

Fetal electrocardiogram (fECG) signals recorded from maternal abdominal electrodes are emerging as a promising non-invasive technique for monitoring fetal health. The principal challenge lies in effectively extracting the weak fetal signal from the overlapping maternal electrocardiogram (mECG) components present in abdominal recordings. This review examines the application of deep learning models to address these challenges, enhancing both signal extraction and analysis. We assess various model architectures—including convolutional, recurrent, hybrid, autoencoder, and generative adversarial networks—evaluate their strengths and weaknesses, and discuss the critical settings necessary for optimal functionality, together with a review of open-source datasets. We also outline challenges and future directions, including the need for larger and more diverse datasets, standardized evaluation protocols, improved interpretability, efficient real-time implementations, and advanced modeling approaches such as transformer-based foundation models. These developments will enable deep learning–based fECG analysis to truly provide clinically actionable insights and scalable maternal–fetal monitoring.