Erik Bojorges-Valdez

Introduction: Respiration is increasingly recognized as a key modulator of brain-body interactions, influencing both neural and autonomic dynamics. While prior work has demonstrated respiratory-driven entrainment, it remains unclear whether distinct phases of the breathing cycle give rise to different dynamical regimes of cortical activity. Methods: In this study, we investigated respiratory-phase-dependent modulation of brain-heart interactions during controlled breathing, focusing on breath-hold periods following inhalation and exhalation. EEG band power time series and heart rate (HR) were analyzed in 15 healthy subjects. In addition to conventional spectral measures, recurrence quantification analysis was employed to characterize non-linear dynamics in cortical activity. Results: Results revealed clear respiratory-phase-dependent differences. Inhale-hold was associated with increased γ band power, higher recurrence rate, and elevated HR, whereas exhale-hold showed reduced γ activity, lower recurrence, and decreased HR. These findings are consistent with known mechanisms of respiratory sinus arrhythmia and suggest that the two conditions correspond to distinct autonomic states. From a dynamical systems perspective, increased recurrence during inhale-hold suggests more structured and constrained cortical dynamics, while reduced recurrence during exhale-hold reflects comparatively more variable activity. Discussion: Together, these findings suggest that respiratory phase modulates the organization of brain-body dynamics, potentially reflecting shifts between more stable and more flexible regimes of neural activity. This phase-dependent organization supports the view that respiration may act as a physiological mechanism for dynamically tuning brain-heart interactions, with implications for understanding the role of breathing in the modulation of neural dynamics.

Introduction: The mechanisms by which conscious breathing influences brain-body signaling remain largely unexplored. Understanding how controlled breathing modulates neural and autonomic activity can offer insights into self-regulation and adaptive physiological control. This study investigates how conscious breathing affects cortical-autonomic communication by analyzing bidirectional interactions between EEG band power time series (BPts), heart rate variability (HRV), and breathing signals.Methods: Data were collected from fifteen healthy subjects during three experimental conditions: a spontaneous breathing state (Rest) and two controlled breathing tasks (CBT 1 and CBT 2). EEG recordings were analyzed to compute BPts across the δ, θ, α, β, and γ frequency bands, while HRV and breathing signals were derived from ECG data. Cross-spectrum analysis and Granger causality tests were performed between HRV and BPts. To further investigate directional interactions, Granger-causal relationships were explored between components of the BPts extracted using empirical mode decomposition and the HRV and breathing signals.Results: Bidirectional Granger-causal relationships were found between neural and autonomic systems, emphasizing the dynamic interaction between the brain and body. Specific BPts components mediated neural-autonomic communication, with one component consistently aligning with the frequency of conscious breathing (~0.05 Hz) during the CBTs. Cross-spectral peaks at this frequency and its harmonics highlight the role of respiratory entrainment in optimizing neuro-autonomic synchronization. Frequency-specific mechanisms observed in both fast and slow components reflect the complex regulation of autonomic functions through cortical modulation. The most prominent causal effects were observed in the γ band, suggesting its pivotal role in dynamic autonomic regulation, potentially acting as a communication pathway between the brain and body.Discussion: Our results demonstrate that conscious breathing enhances bidirectional cortical-autonomic modulation through frequency-specific dynamic neural mechanisms. These findings support a closed-loop model of physiological regulation driven by neural-respiratory entrainment and suggest that respiration can serve as a top-down mechanism for autonomic control. By clarifying how conscious breathing shapes brain-body dynamics, this work lays the foundation for research on neural self-regulation and supports the development of non-pharmacological interventions for improving mental and physiological health.

Transcorneal electrical stimulation (TES) is a promising treatment for several retinal degenerative diseases (RDDs). TES involves the application of a controlled electrical current to the anterior surface of the cornea, aimed at activating the retina and posterior ocular structures. Dawson–Trick–Litzkow (DTL) and ERG-JET electrodes are among the most widely used for TES. However, their continuous metallic surface design limits spatial resolution and the ability to perform selective ES. In this work, we present the development of a transcorneal electrical stimulation (TES) electrode that, unlike conventional electrodes, enables spatially selective TES. The proposed electrode design consists of an array of 20 independent microelectrodes distributed across the central and paracentral regions of the cornea. The fabrication process combines surface micromachining and flexible electronics technologies, employing only three structural materials: aluminum (Al), titanium (Ti), and polyimide (PI). This material selection is critical for achieving a simplified, reproducible, and low-cost fabrication process. The fabricated electrode was validated through electrical and electrochemical testing. The results show a relatively high electrical conductivity of Al/Ti structures, low electrochemical impedance values—ranging from 791 kΩ to 1.75 MΩ for the clinically relevant frequency range (11 to 30 Hz)—and a high charge storage capacity of 1437 mC/cm². The electrode capacity for electrical signal transmission was demonstrated through in vitro testing. Finally, the applicability of the TES electrode for electroretinogram (ERG) recording was evaluated by measuring its optical transmittance across the visible wavelength range.

Mobility is essential for individuals with physical disabilities, and wheelchairs significantly enhance their quality of life. Recent advancements focus on developing sophisticated control systems for effective and efficient interaction. This study evaluates the usability and performance of three wheelchair control modes manual, automatic, and voice controlled using a virtual reality (VR) simulation tool. VR provides a controlled and repeatable environment to assess navigation performance and motion sickness across three scenarios: supermarket, museum, and city. Twenty participants completed nine tests each, resulting in 180 trials. Findings revealed significant differences in navigation efficiency, distance, and collision rates across control modes and scenarios. Automatic control consistently achieved faster navigation times and fewer collisions, particularly in the supermarket. Manual control offered precision but required greater user effort. Voice control, while intuitive, resulted in longer distances traveled and higher collision rates in complex scenarios like the city. Motion sickness levels varied across scenarios, with higher discomfort reported in the city during voice and automatic control. Participant feedback, gathered via a Likert scale questionnaire, highlighted the potential of VR simulation for evaluating user comfort and performance. This research underscores the advantages of VR-based testing for rapid prototyping and user-centered design, offering valuable insights into improving wheelchair control systems. Future work will explore adaptive algorithms to enhance usability and accessibility in real world applications.

Machine learning (ML) algorithms are valuable for various clinical imaging tasks, including organ and lesion segmentation and diagnostic support. This paper suggests a versatile system architecture that follows a standard pipeline of capture, processing, and display and a manager that evaluates input image and performs user-selected tasks over it. This architecture can perform three distinct tasks: detecting specific images in a scene, segmenting based on imaging modality, and classifying images related to pathology. The system was evaluated using Dice index (DI) in segmentation tasks such as lung from CT and transmission radiography (DI: 0.956 ± 0.015, DI: 0.8013 ± 0.277 respectively), heart from CT (DI: 0.8396 ± 0.2188), and optic disc from slit lamps (DI: 0.9134 ± 0.079). In diagnostic tasks, accuracy was evaluated for five-class fundus pathology classification (0.854 ± 0.015) and six class thoracic radiography classification (0.972 ± 0.0069). The real time operation of the system depends on the active mode: detection and segmentation are performed at 23 frames per second (fps), detection and diagnostic 3 fps, all modes at once 2 fps. These values show the viability of the architecture presented here as proof of concept.Clinical Relevance: The presented project aims to build a versatile visualization assistance system for medical staff, that allows three operation modes by an easy change of internal ML model.

Mobility is a fundamental necessity for individuals with physical disabilities, and wheelchairs play a vital role in improving their quality of life. In recent decades, there has been a growing interest in designing and developing more advanced wheelchairs, particularly regarding control systems that enable users to interact effectively and efficiently with their chairs. Implementing different control systems in wheelchairs has been the subject of research and development to enhance user experience and overcome limitations associated with conventional controls. Virtual reality (VR) is a valuable tool for testing new technologies, ensuring user safety, and reducing development time. This article aims to present a comparative evaluation of various controls implemented in a simulated wheelchair, analyzing their effectiveness and ease of use through navigation in three different daily-life scenarios. Twenty participants(11 males and 9 females) received VR operation and controls training. Each participant completed three tests per control in each scenario. The results showed that participants performed better while navigating with autonomous and manual controls than with voice controls. However, participants experienced associated motion sickness during the interaction. Contrary to voice control, despite causing less motion sickness, the participants' performance shows that a high voice command recognition rate and reliability are mandatory to be a viable option. Our setup represents a useful experimental setup for future research and improvements in developing wheelchair control systems in virtual environments to cater to the individual needs and abilities of end-users in reality.

The electric stimulation (ES) of the cornea is a novel therapeutic approach to the treatment of degenerative visual diseases. Currently, ES is delivered by placing a mono-element electrode on the surface of the cornea that uniformly stimulates the eye along the electrode site. It has been reported that a certain degree of correlation exists between the location of the stimulated retinal area and the position of the electrode. Therefore, in this study, we present the development of a sectioned surface electrode for selective electric stimulation of the human cornea. The proposed device consists of 16 independent microelectrodes, a reference electrode, and 18 contact pads. The microelectrodes have a size of 200 µm × 200 µm, are arranged in a 4 × 4 matrix, and cover a total stimulation area of 16 mm². The proposed fabrication process, based on surface micromachining technology and flexible electronics, uses only three materials: polyimide, aluminum, and titanium, which allow us to obtain a simplified, ergonomic, and reproducible fabrication process. The fabricated prototype was validated to laboratory level by electrical and electrochemical tests, showing a relatively high electrical conductivity and average impedance from 712 kΩ to 1.4 MΩ at the clinically relevant frequency range (from 11 Hz to 30 Hz). Additionally, the biocompatibility of the electrode prototype was demonstrated by performing in vivo tests and by analyzing the polyimide films using Fourier transform infrared spectroscopy (FTIR). The resulting electrode prototype is robust, mechanically flexible, and biocompatible, with a high potential to be used for selective ES of the cornea.

The identification of brain dynamical changes under different cognitive conditions with noninvasive techniques such as electroencephalography (EEG) is relevant for the understanding of their underlying neural mechanisms. The comprehension of these mechanisms has applications in the early diagnosis of neurological disorders and asynchronous brain computer interfaces. In both cases, there are no reported features that could describe intersubject and intra subject dynamics behavior accurately enough to be applied on a daily basis. The present work proposes the use of three nonlinear features (recurrence rate, determinism, and recurrence times) extracted from recurrence quantification analysis (RQA) to describe central and parietal EEG power series complexity in continuous alternating episodes of mental calculation and rest state. Our results demonstrate a consistent mean directional change of determinism, recurrence rate, and recurrence times between conditions. Increasing values of determinism and recurrence rate were present from the rest state to mental calculation, whereas recurrence times showed the opposite pattern. The analyzed features in the present study showed statistically significant changes between rest and mental calculation states in both individual and population analysis. In general, our study described mental calculation EEG power series as less complex systems in comparison to the rest state. Moreover, ANOVA showed stability of RQA features along time.

Most methods for sudden cardiac death (SCD) prediction require long-term (24 h) electrocardiogram recordings to measure heart rate variability (HRV) indices or premature ventricular complex indices (with the heartprint method). This work aimed to identify the best combinations of HRV and heartprint indices for predicting SCD based on short-term recordings (1000 heartbeats) through a support vector machine (SVM). Eleven HRV indices and five heartprint indices were measured in 135 pairs of recordings (one before an SCD episode and another without SCD as control). SVMs (defined with a radial basis function kernel with hyperparameter optimization) were trained with this dataset to identify the 13 best combinations of indices systematically. Through 10-fold cross-validation, the best area under the curve (AUC) value as a function of γ (gamma) and cost was identified. The predictive value of the identified combinations had AUCs between 0.80 and 0.86 and accuracies between 80 and 86%. Further SVM performance tests on a different dataset of 68 recordings (33 before SCD and 35 as control) showed AUC = 0.68 and accuracy = 67% for the best combination. The developed SVM may be useful for preventing imminent SCD through early warning based on electrocardiogram (ECG) or heart rate monitoring.