Use of Serious Games in Interventions of Executive Functions in Neurodiverse Children: Systematic Review.

Background: Serious games (SG) have emerged as promising tools for cognitive training and therapeutic interventions, especially for enhancing executive functions. These games have demonstrated the potential to support individuals with diverse health conditions, including neurodevelopmental and cognitive disorders, through engaging and interactive experiences. However, a comprehensive understanding of the effectiveness of SG in enhancing executive functions is needed.

Machine Learning Applied to Reference Signal-Less Detection of Motion Artifacts in Photoplethysmographic Signals: A Review.

Machine learning algorithms have brought remarkable advancements in detecting motion artifacts (MAs) from the photoplethysmogram (PPG) with no measured or synthetic reference data. However, no study has provided a synthesis of these methods, let alone an in-depth discussion to aid in deciding which one is more suitable for a specific purpose. This narrative review examines the application of machine learning techniques for the reference signal-less detection of MAs in PPG signals.

Psychiatric disorders in post-traumatic brain injury patients: A scoping review.

Background: Traumatic Brain Injury (TBI) is an important antecedent in the evaluation of patients with psychiatric disorders. The association between TBI and the subsequent appearance of psychiatric disorders has been documented, however, the findings found in the literature are diverse and controversial.

Objective: To identify the most prevalent psychiatric disorders after head trauma.

Depressive symptomatology in adults during the COVID-19 pandemic.

Depression entails changes in the mental health of individuals worldwide. Episodes of depression lead to mood swings and changes in the motivational dimension. Our research focused on the prevalence of depression in the adult population and on how it affected the social and affective dimensions.

Noninvasive Prototype for Type 2 Diabetes Detection.

The present work demonstrates the design and implementation of a human-safe, portable, noninvasive device capable of predicting type 2 diabetes, using electrical bioimpedance and biometric features to train an artificial learning machine using an active learning algorithm based on population selection. In addition, there is an API with a graphical interface that allows the prediction and storage of data when the characteristics of the person are sent. The results obtained show an accuracy higher than 90% with statistical significance ( < 0.

A SSVEP-BCI Setup Based on Depth-of-Field.

In optical systems, the range of distance near the point of focus where objects are perceived sharply is referred as depth-of-field; objects outside this region are defocused and blurred. Furthermore, ophthalmology studies state that the amplitude and the latency of visual evoked potentials are affected by defocusing. In this context, this paper evaluates a novel setup for a steady-state visual evoked potential (SSVEP) brain-computer interface, in which two stimuli are presented together in the center of the user's field of view but at different distances ensuring that if one stimulus is focused on, the other one is non-focused, and vice versa.

Adaptive BCI based on software agents.

The selection of features is generally the most difficult field to model in BCIs. Therefore, time and effort are invested in individual feature selection prior to data set training. Another great difficulty regarding the model of the BCI topology is the brain signal variability between users.

Prevalence and characterization of extended-spectrum beta-lactamases-producing Salmonella enterica isolates in Saragossa, Spain (2001-2008).

We analyzed the prevalence of resistance to extended-spectrum cephalosporins (ESCs) among clinical strains of Salmonella enterica collected by the Laboratory of Clinical Microbiology in the University Clinical Hospital Lozano Blesa in the region of Aragón (Spain), for which very few epidemiological information exists. A total of 2,092 strains of S. enterica were identified in stool samples from patients with gastroenteritis.