The increasing growth in knowledge about the functioning of the nervous system of mammals and humans, as well as the significant neuromorphic technology developments in recent decades, has led to the emergence of a large number of brain–computer interfaces and neuroprosthetics for regenerative medicine tasks. Neurotechnologies have traditionally been developed for therapeutic purposes to help or replace motor, sensory or cognitive abilities damaged by injury or disease. They also have significant potential for memory enhancement. However, there are still no fully developed neurotechnologies and neural interfaces capable of restoring or expanding cognitive functions, in particular memory, in mammals or humans. In this regard, the search for new technologies in the field of the restoration of cognitive functions is an urgent task of modern neurophysiology, neurotechnology and artificial intelligence. The hippocampus is an important brain structure connected to memory and information processing in the brain. The aim of this paper is to propose an approach based on deep neural networks for the prediction of hippocampal signals in the CA1 region based on received biological input in the CA3 region. We compare the results of prediction for two widely used deep architectures: reservoir computing (RC) and long short-term memory (LSTM) networks. The proposed study can be viewed as a first step in the complex task of the development of a neurohybrid chip, which allows one to restore memory functions in the damaged rodent hippocampus.

Sleep spindles, together with K-complexes, are the distinctive patterns of neuronal activity in EEG recordings during stage 2 sleep. When the mechanisms of sleep spindle generation are impaired, e.g., in epilepsy, their quantitative parameters change. The analysis of these changes can provide valuable insights into the formation of epileptiform activity patterns and help to develop an additional tool for more accurate medical diagnosis. Despite the central role of EEG in the diagnosis of epilepsy, disorders of consciousness, and neurological research, resources specifically dedicated to large-scale EEG data analysis are under-represented. In our study, we collect a specialized database of clinical EEG recordings from epilepsy patients and controls during N2 sleep, characterized by rhythmic spindle activity in frontocentral and vertex regions, and manually annotate them. We then quantify four key sleep spindle characteristics using a comparison of manual annotation by a clinician and artificial intelligence technologies. A thorough evaluation of state-of-the-art deep learning architectures for detecting and characterizing sleep spindles in EEG recordings from epilepsy patients is conducted. The results show that the 1D U-Net and SEED architectures achieve competitive overall performance, but their precision-to-recall ratios differ markedly in clinical settings. This suggests that different approaches may be appropriate for each clinical situation. Furthermore, our results demonstrate that epilepsy is associated with significant and quantifiable changes in sleep spindle morphology and frequency. Automated analysis of these characteristics using artificial intelligence provides a reliable biomarker that provides a detailed picture of thalamocortical dysfunction in epilepsy. This approach has great potential for accelerated diagnosis and the development of targeted therapeutic strategies for epilepsy.

Sleep spindles, together with K-complexes, are hallmark oscillatory events observed in electroencephalographic (EEG) recordings during stage N2 sleep. Alterations in spindle characteristics, including frequency, amplitude, duration, and density, are frequently reported in epilepsy and may reflect underlying disturbances in thalamocortical network function. Quantitative analysis of these alterations has the potential to improve our understanding of epileptiform activity and support the development of clinically useful biomarkers. In this work, we present an automated framework for sleep spindle analysis in EEG recordings from both healthy subjects and patients with epilepsy. The framework integrates deep learning architectures (1D U-Net, SlumberNet, and SEED) with statistical evaluation methods to address two complementary tasks: (i) spindle segmentation and (ii) direct regression-based prediction of spindle characteristics. The proposed approach was validated on two datasets: the open-access Montreal Archive of Sleep Studies (MASS) and a custom clinical database of pediatric epilepsy patients acquired at the Video-EEG Laboratory “Genomed” (Moscow, Russia). Our results demonstrate that while both convolutional and hybrid recurrent–convolutional architectures achieve comparable overall F1-scores, their precision–recall profiles differ substantially. This enables a principled, context-specific selection of models, with U-Net favoring high sensitivity and SEED favoring high precision. Moreover, we show that segmentation-based pipelines consistently outperform direct regression (segmentation-free) approaches for characteristic prediction. These findings provide methodological guidance for the optimal deployment of deep learning models in sleep spindle analysis and establish a foundation for robust, automated, and clinician-independent EEG biomarkers in epilepsy.

Automatic analysis of electroencephalography (EEG) recordings relies on large, high-quality labeled datasets. Manual segmentation by medical experts is resource-intensive and time-consuming. Moreover, to overcome potential subjectivity in labeling, independent annotation by at least two experts is required. Therefore, reliable automatic data labeling is essential for obtaining the large datasets needed to train robust AI models. In this paper, we show that a properly trained state-of-the-art deep neural network (DNN) achieves labeling performance comparable to inter-expert agreement in the task of segmenting epileptiform activity patterns. To this end, we first compiled a custom database of EEG recordings containing such patterns. Second, five experts based on part of these recordings independently assessed spike-wave index (SWI), which is a key diagnostic criterion that indicates the percentage of the EEG recording during which epileptic discharges are observed. Third, we compared the expert assessments with SWI calculated based on automatic segmentation by the trained DNN. Our results demonstrate that the 1D U-Net architecture achieves competitive overall performance and aligns well with both expert assessments and expert-derived SWI values. Thus, automated segmentation and analysis of EEG recordings holds great promise for accelerating diagnosis and developing targeted therapeutic strategies for epilepsy.