A new pandemic was declared at the end of 2019 because of coronavirus disease 2019 (COVID-19). One of the effects of COVID-19 infection is anosmia (i.e., a loss of smell). Unfortunately, this olfactory dysfunction is persistent in around 5% of the world’s population, and there is not an effective treatment for it yet. The aim of this paper is to describe a potential non-invasive neurostimulation strategy for treating persistent anosmia in post-COVID-19 patients. In order to design the neurostimulation strategy, 25 subjects who experienced anosmia due to COVID-19 infection underwent an olfactory assessment while their electroencephalographic (EEG) signals were recorded. These signals were used to investigate the activation of brain regions during the olfactory process and identify which regions would be suitable for neurostimulation. Afterwards, 15 subjects participated in the evaluation of the neurostimulation strategy, which was based on applying transcranial direct current stimulation (tDCS) in selected brain regions related to olfactory function. The results showed that subjects with lower scores in the olfactory assessment obtained greater improvement than the other subjects. Thus, tDCS could be a promising option for people who have not fully regained their sense of smell following COVID-19 infection.

The non-invasive recording of spinal cord neuronal activity, also known as electrospinography (ESG), using high-density surface electromyography (HD-sEMG) is a promising emerging biosensing modality. However, these recordings often contain electrocardiographic (ECG) artifacts that must be removed for accurate analysis. Given the emerging nature of ESG and the lack of dedicated signal processing methods, this study assesses the performance of seven established EMG denoising algorithms for their ability to preserve the broad spectral bandwidth needed for future ESG characterization: Template Subtraction (TS), Adaptive Template Subtraction (ATS), High-Pass Filtering at 200 Hz (HP200), ATS combined with HP200, Second-Order Extended Kalman Smoother (EKS2), Stationary Wavelet Transform (SWT), and Empirical Mode Decomposition (EMD). Performance was quantified using six metrics: Relative Error (RE), Signal-to-Noise Ratio (SNR), Cross-Correlation (CC), Spectral Distortion (SD), and Kurtosis Ratio (KR2) and its variation ($\Delta$KR2). ESG data were recorded from nine healthy participants at brachial and lumbar plexus sites with various electrode configurations. ATS consistently outperformed all other methods in suppressing cardiac artifacts of varying shapes. Although it did not fully preserve low-frequency content, ATS achieved the best balance between artifact removal and signal integrity. Algorithm performance improved when ECG contamination was lower, especially in brachial plexus recordings with closer reference electrodes.

El estudio aborda la necesidad de cuantificar la presión superficial con baja latencia en la transmisión de datos. Utilizando el sensor de fuerza resistivo FSR 406 y el microcontrolador ESP32, se ha desarrollado una arquitectura para optimizar el envío y registro de datos. La arquitectura incluye la activación de triggers basados en umbrales de voltaje o botones pulsadores, permitiendo la sincronización precisa entre dispositivos. El sistema envía datos en vectores o bloques de cadenas, asegurando la integridad y continuidad de la información. También, ejemplos de envío de triggers y datos muestran cómo se logra una sincronización efectiva, facilitando la comparación de la presión superficial con otras señales fisiológicas sin generar desfases. Este estudio optimiza el registro y transmisión de datos, mejorando la precisión y eficiencia en aplicaciones fisiológicas y biomecánicas y, demostrando una arquitectura robusta para la sincronización y análisis de múltiples dispositivos.

This study presents a dataset of electrospinography (ESG) signals recorded from the human spinal cord during gait-related activities and motor imagery tasks. The dataset was acquired as part of a broader initiative to develop a spinal-machine interface (SMI) for closed-loop control of lower limb exoskeletons. ESG signals were collected using high-density surface electromyography (HD-sEMG) electrodes from fourteen able-bodied participants performing baseline trials (2), movement execution tasks (12) and motor imagery tasks conducted both in static conditions (5) and during movement (5). The dataset encompasses multiple electrode configurations targeting the brachial and lumbar plexuses, as well as surrounding musculature, across three experimental protocols. A total of 10 sessions were recorded for Experiment 1 (one 64-electrode matrix), 10 sessions for Experiment 2 (two 32-electrode matrices) and 5 sessions for Experiment 3 (two-32 electrode matrices). Preprocessing techniques were applied to mitigate cardiac and motion artifacts. The data provides a valuable and pionneering resource for advancing neurorehabilitation research, allowing the refining of exoskeleton control strategies and improving artifact removal methods.