Background: Studies have shown a decline in hospitalizations due to acute exacerbation of chronic obstructive pulmonary disease (AECOPD) during the coronavirus disease 2019 (COVID-19) pandemic. However, the impact of the pandemic on AECOPDs of all severities in longitudinal cohorts of patients is lacking.

Virtual reality (VR) enables the development of virtual training frameworks suitable for various domains, especially when real-world conditions may be hazardous or impossible to replicate because of unique additional resources (e.g., equipment, infrastructure, people, locations). Although VR technology has significantly advanced in recent years, methods for evaluating immersion (i.e., the extent to which the user is engaged with the sensory information from the virtual environment or is invested in the intended task) continue to rely on self-reported questionnaires, which are often administered after using the virtual scenario. Having an objective method to measure immersion is particularly important when using VR for training, education, and applications that promote the development, fine-tuning, or maintenance of skills. The level of immersion may impact performance and the translation of knowledge and skills to the real-world. This is particularly important in tasks where motor skills are combined with complex decision making, such as surgical procedures. Efforts to better measure immersion have included the use of physiological measurements including heart rate and skin response, but so far they do not offer robust metrics that provide the sensitivity to discriminate different states (idle, easy, and hard), which is critical when using VR for training to determine how successful the training is in engaging the user’s senses and challenging their cognitive capabilities. In this study, electroencephalography (EEG) data were collected from 14 participants who completed VR jigsaw puzzles with two different levels of task difficulty. Machine learning was able to accurately classify the EEG data collected during three different states, obtaining accuracy rates of 86% and 97% for differentiating easy versus hard difficulty states and baseline vs. VR states. Building on these results may enable the identification of robust biomarkers of immersion in VR, enabling real-time recognition of the level of immersion that can be used to design more effective and translative VR-based training. This method has the potential to adjust aspects of VR related to task difficulty to ensure that participants are immersed in VR.

Artificial Intelligence (AI), computer simulations, and virtual reality (VR) are increasingly becoming accessible tools that can be leveraged to implement training protocols and educational resources. Typical assessment tools related to sensory and neural processing associated with task performance in virtual environments often rely on self-reported surveys, unlike electroencephalography (EEG), which is often used to compare the effects of different types of sensory feedback (e.g., auditory, visual, and haptic) in simulation environments in an objective manner. However, it can be challenging to know which aspects of the EEG signal represent the impact of different types of sensory feedback on neural processing. Machine learning approaches offer a promising direction for identifying EEG signal features that differentiate the impact of different types of sensory feedback during simulation training. For the current study, machine learning techniques were applied to differentiate neural circuitry associated with haptic and non-haptic feedback in a simulated drilling task. Nine EEG channels were selected and analyzed, extracting different time-domain, frequency-domain, and nonlinear features, where 360 features were tested (40 features per channel). A feature selection stage identified the most relevant features, including the Hurst exponent of 13–21 Hz, kurtosis of 21–30 Hz, power spectral density of 21–30 Hz, variance of 21–30 Hz, and spectral entropy of 13–21 Hz. Using those five features, trials with haptic feedback were correctly identified from those without haptic feedback with an accuracy exceeding 90%, increasing to 99% when using 10 features. These results show promise for the future application of machine learning approaches to predict the impact of haptic feedback on neural processing during VR protocols involving drilling tasks, which can inform future applications of VR and simulation for occupational skill acquisition.

Aunque no hay un consenso científico sobre la definición de las emociones, estas se pueden entender como las respuestas psicofisiológicas a un estímulo interno o externo, donde se dan un grupo de interacciones entre factores objetivos y subjetivos que son manejados por sistemas hormonales y neuronales. Esto puede conducir a comportamientos que en algunos casos son expresivos. Ahora, como el sistema neuronal se ve inmerso en la regulación de estas, algunas de las regiones directamente relacionadas son; la corteza cingulada anterior subgenual con depresión y rumia, las cortezas orbitofrontal y cingulada con procesamiento de recompensa. Incluso, otras zonas como las cortezas sensoriales primarias se pueden ver afectadas indirectamente. Teniendo en cuenta estas zonas de actividad cerebral, es posible hacer uso del EEG para un estudio profundo del comportamiento de estas bajo situaciones emocionales. En este contexto, el presente artículo propone integrar una serie de técnicas de procesamiento digital de señales, aplicadas a EEG. Para esto se hace uso de la base de datos DEAP, la cual consta de 32 registros, donde cada uno de estos cuenta con 48 canales. Dichos registros contienen la grabación del antes, durante y después de la inducción de un estado emocional. El procesamiento y extracción de características de dichas señales se planteó a partir de un análisis por ventanas de tiempo de 5 segundos. Posteriormente se calcularon parámetros no lineales como la entropía, la complejidad y la movilidad (parámetros de Hjörth), que permiten caracterizar la señal. Ahora bien, la integración de los parámetros planteados permitió definir cuantitativamente cuándo ocurre la inducción de una emoción y cómo evoluciona su respuesta en EEG, teniendo en cuenta una línea base (estado neutral), un instante de presentación de estímulo y finalmente la manifestación fisiológica de la emoción. Se considera que la metodología planteada en el presente artículo es novedosa, en tanto a que propone un análisis de la evolución de las características del EEG en el contexto de la inducción de emociones. Así mismo, se pudo determinar que los métodos aquí descritos pueden constituir una herramienta viable para ser usada en el desarrollo de estudios que involucren procesos emocionales. Se pretende a futuro llevar a cabo un protocolo experimental que permita determinar las transiciones fisiológicas, cuando hay inducción de emociones, en una muestra más amplia.