Research interest in the application of electroencephalogram (EEG) as a non-invasive diagnostic tool for the automated detection of neurodegenerative diseases is growing. Open-access datasets have become crucial for researchers developing such methodologies. Our previously published open-access dataset of resting-state (eyes-closed) EEG recordings from patients with Alzheimer’s disease (AD), frontotemporal dementia (FTD), and cognitively normal (CN) controls has attracted significant attention. In this paper, we present a complementary dataset consisting of eyes-open photic stimulation recordings from the same cohort. The dataset includes recordings from 88 participants (36 AD, 23 FTD, and 29 CN) and is provided in Brain Imaging Data Structure (BIDS) format, promoting consistency and ease of use across research groups. Additionally, a fully preprocessed version is included, using EEGLAB-based pipelines that involve filtering, artifact removal, and Independent Component Analysis, preparing the data for machine learning applications. This new dataset enables the study of brain responses to visual stimulation across different cognitive states and supports the development and validation of automated classification algorithms for dementia detection. It offers a valuable benchmark for both methodological comparisons and biological investigations, and it is expected to significantly contribute to the fields of neurodegenerative disease research, biomarker discovery, and EEG-based diagnostics.

The rapid development of digital technologies is opening up new avenues for transforming education, particularly in fields that require practical training, such as electronic circuit design. In this context, this paper presents the development of a multiplayer virtual learning platform that makes use of digital twins technology to offer a realistic, collaborative experience in a simulated environment. Users can interact in real time through synchronized avatars, voice communication, and multiple viewing angles, simulating a physical classroom. Evaluation of the platform with undergraduate students showed positive results in terms of usability, collaboration, and learning effectiveness. Despite the limitations of the sample, the findings reinforce the prospect of virtual laboratories as a modern tool in technical education.

Digital Twins are an innovative approach to medical education, allowing realistic simulation of clinical procedures. Their combination with feedback technologies, such as Artificial Intelligence (AI), significantly enhances training in complex and specialized scenarios, such as those related to neurodegenerative diseases. In this paper, we present an integrated virtual avatar system with embedded AI, designed based on data from real patients in everyday environments. This educational environment offers an immersive and interactive learning experience, targeting both healthcare professionals, caregivers and ordinary users, through questions and scenarios that simulate reality. The methodology is based on machine learning models and real-time behavioral simulations, aiming to reproduce symptoms of conditions such as Parkinson's and Alzheimer's disease. The system allows researchers to practice diagnostic and treatment strategies in a controlled environment, while providing caregivers with practical guidelines for daily patient care. In addition, integration into virtual reality (VR) environments is supported. A user study was conducted to evaluate the effectiveness of the system, focusing on user engagement, educational value and clinical applicability. Results demonstrate significant improvement in knowledge acquisition and retention, as well as in the development of diagnostic skills.

Stress is a subject always relevant to scientific research due to the numerous implications in human life. Typical biomarkers used in the physiological evaluation of stress include Electrocardiography, cortisol levels, galvanic skin response and other. Recently, one less widely used instrument for the assessment of stress that has been re-emerged due to advancements in computational power and machine learning techniques, is Electroencephalography. Moreover, as Virtual Reality HMDs are being rapidly adopted by the research community it becomes apparent that leveraging the offered advantages of VR for the exploration of stress can lead to novel controlable and reproducable experimental procedures. In this paper we combine EEG, ECG and the Perceived Stress Scale with a Virtual Reality phobia induction setting, to propose a protocol for assessing stress. The suggested protocol can be used for functional brain connectivity investigation and thus the evaluation of stress while it and can be expanded via the incorporation of machine learning algorithms for automatic stress level classification.

Parkinson’s disease is a neurological disorder characterized by motor and non-motor symptoms. Assessment methods, despite the many years of existence of the disease, lack individualized visualization. On the other hand, virtual reality promises immersion and realism. In this paper, we develop an integrated system for visualizing the gestures of Parkinson’s disease patients in a virtual reality environment. With this application, clinicians will have information about the unique motor patterns and challenges they must address in each individual patient’s case, while the collected data can travel and be easily and instantly visualized in any location. At the beginning of this research, the current terms of immersive technologies in conjunction with data visualization and Parkinson’s disease are described. Through an extensive systematic literature review, the technological developments in the field of Parkinson’s data visualization are presented. The findings of the review lead to the experimental procedure and implementation of the application. The conclusions drawn from this work fuel future extensions on the contribution of immersive technologies to various diseases.

Alzheimer's Disease (AD) and Frontotemporal Dementia (FTD) are two of the most common neurodegenerative disorders, most commonly leading to progressive cognitive decline. Electroencephalography (EEG) has been proven as a promising tool for early and non-invasive detection of these disorders, leveraging advanced computational methods for classification. In this study, we propose a novel Convolutional Neural Network (CNN) architecture for the automatic classification of AD and FTD using EEG-based biomarkers. Our approach utilizes Power Spectral Density derived Relative Band Power and Spectral Coherence Index as input features, extracted from preprocessed EEG signals of 88 participants following Independent Component Analysis and Artifact Subspace Reconstruction for noise reduction. The extracted features capture both spectral power variations and functional connectivity disruptions, key findings of neurodegenerative disorders. The proposed CNN model is trained and validated using a Leave-One-Subject-Out crossvalidation approach to ensure robust generalization. Experimental results demonstrate that our model achieves high classification accuracy, outperforming state-of-the-art machine learning methods and achieving competitive performance with state-of-the-art deep learning approaches. These findings highlight the effectiveness of EEG-based deep learning models for differential dementia diagnosis, providing a potential avenue for early detection and clinical decision support.