Neural Noise

2026 week 13

19 min read

BCI Weekly Brief (week of 2026-03-23)

Industry and funding

  • China commercial BCI approval (March 13, 2026): Regulators cleared what is widely reported as the first commercially authorized BCI medical device in this class — a minimally invasive extradural system (electrodes outside the dura, unlike penetrating intracortical implants) from Shanghai-based Borui Kang Medical Technology aimed at restoring hand grasp for quadriplegia patients (e.g. with a robotic glove). Coverage also notes a growing slate of other domestic implantable BCI programs and enrollment plans for 2026. Reuters
  • Science Corp Series C ($230M): The retinal-implant / neurotechnology company (founded by Neuralink co-founder and former president Max Hodak) closed a large round toward commercialization of its PRIMA program; Science Corp says that with PRIMA’s expected commercial launch in Europe it will be the first BCI company with a vision restoration product on the market (CE mark submitted). Trade press discusses EU regulatory timing alongside U.S. development. Stat News · Science Corp
  • Cognito Therapeutics (~$105M Series C): Oversubscribed financing to advance the gamma-frequency Spectris headset through Alzheimer’s development; company-reported EEG data at AD/PD 2026 included attenuation of EEG slowing in treated patients. Yahoo Finance · BioSpace (AD/PD)
  • Neuralink production scale-up: Reporting points to a push toward higher-volume manufacturing and a more automated surgical workflow in 2026 as the commercial backdrop for implantable-interface R&D. Reuters

Regulatory and policy

  • Competitive geography: The Borui Kang approval is widely framed as a signal that Chinese firms may hit certain commercial milestones on timelines that differ from U.S. trial-heavy paths — useful macro context alongside DBS and neuromodulation papers. TechCrunch
  • At-home neuromodulation precedent: Flow Neuroscience received FDA approval in December 2025 for an at-home prescription tDCS (transcranial direct current stimulation) treatment for depression — a regulatory comparator for readers following portable stimulation trials (this week’s tACS depression RCT); the cleared modality differs from tACS. Reuters
  • Neural data and governance: Policy and ethics discussions continue to stress sensitive treatment of neural and EEG-derived signals (privacy-law evolution, workplace monitoring, and neurotech governance). New America · WEF

How this context maps to this week’s picks

Field headlineConnects to
China BCI approval (Borui Kang)DBS and clinical neuromodulation entries
Science Corp Series C / PRIMA vision-restoration timelineOn-device decoding / hardware story
Cognito financing + Alzheimer’s EEG dataEEG items + portable tACS RCT
Flow at-home depression device clearancePortable tACS RCT for depression
Neuralink manufacturing / automation narrativeBidirectional neuromorphic interface
Neural data privacy / governanceDBS-for-depression coverage; ethics framing for chronic implants

A Bidirectional Neural Interface With Direct On-Device Neuromorphic Decoding for Closed-Loop Optogenetics

bioRxiv Neuroscience

Published: 2026-03-28T00:00:00+00:00

Tags: neural-interface, neural-decoding, closed-loop, neuromodulation, hardware, tier-1

Wireless bidirectional neural interface with on-device neuromorphic spike decoder enabling closed-loop optogenetic stimulation in freely moving animals. Directly advances compact BCI hardware and real-time neural decoding — key bottleneck for next-gen implantable systems.

  • Researchers developed a wireless bidirectional neural interface that simultaneously records neural activity and delivers optogenetic stimulation in a single compact headstage.
  • The device integrates a neuromorphic spike decoder directly on-chip, eliminating the need for an external computer to run computationally intensive decoding algorithms.
  • The system enables closed-loop neuromodulation, automatically adjusting optogenetic stimulation in real time based on decoded neural signals.
  • The platform is designed for freely moving animals, removing the tethered-setup constraint that limits most existing closed-loop optogenetics systems.
  • Most current bidirectional neural interfaces offload spike decoding to external processors, making them impractical for untethered, naturalistic behavioral experiments.
  • The work targets a key bottleneck for next-generation implantable brain-computer interfaces: fitting real-time neural decoding into miniaturized, wireless hardware.

Exploration of using ‘distance-to-bound’ to manipulate the difficulty during motor imagery BCI training after stroke—a clinical two-cases study

Journal of Neural Engineering

Published: 2026-03-27T00:00:00+00:00

Tags: BCI, motor-imagery, neurorehabilitation, adaptive-training, tier-1

Directly addresses a key BCI usability bottleneck: adapting MI-BCI task difficulty online using distance-to-bound. Clinical stroke cases in JNE give it immediate translational weight for rehab BCI teams.

  • Motor imagery-based brain–computer interfaces (MI-BCIs) are a promising but difficult-to-use technology for neurorehabilitation after stroke, with many users unable to produce discriminable brain-activity patterns.
  • The study explores a distance-to-bound (DTB) method for adapting MI-BCI task difficulty online during training sessions.
  • DTB measures how far a user’s brain-activity features are from the classifier decision boundary and uses that metric to adjust task difficulty in real time.
  • The work is a clinical two-case study conducted with stroke patients, giving it direct translational relevance for rehabilitation BCI teams.
  • Personalizing BCI task difficulty is proposed as a way to support the user learning process, yet very limited knowledge exists on methods that can operate online.
  • The paper was published in the Journal of Neural Engineering, a venue that lends peer-reviewed clinical weight to the findings.

Meta’s TRIBE AI: A New Foundation Model Decoding Human Brain Activity

Neuroscience News Magazine

Published: 2026-03-26T20:10:10+00:00

Tags: neural-decoding, foundation-model, computational-neuroscience, tier-1

Meta's TRIBE uses in-silico simulation to build cross-individual, cross-language neural decoding at 70x resolution gain. Signals big-tech investment in neural decoding foundation models applicable to BCI pipelines.

  • Meta AI introduced TRIBE, a foundation model designed to predict human brain responses to both visual and auditory stimuli.
  • TRIBE uses in-silico (computer-simulated) brain activity rather than relying solely on physical neuroimaging scans.
  • The model achieves a 70-fold increase in resolution compared to prior approaches for mapping neural responses.
  • TRIBE can decode neural patterns across different individuals without requiring new brain scans for each person.
  • The model generalizes across languages, enabling cross-language neural decoding from a shared learned representation.
  • TRIBE is positioned as a cross-individual foundation model, aiming to learn universal neural encoding patterns rather than person-specific ones.
  • The work signals significant big-tech investment in neural decoding foundation models with potential applications in brain-computer interface (BCI) pipelines.

Effect sizes and test power to evaluate spike sorting

Journal of Neurophysiology

Published: 2026-03-27T03:52:34+00:00

Tags: spike-sorting, neural-recording, methods, electrophysiology, tier-1

Spike sorting is foundational infrastructure for invasive BCIs. This paper provides statistical rigor (effect sizes, power analysis) for evaluating sorters—directly useful for labs benchmarking decoders on Utah arrays or Neuropixels.

  • A new study in the Journal of Neurophysiology (ahead of print) introduces effect-size metrics and statistical power analysis as tools for evaluating spike-sorting algorithms.
  • Spike sorting—the process of assigning recorded extracellular waveforms to individual neurons—is foundational infrastructure for invasive brain–computer interfaces.
  • The framework is designed to give labs a statistically rigorous way to benchmark spike sorters used with high-density recording platforms such as Utah arrays and Neuropixels probes.
  • By formalizing effect sizes and test power, the paper aims to replace ad hoc accuracy comparisons with reproducible, quantitative evaluations of sorting performance.

Electroencephalogram-based multimodal attention level classification using deep learning techniques

Frontiers in Human Neuroscience

Published: 2026-03-27T00:00:00+00:00

Tags: EEG, BCI, deep-learning, multimodal, tier-1

Proposes MEAN architecture fusing EEG+ECG+EOG for attention-state BCI. Multimodal physiological fusion is a growing trend in non-invasive BCI the deep-learning pipeline is directly replicable for real-time applications.

  • Researchers propose the Multi-Feature Enhanced Attention Network (MEAN), a deep-learning architecture that classifies attention levels by fusing EEG, ECG, and EOG signals in a single multimodal brain-computer interface pipeline.
  • EEG supplies direct measures of brain electrical activity, ECG captures heart-rate variability as a proxy for autonomic and emotional state, and EOG contributes eye-movement data—each modality offsetting the others’ blind spots.
  • The multimodal fusion approach is designed to improve both prediction accuracy and robustness compared with EEG-only attention classifiers.
  • The study targets real-time BCI applications, making the deep-learning pipeline directly replicable for live attention-state monitoring.
  • The work is published in Frontiers in Human Neuroscience, contributing to a growing body of research on non-invasive physiological-signal fusion for brain-computer interfaces.

A multicenter randomized clinical trial of portable transcranial alternating current stimulation for major depressive disorder

Nature (Neuroscience subject)

Published: 2026-03-28T00:00:00+00:00

Tags: tACS, transcranial-stimulation, neuromodulation, clinical-trial, tier-1

Multicenter RCT of portable tACS for depression — direct keyword match on transcranial stimulation and neuromodulation. Clinical trial evidence for at-home non-invasive brain stimulation devices informs the regulatory and commercial path for consumer neurostim.

’RMT-Finder’: an automated procedure to determine the Resting Motor Threshold for Transcranial Magnetic Stimulation

bioRxiv Neuroscience

Published: 2026-03-27T00:00:00+00:00

Tags: TMS, transcranial-stimulation, neuromodulation, methods, tier-1

Automated RMT calibration reduces TMS setup variability and time. Directly relevant to neuromodulation and transcranial stimulation workflows the algorithm auto-adjusts intensity from MEP amplitudes, improving standardization.

  • Researchers developed ‘RMT-Finder,’ an automated algorithm that determines the Resting Motor Threshold (RMT) for Transcranial Magnetic Stimulation by measuring Motor Evoked Potential (MEP) amplitudes and auto-adjusting stimulation intensity.
  • RMT calibration is a standard step in TMS studies but is traditionally time-consuming and subject to inter-operator variability.
  • The algorithm replaces manual intensity adjustments with an automated feedback loop driven by real-time MEP amplitude data.
  • In Experiment 1 the team directly compared the automated RMT-Finder method against the conventional manual threshold-determination procedure.
  • By standardizing how stimulation intensity is titrated, RMT-Finder aims to reduce both setup time and between-session variability in TMS experiments.
  • The tool is designed to slot into existing neuromodulation and transcranial stimulation workflows without requiring new hardware.

Resting-State Electroencephalogram and Heart Rate Variability Jointly Predicts Intentional Episodic Memory Control Using Machine Learning Fusion

Journal of Neurophysiology

Published: 2026-03-27T03:52:04+00:00

Tags: EEG, machine-learning, neural-signal-processing, neurofeedback, tier-1

Fuses resting-state EEG with HRV via ML to predict cognitive state. Demonstrates the value of multimodal physiological time-series for brain-state decoding—relevant to passive BCI and neurofeedback paradigms.

  • Researchers combined resting-state EEG and heart rate variability (HRV) recordings to jointly predict intentional episodic memory control.
  • A machine learning fusion approach was used to integrate the two physiological modalities into a single predictive model.
  • Both signals were collected at rest, meaning no active task was required from participants during recording—a key feature for passive brain-computer interface (BCI) applications.
  • The study, published ahead of print in the Journal of Neurophysiology, demonstrates that multimodal physiological time-series can decode cognitive states relevant to neurofeedback paradigms.

Ultrasound Wristband Translates Muscle “Strings” into Robotic Dexterity

Neuroscience News Magazine

Published: 2026-03-28T17:17:32+00:00

Tags: neuroprosthetics, neural-decoding, neurorobotics, wearable, tier-1

Non-invasive wrist-worn ultrasound device uses AI to decode muscle and tendon kinematics across 22 DOF in real time. Directly adjacent to BCI as a peripheral neural/musculoskeletal interface for prosthetic and VR control. Demonstrates viable decoding pipeline outside the brain.

  • Researchers developed a non-invasive ultrasound wristband that reads muscle and tendon movements to give robots and VR avatars human-like hand dexterity.
  • The wearable device tracks 22 degrees of freedom in the hand in real time, capturing the full complexity of finger and wrist motion.
  • An AI model decodes the ultrasound signals from muscle and tendon kinematics at the wrist, translating them into precise hand-control commands.
  • The system works as a peripheral musculoskeletal interface, demonstrating that detailed motor intent can be decoded outside the brain without invasive implants.
  • Target applications include prosthetic hand control and virtual-reality interaction, where fine finger movements are essential.
  • The approach offers a potential complement or alternative to brain-computer interfaces by capturing motor signals at the wrist rather than the cortex.

Pallidal and subthalamic stimulations modulate inter-hemispheric interaction and asymmetry in Parkinson’s disease

Molecular Psychiatry

Published: 2026-03-29T00:00:00+00:00

Tags: neuromodulation, DBS, clinical-neurophysiology, Parkinson, tier-1

DBS study in Molecular Psychiatry examining how pallidal vs. subthalamic stimulation modulates inter-hemispheric neural dynamics in PD. Directly relevant to neuromodulation target selection and understanding stimulation-driven network effects — key for next-gen adaptive DBS systems.

  • A new study published in Molecular Psychiatry investigates how deep brain stimulation of two common surgical targets — the globus pallidus (GPi) and the subthalamic nucleus (STN) — differently modulates inter-hemispheric neural dynamics in Parkinson’s disease.
  • The research directly compares pallidal versus subthalamic stimulation, examining how each target reshapes communication and asymmetry between the left and right cerebral hemispheres.
  • Findings bear on the clinical question of DBS target selection, potentially informing which patients benefit more from GPi versus STN implantation.
  • The work is framed as relevant to next-generation adaptive (closed-loop) DBS systems, which adjust stimulation in real time based on neural feedback.

Time-Varying Dynamic Causal Modelling for Sequential Responses: Neural Mechanisms of Slow Cortical Potentials, Preparation, Planning and Beyond

bioRxiv Neuroscience

Published: 2026-03-27T00:00:00+00:00

Tags: computational-neuroscience, neural-signal-processing, methods, tier-2

Extends DCM to time-varying parameters for slow cortical potentials and motor planning. SCPs are a classic BCI control signal non-stationary modeling could improve real-time BCI decoding of preparatory neural dynamics.

  • The paper extends Dynamic Causal Modelling (DCM) to support time-varying parameters, removing the conventional assumption that model parameters remain stationary over time.
  • It targets slow cortical potentials (SCPs) and motor-planning signals, both of which unfold over timescales longer than typical event-related transients.
  • Cognitive processes such as decision-making, working memory, and motor planning are shown to operate across a hierarchy of timescales, combining rapid neural transients with slower mechanisms like short-term plasticity.
  • Standard DCM treats connection strengths and other parameters as fixed, which limits its ability to capture the non-stationary dynamics underlying sequential neural responses.
  • Previous time-varying DCM approaches rely on segmenting data into discrete epochs, which artificially resets neural states at window boundaries.
  • The proposed framework avoids epoch-based segmentation, allowing neural state estimates to evolve continuously across a sequence of responses.
  • Slow cortical potentials are a classic control signal for brain–computer interfaces, and non-stationary modelling of preparatory dynamics could improve real-time BCI decoding accuracy.

Can Deep Brain Stimulation Unlock Treatment-Resistant Depression?

Neuroscience News Magazine

Published: 2026-03-28T17:35:37+00:00

Tags: neuromodulation, DBS, clinical-trial, tier-1

Reports on DBS trials targeting white matter tracts for treatment-resistant depression. Implanted neuromodulation expanding beyond movement disorders signals growing clinical scope for neural devices. Relevant to BCI community tracking regulatory and ethical precedent for chronic brain implants.

  • Researchers are conducting clinical trials of Deep Brain Stimulation (DBS) as a treatment for treatment-resistant depression.
  • The implanted device functions as a “brain pacemaker,” delivering targeted electrical stimulation to specific brain regions.
  • DBS electrodes in these trials target white matter tracts, the brain’s connective wiring, rather than gray matter nuclei.
  • The therapy is aimed at patients who have not responded to standard antidepressant medications or psychotherapy.
  • DBS has traditionally been used for movement disorders such as Parkinson’s disease and is now expanding into psychiatric indications.
  • The trials represent a broader trend of implanted neuromodulation devices moving into new clinical territory beyond their original movement-disorder applications.

Transformer Language Models Reveal Distinct Patterns in Aphasia Subtypes and Recovery Trajectories

bioRxiv Neuroscience

Published: 2026-03-27T00:00:00+00:00

Tags: speech-prosthesis, computational-neuroscience, language, tier-2

Uses GPT-2 embeddings to track aphasia recovery longitudinally. Computational language modeling of impaired speech connects to speech-prosthesis decoding pipelines and could inform BCI language models for communication restoration.

  • Researchers used GPT-2 transformer embeddings as a computational framework to analyze narrative speech produced by individuals with aphasia.
  • The study drew on a longitudinal dataset of narrative speech samples collected at six time points from 47 participants with aphasia as part of an intervention study.
  • Distinct representational disruption patterns in discourse production were identified across different aphasia subtypes using the language-model approach.
  • The transformer-based analysis captured recovery trajectories over time, linking changes in model-derived features to clinical improvement.
  • The work connects computational language modeling of impaired speech to applied pipelines such as speech-prosthesis decoding and brain–computer-interface language models for communication restoration.

From Coarse to Rich: Successive Waves of Visual Perception in Prefrontal Cortex

bioRxiv Neuroscience

Published: 2026-03-28T00:00:00+00:00

Tags: neural-recording, neural-decoding, electrophysiology, computational-neuroscience, tier-2

Chronic spiking recordings in macaque vlPFC reveal dynamic representational geometry for naturalistic images. Methodology — large-scale chronic neural recording with population decoding — is directly transferable to BCI decoder design.

  • The ventrolateral prefrontal cortex (vlPFC), traditionally associated with cognitive control and language, also plays a dynamic role in visual processing according to new chronic spiking data from macaque monkeys.
  • Researchers used large-scale chronic recordings of spiking activity to map vlPFC’s representational geometry while a macaque passively viewed a large set of naturalistic images.
  • Visual information in vlPFC is encoded in successive waves, progressing from coarse to rich representations over time — suggesting a staged, dynamic encoding process rather than a single static snapshot.
  • Population-level decoding of neuronal ensembles revealed how different types of visual information are represented across the vlPFC population at different time points.
  • The study’s methodology — chronic neural recording combined with population decoding of naturalistic stimuli — is directly transferable to brain-computer interface (BCI) decoder design.

Neural mechanisms underlying the effects of visual input on static balance in older adults with mild cognitive impairment

Frontiers in Neuroscience

Published: 2026-03-27T00:00:00+00:00

Tags: fNIRS, neuroimaging, clinical-neurophysiology, tier-2

Uses fNIRS to map brain network topology during balance tasks in MCI patients. Demonstrates fNIRS as a practical non-invasive neuroimaging tool linking cortical dynamics to functional motor outcomes.

  • The study enrolled 34 older adults with mild cognitive impairment (MCI) and 34 cognitively normal (CN) controls to investigate how visual input affects static balance.
  • Participants performed two-leg standing tasks under eyes-open and eyes-closed conditions on a three-dimensional force platform.
  • Center of pressure (COP) indices were measured to quantify static balance performance across visual conditions.
  • Functional near-infrared spectroscopy (fNIRS) was used during the balance tasks to capture cortical activation in real time.
  • Researchers analyzed brain network topological properties derived from fNIRS to explore how cortical network organization couples with balance control.
  • The study specifically targeted the link between brain network topology and balance performance, aiming to clarify why MCI patients show greater postural instability.
  • fNIRS was chosen as a practical, non-invasive neuroimaging approach compatible with upright standing tasks—unlike fMRI, which requires participants to lie still.
  • The work was published in Frontiers in Neuroscience (2026) and addresses the gap in understanding how cognitive decline in MCI disrupts sensory integration for postural control.

Accumulation of virtual tokens towards a jackpot reward enhances performance and value encoding in dorsal anterior cingulate cortex

Nature (Neuroscience subject)

Published: 2026-03-28T00:00:00+00:00

Tags: computational-neuroscience, neural-recording, neural-decoding, tier-2

Nature Communications paper on neural value encoding in dorsal ACC, likely using electrophysiology. Relevant to computational neuroscience and understanding how reward signals are represented — foundational for decoding motivational states in BCI applications.

Brain criticality emerges with developmental shifts in frequency-specific excitation-inhibition balance

bioRxiv Neuroscience

Published: 2026-03-28T00:00:00+00:00

Tags: EEG, computational-neuroscience, neural-signal-processing, tier-2

Links EEG-derived E/I balance to criticality across development. Relevant to EEG-based BCI calibration — understanding how neural operating regimes shift with age could improve decoder robustness for pediatric and aging populations.

Rapid cortical mapping with cross-participant encoding models

bioRxiv Neuroscience

Published: 2026-03-27T00:00:00+00:00

Tags: neural-decoding, computational-neuroscience, methods, tier-2

Cross-participant transfer of voxelwise encoding models enables rapid cortical mapping from minimal per-subject data. The transfer-learning framework is conceptually applicable to reducing calibration time in neural decoders and BCIs.

  • Voxelwise encoding models trained on functional MRI data can produce detailed maps of cortical organization but currently require many hours of brain responses per participant, limiting clinical use.
  • The study introduces a cross-participant modeling framework that trains voxelwise encoding models on extensive fMRI data from previously scanned reference participants and then transfers those models to new individuals.
  • The transfer step enables rapid cortical mapping from minimal per-subject data, dramatically reducing the scanning burden on each new participant.
  • The framework follows a transfer-learning paradigm: a large shared model captures general cortical response patterns, then a small amount of target-participant data adapts it to individual anatomy and function.
  • Reducing per-subject data requirements has direct implications for clinical neuroimaging, where long scanning sessions are often impractical.
  • The same cross-participant transfer concept is applicable to shortening calibration time in neural decoders and brain–computer interfaces.

Representational Dynamics in the Hippocampus and Medial Prefrontal Cortex during Learning and Task Mastery

bioRxiv Neuroscience

Published: 2026-03-28T00:00:00+00:00

Tags: neural-recording, electrophysiology, computational-neuroscience, neural-data-analysis, tier-2

Large-scale neural recordings from CA1 and mPFC during learning show trial-by-trial place-field dynamics and representational drift. Methods for tracking evolving neural codes inform adaptive BCI decoders that must handle non-stationary signals.

  • Researchers recorded large-scale neural activity simultaneously from hippocampal area CA1 and the medial prefrontal cortex (mPFC) in rats learning radial maze tasks over multiple weeks.
  • The study tracked how spatial representations in both regions evolved trial by trial as animals progressed from naïve to proficient performance.
  • A novel form of trial-by-trial ‘flickering’ was identified in which competing hippocampal place fields alternated in dominance within a single neuron.
  • During learning, one hippocampal place field gradually overtook its competitor, suggesting a competitive selection mechanism for stabilizing spatial maps.
  • Despite long-standing evidence that hippocampus and prefrontal cortex interact across cognitive functions, how their spatial representations co-evolve during learning had remained poorly characterized.
  • The methods developed to track representational drift on a trial-by-trial basis could inform adaptive brain–computer-interface decoders that must cope with non-stationary neural signals.

Neuroscience News Magazine

Published: 2026-03-27T17:34:19+00:00

Tags: EEG, neural-signal-processing, computational-neuroscience, tier-2

New Xi-alphaNET model links alpha-wave slowing to white-matter decline across 1,965 subjects aged 5-100. Relevant to EEG-based BCI: age-dependent alpha dynamics affect signal baselines and decoder assumptions for neurofeedback and BCI.

  • A new computational model called Xi-alphaNET links the speed of brain alpha waves directly to the brain’s physical white-matter wiring.
  • The study analyzed EEG and structural data from 1,965 participants spanning ages 5 to 100.
  • Slowing of alpha-wave oscillations across the lifespan serves as a direct biomarker of white-matter decline during aging.
  • Xi-alphaNET demonstrates that alpha-wave dynamics are not purely neural in origin but are shaped by the structural connectivity of the brain.
  • The findings span nearly the entire human lifespan, capturing developmental changes in childhood through deterioration in old age.
  • Age-dependent shifts in alpha rhythms have practical implications for EEG-based brain-computer interfaces, where decoder models and neurofeedback baselines may need age-specific calibration.

Integrating the behavior of biological soft tissue into musculoskeletal simulation for the design of wearable assistive devices

Frontiers in Human Neuroscience

Published: 2026-03-27T00:00:00+00:00

Tags: neuroprosthetics, biomedical-engineering, wearable-devices, tier-2

Addresses the body-device interface for exoskeletons and orthoses by modeling soft-tissue mechanics. Relevant to neuroprosthetics teams designing wearable assistive devices where force transmission fidelity matters.

  • Wearable assistive devices such as ankle-foot orthoses (AFOs) and exoskeletons are routinely evaluated with musculoskeletal simulations, yet the body-device interface is typically oversimplified.
  • The physical interface between a wearable device and the body consists of biological soft tissue, whose deformation affects relative movement, force transmission, and overall biomechanical effectiveness.
  • Current simulation-based design workflows generally neglect soft-tissue mechanics at the device-body boundary, potentially compromising force-transmission fidelity.
  • This study proposes integrating realistic soft-tissue behavior into musculoskeletal simulations to improve the design accuracy of wearable assistive devices.

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