Biology subjects

Moscoso-Barrera, W. D.

Publications and source records attributed to Moscoso-Barrera, W. D..

4 recordsLinked to original sources

bioRxiv2025

Wearable Focused Ultrasound Neuromodulation and Electrophysiological Recording Patch for REM Sleep Enhancement

The rise in sleep disease affecting the general population globally in the past decade has been detrimental to individually and socioeconomically. As of now, approaches often are temporary through medication, permanently using invasive implants with surgical complications or neuromodulation therapy. However, non-invasive, state-dependent neuromodulation during sleep is technically challenging, especially with the lack of flexibility, comfortability and robustness for sleep conditions. Here, we introduce a Non-invasive Electrophysiological Recording and Ultrasound Neuromodulation Sleep Patch (NEUSLeeP) in delivering focused ultrasound stimulation to the subthalamic nucleus (STN) overnight with simultaneous stable polysomnography recording. Our sleep patch integrates a custom eight-channel concentric ring transducer array with real-time electroencephalography (EEG), electrooculography (EOG), electromyography (EMG) recording, and individualized line-of-sight targeting to sonicate deep brain areas while preserving mobility. Our platform operated safely and comfortably across the two-nights sleep study. Stimulation of the left STN was delivered every 90 minutes throughout the night and was associated with a 25% increase in REM (Rapid Eye Movement) sleep duration and a 43 minutes reduction in REM sleep latency compared to a sham night in a study of 26 subjects. Blood Oxygen Level Dependent (BOLD) signal attenuation in functional Magnetic Resonance Imaging (fMRI) was localized primarily to a left ipsilateral basal-ganglia-midbrain-temporal circuit, consistent with selective network modulation rather than global arousal changes. Overall, NEUSLeeP demonstrates feasibility by (i) a light weight and wearable ultrasound neuromodulation and sleep recording during natural sleep; (ii) establishing a potential mechanism relating targeted ultrasound stimulation of STN/sleep networks to REM enhancement.

bioengineering↗

bioRxiv2025

Postprocessing-Enhanced Machine Learning for Reliable Real-Time Sleep Staging in Closed-Loop Neuromodulation

Real-time sleep stage classification is important for closed-loop neuromodulation at certain stages during sleep, yet current models often yield noisy and unstable outputs that risk false triggers. These fluctuations, especially near stage boundaries, can compromise the safety and reliability of stimulation. Existing methods frequently rely on model-specific architectures or require extensive tuning to maximize precision, which limits their generalizability and hinders deployment in real-time systems. To address this, we propose a lightweight, classifier-independent post-processing pipeline that stabilizes predictions without modifying the underlying classifier. Our method first applies temporal smoothing to predicted sleep-stage probabilities, followed by conservative control logic to enhance stimulation precision. We evaluate four smoothing techniques: Moving Average (MA), Exponential Smoothing (ES), Kalman Filtering (KF), and a novel Weighted Exponential Smoothing (WES), on a 29-subject open-source sleep dataset. To capture the trade-offs between stability and responsiveness, we introduce new real-time evaluation metrics. We identify an optimal smoothing intensity range and assess three control strategies: probability thresholding, naive waiting, and entropy constraint, as well as a hybrid method combining high confidence and low uncertainty. Smoothing improves generalization and robustness: under both low and high synthetic noise ({sigma} > 0.2), smoothed outputs retain 10-15% higher precision and recall across all stages. Control logic further enhances reliability: our hybrid method achieves 80% (Wake), 96% (N2), 98% (N3), and 86% (REM) precision. Finally, we introduce a stage-transition constraint matrix to suppress biologically implausible transitions (e.g., REM[->] N3), to further stabilize outputs during evidence accumulation, with potential applications in objective sleep quality assessment. To our knowledge, this is the first study to systematically characterize real-time trade-offs between smoothing, latency, and control logic in sleep staging. Overall, our generalizable framework is expected to improve safety, interpretability, and deployment feasibility of real-time sleep classification for both clinical and wearable closed-loop neuromodulation systems.

bioengineering↗

bioRxiv2025

Stretchable, hair-compatible, and long-term stable wearable EEG system

Electroencephalography (EEG) is a cornerstone in both neuroscience research and clinical diagnostics. However, conventional EEG monitoring faces hardware limitations, particularly its adaptability and stability. Headsets either require complicated wiring or do not have enough stretchability and wearability to comply with the diverse head anthropometry and hair conditions of the user population. Additionally, there is an inherent tradeoff between wet and dry electrodes in capturing high-fidelity signals from hair-covered scalp regions while ensuring continuous and long-term recording quality. Here, we present a Mesh-integrated, Stretchable, and Hair-compatible EEG system engineered to overcome these limitations. By incorporating a kirigami-inspired mesh design and stretchable eutectic Gallium-Indium interconnects, MindStretcH adapts to various head sizes and allows for easy wearing and removal. Moreover, its uniquely designed porous, conical, soft 3D-printed mold, embedded with hydrogel electrodes, effectively penetrates hair layers to deliver low impedance and sustained signal integrity with minimal discomfort. We validate MindStretcH through offline and online EEG-based brain-computer interface tasks over a month, demonstrating its exceptional stability in continuous monitoring and dynamic applications. These results mark a promising advance toward non-invasive neural interfaces in both clinical and everyday use.

bioengineering↗

bioRxiv2024

Bioadhesive Hydrogel-Coupled and Miniaturized Ultrasound Transducer System for Long-Term, Wearable Neuromodulation

Transcranial focused ultrasound has become a promising non-invasive approach for neuromodulation applications, particularly for neurodegenerative diseases and psychiatric illnesses. However, its implementation in wearable neuromodulation has thus far been limited due to the devices large size, which needs external supporting systems for the neuromodulation process. Furthermore, the need for ultrasound gel for acoustic coupling between the device and skin limits the viability for long-term use, due to its inherent susceptibility to dehydration and lack of adhesiveness to form a stable interface. Here, we report a wearable miniaturized ultrasound device with size comparable to standard EEG/ECG electrodes integrated with bioadhesive hydrogel to achieve efficient acoustic intensity upon ultrasound stimulation for long-term, wearable primary somatosensory cortical stimulation. Specifically, air-cavity Fresnel lens (ACFAL) based self-focusing acoustic transducer (SFAT) was fabricated using a lithography-free microfabrication process. Our transducer was able to achieve an acoustic intensity of up to 30.7 W/cm2 (1.92 MPa) in free-field with a focal depth of 10 mm. Bioadhesive hydrogel was developed to address the need for long-term stability of acoustic couplant for ultrasound application. The hydrogel demonstrated less than 13% attenuation in acoustic intensity and stable adhesion force of 0.961 N/cm over 35 days. Leveraging our bioadhesive hydrogel-integrated wearable ultrasound transducer, we were able to suppress somatosensory evoked potentials elicited by median nerve stimulation via functional electrical stimulation over 28 days, demonstrating the efficacy of our transducer for long-term, wearable neuromodulation in the brain.

bioengineering↗