Search

Abstract 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.

Abstract INTRODUCTION Accumulation and interaction of amyloid‐beta (Aβ) and tau proteins during progression of Alzheimer's disease (AD) are shown to tilt neuronal circuits away from balanced excitation/inhibition (E/I). Current available techniques for noninvasive interrogation of E/I in the intact human brain, for example, magnetic resonance spectroscopy (MRS), are highly restrictive (i.e., limited spatial extent), have low temporal and spatial resolution and suffer from the limited ability to distinguish accurately between different neurotransmitters complicating its interpretation. As such, these methods alone offer an incomplete explanation of E/I. Recently, the aperiodic component of neural power spectrum, often referred to in the literature as the ‘1/f slope’, has been described as a promising and scalable biomarker that can track disruptions in E/I potentially underlying a spectrum of clinical conditions, such as autism, schizophrenia, or epilepsy, as well as developmental E/I changes as seen in aging. METHODS Using 1/f slopes from resting‐state spectral data and computational modeling, we developed a new method for inferring E/I alterations in AD. RESULTS We tested our method on recent freely and publicly available electroencephalography (EEG) and magnetoencephalography (MEG) datasets of patients with AD or prodromal disease and demonstrated the method's potential for uncovering regional patterns of abnormal excitatory and inhibitory parameters. DISCUSSION Our results provide a general framework for investigating circuit‐level disorders in AD and developing therapeutic interventions that aim to restore the balance between excitation and inhibition.

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.

Recent studies have addressed stress level classification via electroencephalography (EEG) and machine learning. These works typically use EEG-based features, like power spectral density (PSD), to develop stress classifiers. Nonetheless, these classifiers are usually limited to the discrimination of two (stress and no stress) or three (low, medium, and high) stress levels. In this study we propose an alternative for quantitative stress assessment based on EEG and regression algorithms. To this aim, we conducted a group of 23 participants (mean age 22.65 ± 5.48) over a stress-relax experience while monitoring their EEG. First, we stressed the participants via the Montreal imaging stress task (MIST), and then we led them through a 360-degree virtual reality (VR) relaxation experience. Throughout the session, the participants reported their self-perceived stress level (SPSL) via surveys. Subsequently, we extracted spectral features from the EEG of the participants and we developed individual models based on regression algorithms to predict their SPSL. We evaluated stress regression performance in terms of the mean squared percentage error (MSPE) and the correlation coefficient (R2). The results yielded from this evaluation (MSPE = 10.62 ± 2.12, R2 = 0.92 ± 0.02) suggest that our approach predicted the stress level of the participants with remarkable performance. These results may have a positive impact in diverse areas that could benefit from stress level quantitative prediction. These areas include research fields like neuromarketing, and training of professionals such as surgeons, industrial workers, or firefighters, that often face stressful situations.

This study explored the use of artificial neural networks in the estimation of runners' kinetics from lower body kinematics. Three supervised feed-forward artificial neural networks with one hidden layer each were modelled and assigned individually with the mapping of a single force component. Number of training epochs, batch size and dropout rate were treated as modelling hyper-parameters and their values were optimised with a grid search. A public data set of twenty-eight professional athletes containing running trails of different speeds (2.5 m/sec, 3.5 m/sec and 4.5 m/sec) was employed to train and validate the networks. Movements of the lower limbs were captured with twelve motion capture cameras and an instrumented dual-belt treadmill. The acceleration of the shanks was fed to the artificial neural networks and the estimated forces were compared to the kinetic recordings of the instrumented treadmill. Root mean square error was used to evaluate the performance of the models. Predictions were accompanied with low errors: 0.134 BW for the vertical, 0.041 BW for the anteroposterior and 0.042 BW for the mediolateral component of the force. Vertical and anteroposterior estimates were independent of running speed (p=0.233 and p=.058, respectively), while mediolateral results were significantly more accurate for low running speeds (p=0.010). The maximum force mean error between measured and estimated values was found during the vertical active peak (0.114 ± 0.088 BW). Findings indicate that artificial neural networks in conjunction with accelerometry may be used to compute three-dimensional ground reaction forces in running.

Athletic performance, technique assessment, and injury prevention are all important aspects in sports for both professional and amateur athletes. Wearable technology is attracting the research community’s interest because of its capability to provide real-time biofeedback to coaches and athletes when on the field and outside of more restrictive laboratory conditions. In this paper, a novel wearable motion sensor-based system has been designed and developed for athletic performance assessment during running and jumping tasks. The system consists of a number of components involving embedded systems (hardware and software), back-end analytics, information and communications technology (ICT) platforms, and a graphical user interface for data visualization by the coach. The system is able to provide automatic activity recognition, estimation of running and jumping metrics, as well as vertical ground reaction force (GRF) predictions, with sufficient accuracy to provide valuable information as regards training outcomes. The developed system is low-power, sufficiently small for real-world scenarios, easy to use, and achieves the specified communication range. The system’s high sampling rate, levels of accuracy and performance enables it as a performance evaluation tool able to support coaches and athletes in their real-world practice.

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.

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 self- perceived stress level at different stages of the experiment and it yielded no significant differences in reducing stress for both groups, during relaxing (p-value: 0.8379, α = 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.