electrophysiology

This study identifies distinct subpopulations of lateral habenula neurons and shows they respond differently to reward-related stimuli during behavior.

The paper estimates electrostatic attraction during fingertip sliding on a touchscreen by measuring contact area and impedance under varying normal force and speed.

This study identifies a frontal midline theta EEG signature in dogs during a delayed gratification task, similar to human cognitive control.

This paper develops a closed-loop vibrotactile stimulation system to reduce tremor amplitude and propranolol use by modeling a nonmonotonic tremor-vibration relationship.

This paper develops a coupled Maxwell-electromagnetic cable framework to model action potential propagation in branched axons, showing electromagnetic effects significantly impact conduction and bifurcation transmission.

This paper describes a method to create PEDOT:PDA coatings on gold electrodes using cyclic voltammetry, resulting in enhanced electrochemical performance for bioelectronic applications.

This paper introduces a wireless powering method for injectable neural implants, demonstrating effective energy delivery and chronic stability in vivo.

This paper develops an electrophysiological method to study pore formation by bi-component staphylococcal leukotoxins in real-time using Xenopus oocytes, revealing receptor-dependent kinetics and a hybrid model of subunit oligomerization.

This study uses computational models to investigate how anatomical and stimulation parameters affect ECAPs during spinal cord stimulation, revealing trade-offs and optimization strategies.

This study evaluates the acute effects of four taVNS configurations on cardiorespiratory physiology, finding that unilateral left 25 Hz tragus stimulation increases heart rate variability compared to earlobe control.

This paper introduces cDONN, a neural network that generates EEG signals and classifies behavior simultaneously, showing high fidelity in synthesized EEG data.

The paper introduces KINESIS, a nanoelectrode array for label-free, high-resolution potassium sensing in single human iPSC-derived cardiomyocytes, showing distinct potential shifts in response to caffeine and ouabain.

This paper introduces a high-channel-count neural recording and stimulation platform with 5,376 channels, demonstrating its use in high-resolution cortical mapping in rat brains.

This study develops a synergy-based feedforward plus feedback control model for post-stroke walking, finding that 100% feedforward control best reproduces experimental data.

This paper shows polymicrobial extracellular vesicles enhance virulence and inflammation in CF bronchial epithelial cells, and modulator therapy ETI does not reverse this effect.

The paper tests the 'target mutation' hypothesis for batrachotoxin autoresistance in toxic birds and finds substitutions do not confer resistance, supporting a sequestration model.

This study shows that EEG microstate dynamics generate both periodic and aperiodic features in the neural power spectrum.

This paper compares methods for stress detection using EDA signals, showing that domain-specific models outperform foundation models in classification performance.

This paper introduces molecular pendulum electrochemical sensors with active-reset mechanisms for real-time, ultrasensitive protein detection in biofluids.

jaxon provides a differentiable, GPU-accelerated simulator for peripheral-nerve fiber models, achieving high speedup and precision in simulating and optimizing extracellular stimulation parameters.

This paper distinguishes between desensitization and inactivation of MscS channels using electrophysiology and simulations, showing inactivated channels resist activation under high tension.

This paper shows that heart rate, heart rate variability, and respiration can be estimated from tDCS output voltage without additional sensors.

This study develops human neurospheroids to model cortical complexity, finding that three-dimensionality and modularity enhance network activity richness and complexity.

This paper introduces Neural SS-DMP, a method that improves neural decoding by reframing it as structured control inference with behavior priors, achieving better accuracy and trajectory smoothness in ECoG and spiking data.

This study investigates how auditory-motor learning adapts to changing mappings, finding rapid short-term adjustments and slower long-term learning through targeted training.

This paper introduces a multi-user transformer-based decoder for intracortical speech BCIs, achieving lower word error rates with less training data than individual models.

This paper introduces ASWIFT, a tool for automated SWV signal quantification that outperforms existing methods in simulated and in vivo settings.

This study examines early cortical auditory responses to speech and clicks using EEG and MEG, finding a unique early peak in speech TRFs not present in click TRFs.

This study decodes agency-related neural states from EEG during human-AI interaction using deep learning, showing that low-frequency neural activity reflects perceived control.