Virtual Reality as a Portable Alternative to Chromotherapy Rooms for Stress Relief: A Preliminary Study

Chromotherapy rooms are comfortable spaces, used in places like special needs schools, where stimuli are carefully selected to cope with stress. However, these rooms are expensive and require a space that cannot be reutilized. In this article, we propose the use of virtual reality (VR) as an inexpensive and portable alternative to chromotherapy rooms for stress relief. We recreated a chromotherapy room stress relief program using a commercial head mounted display (HD). We assessed the stress level of two groups (test and control) through an EEG biomarker, the relative gamma, while they experienced a relaxation session. First, participants were stressed using the Montreal imaging stress task (MIST). Then, for relaxing, the control group utilized a chromotherapy room while the test group used virtual reality. We performed a hypothesis test to compare the selfperceived stress level at di erent stages of the experiment and it yielded no significant di erences in reducing stress for both groups, during relaxing (p-value: 0.8379, = 0.05) or any other block. Furthermore, according to participant surveys, the use of virtual reality was deemed immersive, comfortable and pleasant (3.9 out of 5). Our preliminary results validate our approach as an inexpensive and portable alternative to chromotherapy rooms for stress relief.Spanish Ministry of Science, Innovation and Universities PGC2018-098813-B-C31 TIN2016-75097-PEuropean Union (EU) PGC2018-098813-B-C31Nicolo Association for the R&D in Neurotechnologies for disabilit

Virtual Reality Customized 360-Degree Experiences for Stress Relief

The latest studies in virtual reality (VR) have evidenced the potential of this technology to reproduce environments from multiple domains in an immersive way. For instance, in stress relief research, VR has been presented as a portable and inexpensive alternative to chromotherapy rooms, which require an adapted space and are expensive. In this work, we propose a portable and versatile alternative to the traditional chromotherapy color-loop treatment through four different 360-degree virtual experiences. A group of 23 healthy participants (mean age 22.65 ± 5.48) were conducted through a single-session experience divided into four phases while their electroencephalography (EEG) was recorded. First, they were stressed via the Montreal imaging stress task (MIST), and then relaxed using our VR proposal. We applied the Wilcoxon test to evaluate the relaxation effect in terms of the EEG relative gamma and self-perceived stress surveys. The results that we obtained validate the effectiveness of our 360-degree proposal to significantly reduce stress (p-value = 0.0001). Furthermore, the participants deemed our proposal comfortable and immersive (score above 3.5 out of 5). These results suggest that 360-degree VR experiences can mitigate stress, reduce costs, and bring stress relief assistance closer to the general public, like in workplaces or homes

Dry EEG Electrodes

Electroencephalography (EEG) emerged in the second decade of the 20th century as a technique for recording the neurophysiological response. Since then, there has been little variation in the physical principles that sustain the signal acquisition probes, otherwise called electrodes. Currently, new advances in technology have brought new unexpected fields of applications apart from the clinical, for which new aspects such as usability and gel-free operation are first order priorities. Thanks to new advances in materials and integrated electronic systems technologies, a new generation of dry electrodes has been developed to fulfill the need. In this manuscript, we review current approaches to develop dry EEG electrodes for clinical and other applications, including information about measurement methods and evaluation reports. We conclude that, although a broad and non-homogeneous diversity of approaches has been evaluated without a consensus in procedures and methodology, their performances are not far from those obtained with wet electrodes, which are considered the gold standard, thus enabling the former to be a useful tool in a variety of novel applications.This work was supported by Nicolo Association for the R+D+i in Neurotechnologies for disability, the research project P11-TIC-7983, Junta of Andalucia (Spain) and the Spanish National Grant TIN2012-32030, co-financed by the European Regional Development Fund (ERDF). We also thank Erik Jung, head of the Medical Microsystems working group, at the Department of System Integration & Interconnection Technologies, Fraunhofer IZM (Berlin), for his support

Evaluating the feasibility of cognitive impairment detection in Alzheimer’s disease screening using a computerized visual dynamic test

Background Alzheimer’s disease (AD) is a neurodegenerative disease without known cure. However, early medical treatment can help control its progression and postpone intellectual decay. Since AD is preceded by a period of cognitive deterioration, the effective assessment of cognitive capabilities is crucial to develop reliable screening procedures. For this purpose, cognitive tests are extensively used to evaluate cognitive areas such as language, attention, or memory. Methods In this work, we analyzed the potential of a visual dynamics evaluation, the rapid serial visual presentation task (RSVP), for the detection of cognitive impairment in AD. We compared this evaluation with two of the most extended brief cognitive tests applied in Spain: the Clock-drawing test (CDT) and the Phototest. For this purpose, we assessed a group of patients (mild AD and mild cognitive impairment) and controls, and we evaluated the ability of the three tests for the discrimination of the two groups. Results The preliminary results obtained suggest the RSVP performance is statistically higher for the controls than for the patients (p-value = 0.013). Furthermore, we obtained promising classification results for this test (mean accuracy of 0.91 with 95% confidence interval 0.72, 0.97). Conclusions Since the RSVP is a computerized, auto-scored, and potentially self-administered brief test, it could contribute to speeding-up cognitive impairment screening and to reducing the associated costs. Furthermore, this evaluation could be combined with other tests to augment the efficiency of cognitive impairment screening protocols and to potentially monitor patients under medical treatment.FEDER/Junta de Andalucía-Council for Economic Transformation, Industry, Knowledge and Universities/ grant (B-TIC-352- UGR20); grant PID2021-128529OA-I00, MCIN / AEI / 10.13039 / 501100011033ERDF A way of making Europe; grant PROYEXCEL_00084, Projects for Excellence Research,Council for Economic Transformation,Industry, Knowledge and Universities, Junta de Andalucía 2021Circuits And Systems for Information Processing (CASIP) research group, TIC-117 (PAIDI Junta de Andalucia)PGC2018-098813-B-C31 and PGC2018-098813-B-C32 (Spanish Ministry of Science, Innovation and Universities

An Automated Approach for the Detection of Alzheimer’s Disease From Resting State Electroencephalography

Early detection is crucial to control the progression of Alzheimer’s disease and to postpone intellectual decline. Most current detection techniques are costly, inaccessible, or invasive. Furthermore, they require laborious analysis, what delays the start of medical treatment. To overcome this, researchers have recently investigated AD detection based on electroencephalography, a non-invasive neurophysiology technique, and machine learning algorithms. However, these approaches typically rely on manual procedures such as visual inspection, that requires additional personnel for the analysis, or on cumbersome EEG acquisition systems. In this paper, we performed a preliminary evaluation of a fully-automated approach for AD detection based on a commercial EEG acquisition system and an automated classification pipeline. For this purpose, we recorded the resting state brain activity of 26 participants from three groups: mild AD, mild cognitive impairment (MCI-non-AD), and healthy controls. First, we applied automated data-driven algorithms to reject EEG artifacts. Then, we obtained spectral, complexity, and entropy features from the preprocessed EEG segments. Finally, we assessed two binary classification problems: mild AD vs. controls, and MCI-non-AD vs. controls, through leave-one-subject-out cross-validation. The preliminary results that we obtained are comparable to the best reported in literature, what suggests that AD detection could be automatically detected through automated processing and commercial EEG systems. This is promising, since it may potentially contribute to reducing costs related to AD screening, and to shortening detection times, what may help to advance medical treatment.PID2021-128529OA-I00 Spanish Ministry of Science, Innovation and UniversitiesEuropean Regional Development FundsBTIC- 352-UGR20Operative Program FEDER 2014–2020Economy, Universities and Science Office of the Andalusian Regional Governmen

A self-driven approach for multi-class discrimination in Alzheimer’s disease based on wearable EEG

Early detection is critical to control Alzheimer’s disease (AD) progression and postpone cognitive decline. Traditional medical procedures such as magnetic resonance imaging are costly, involve long waiting lists, and require complex analysis. Alternatively, for the past years, researchers have successfully evaluated AD detection approaches based on machine learning and electroencephalography (EEG). Nonetheless, these approaches frequently rely upon manual processing or involve non-portable EEG hardware. These aspects are suboptimal regarding automated diagnosis, since they require additional personnel and hinder porta- bility. In this work, we report the preliminary evaluation of a self-driven AD multi-class discrimination approach based on a commercial EEG acquisition system using sixteen channels. For this purpose, we recorded the EEG of three groups of participants: mild AD, mild cognitive impairment (MCI) non-AD, and controls, and we implemented a self-driven analysis pipeline to discriminate the three groups. First, we applied automated artifact rejection algorithms to the EEG recordings. Then, we extracted power, entropy, and complexity features from the preprocessed epochs. Finally, we evaluated a multi-class classification problem using a multi-layer perceptron through leave-one-subject-out cross-validation. The preliminary results that we obtained are comparable to the best in literature (0.88 F1-score), what suggests that AD can potentially be detected through a self-driven approach based on commercial EEG and machine learn- ing. We believe this work and further research could contribute to opening the door for the detection of AD in a single consultation session, therefore reducing the costs associated to AD screening and poten- tially advancing medical treatment.Spanish Government PGC2018-098813-B-C31European Commission Operative Program FEDER 2014-2020 BTIC-352-UGR20Economy, Universities and Science Office of the Andalusian Regional GovernmentUniversidad de Granada/CBU

Low-Cost EEG Multi-Subject Recording Platform for the Assessment of Students’ Attention and the Estimation of Academic Performance in Secondary School

The level of student attention in class greatly affects their academic performance. Teachers typically rely on visual inspection to react to students’ attention in time, but this subjective method leads to inconsistencies across classes. Online education exacerbates the issue as students can turn off cameras and microphones to keep their own privacy. To address this, we present a novel, low-cost EEG-based platform for assessing students’ attention and estimating their academic performance. In a study involving 34 secondary school students (aged 14 to 16), participants watched an academic video and answered evaluation questions while their EEG activity was recorded using a commercial headset. The results demonstrate a significant correlation (0.53, p-value = 0.003) between the power spectral density (PSD) of the EEG beta band (12–30 Hz) and students’ academic performance. Additionally, there was a notable difference in PSD-beta between high and low academic performers. These findings encourage the use of PSD-beta for the immediate and objective assessment of both the student attention and the subsequent academic performance. The platform offers valuable and objective feedback to teachers, enhancing the effectiveness of both face-to-face and online teaching and learning environments.This work was supported by grant B-TIC-352-UGR20, Junta de Andalucía; grant PID2021-128529OA-I00, MCIN/AEI/10.13039/501100011033, and by ERDF A way of making Europe; grant PP2021.PP-28, University of Granada; grant PROYEXCEL_00084, P21_00084P21_00084, Projects for Excellence Research, funded by the Council for Economic Transformation, Industry, Knowledge and Universities, Junta de Andalucía 2021.The study was approved by The Research Ethics Committee of the University of Granada (3582/CEIH/2023

Web 2.0: Arquitectura orientada a servicios en Java

Este trabajo presenta los contenidos del curso “Web 2.0: Arquitectura Orientada a Servicios en Java” de la Escuela de Posgrado de la Universidad de Granada. El objetivo del curso es familiarizar al alumno con la programación de Servicios Web. Dada la gran variedad de técnicas disponibles para utilizar Arquitectura Orientada a Servicios, se presentan las siguientes técnicas: utilización de protocolos bien definidos para comunicación y contrato (SOAP y WSDL), creaci´on de Web Services con JAX-WS, orquestación de Servicios Web con BPEL. Al final del curso, el alumno será capaz de crear, utilizar y mantener Servicios Web para el desarrollo de aplicaciones interempresariales, utilizando servicios ya disponibles en la web, así como la orquestación lógica de los mismos.Financiado con los proyectos AmIVital (CENIT2007-2010), EvOrq (TIC-3903) y Beca FPU AP2009-294

Clustering and beamforming for efficient communication in wireless sensor networks

Energy efficiency is a critical issue for wireless sensor networks (WSNs) as sensor nodes have limited power availability. In order to address this issue, this paper tries to maximize the power efficiency in WSNs by means of the evaluation of WSN node networks and their performance when both clustering and antenna beamforming techniques are applied. In this work, four different scenarios are defined, each one considering different numbers of sensors: 50, 20, 10, five, and two nodes per scenario, and each scenario is randomly generated thirty times in order to statistically validate the results. For each experiment, two different target directions for transmission are taken into consideration in the optimization process (ɸ = 0º and Ɵ = 45º; ɸ = 45º, and Ɵ = 45º). Each scenario is evaluated for two different types of antennas, an ideal isotropic antenna and a conventional dipole one. In this set of experiments two types of WSN are evaluated: in the first one, all of the sensors have the same amount of power for communications purposes; in the second one, each sensor has a different amount of power for its communications purposes. The analyzed cases in this document are focused on 2D surface and 3D space for the node location. To the authors’ knowledge, this is the first time that beamforming and clustering are simultaneously applied to increase the network lifetime in WSNs.Gobierno de Extremadura y Fondos FEDER: Proyecto IB13113peerReviewe

Portable System for Real-Time Detection of Stress Level

Currently, mental stress is a major problem in our society. It is related to a wide variety of diseases and is mainly caused by daily-life factors. The use of mobile technology for healthcare purposes has dramatically increased during the last few years. In particular, for out-of-lab stress detection, a considerable number of biosignal-based methods and systems have been proposed. However, these approaches have not matured yet into applications that are reliable and useful enough to significantly improve people’s quality of life. Further research is needed. In this paper, we propose a portable system for real-time detection of stress based on multiple biosignals such as electroencephalography, electrocardiography, electromyography, and galvanic skin response. In order to validate our system, we conducted a study using a previously published and well-established methodology. In our study, ten subjects were stressed and then relaxed while their biosignals were simultaneously recorded with the portable system. The results show that our system can classify three levels of stress (stress, relax, and neutral) with a resolution of a few seconds and 86% accuracy. This suggests that the proposed system could have a relevant impact on people’s lives. It can be used to prevent stress episodes in many situations of everyday life such as work, school, and home.This research was funded by [Ministry of Economy and Competitiveness (Spain)] grant number [TIN2015-67020P], [Ministry of Economy and Competitiveness (Spain)] grant number [DPI2015-69098-REDT], [Junta of Andalucia (Spain)] grant number [P11-TIC-7983], [Spanish National Youth Guarantee Implementation Plan] grant number [Research contract], [Nicolo Association for the R+D in neurotechnologies for disability] grant number [Research support], and [Orden Hospitalaria San Juan de Dios] grant number [Beca investigacion]