Evidence map›Paper›PMID 41388309›Full record

ArticleJournal of neuroengineering and rehabilitation2025

NeuraLoop: a high bandwidth closed-loop human-machine interface.

Luis Pelaez Murciego, Elias Thomassen Dam, Hans Henrik Dalgaard, Nikola Jorgovanovic, Matija Strbac, Erika G Spaich, Strahinja Dosen

Abstract read
In one paragraph

Article in Journal of neuroengineering and rehabilitation, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Luis Pelaez MurciegoDepartment of Health Science and Technology, The Faculty of Medicine, Aalborg University, Aalborg, Denmark.
Elias Thomassen DamDepartment of Health Science and Technology, The Faculty of Medicine, Aalborg University, Aalborg, Denmark.
Hans Henrik DalgaardDepartment of Health Science and Technology, The Faculty of Medicine, Aalborg University, Aalborg, Denmark.
Nikola JorgovanovicDepartment of Systems, Signals and Control, University of Novi Sad, Novi Sad, Serbia.
Matija StrbacTecnalia Serbia, Belgrade, Serbia.
Erika G SpaichDepartment of Health Science and Technology, The Faculty of Medicine, Aalborg University, Aalborg, Denmark.
Strahinja DosenDepartment of Health Science and Technology, The Faculty of Medicine, Aalborg University, Aalborg, Denmark. sdosen@hst.aau.dk.

Funding

Innovation Foundation Denmark program Innoexplorer 2071-00039BInnovation Foundation Denmark program Innoexplorer grant number: 2071-00039B
6 · The paper itself

Abstract

backgroundMyoelectric interfaces have emerged as powerful tools for human-machine interaction (HMI), enabling intuitive control of virtual and physical devices. However, most existing systems are limited by low spatial resolution and unidirectional communication. To address these limitations, we developed NeuraLoop, a wearable, high-bandwidth, bidirectional interface that integrates myoelectric (EMG) signal acquisition and electrotactile stimulation feedback within a single wearable textile-based platform.

methodsNeuraLoop comprises a flexible matrix of 32 EMG recording and 32 electrotactile stimulation pads controlled by a compact electronic unit. We evaluated the system in two experimental tasks involving ten healthy subjects to demonstrate: (1) online classification of four transient thumb micro-gestures (thumb rightwards, leftwards, upwards, and downwards swipe directions), and (2) closed-loop control of a virtual cursor using micro-gesture commands and spatially encoded tactile feedback. A time-division multiplexing (TDM) strategy was implemented to enable simultaneous stimulation and recording.

resultsThe subjects achieved a median success rate of 82% on the first attempt and over 94% within two attempts during online classification with visual feedback. All four micro-gestures were classified with similar accuracy. In the closed-loop control task with tactile feedback, participants navigated a 3 × 4 grid using only electrotactile stimulation, achieving 70% accuracy for exact target hits and 95% when including the hits in the neighboring cells (1 cell distance error).

conclusionsNeuraLoop demonstrates the feasibility of high-bandwidth, bidirectional HMI using a wearable, textile-based interface. The system enables accurate recognition of subtle micro-gestures and effective delivery of spatially encoded tactile feedback. These capabilities open new possibilities for intuitive control in applications such as prosthetics, rehabilitation, and virtual/augmented reality. Future work will explore multimodal feedback encoding and proportional gesture control.

Indexed as

ElectromyographyMan-Machine SystemsUser-Computer InterfaceWearable Electronic DevicesAdultFemaleGesturesHumansMaleTextilesTouchYoung AdultBidirectional human-machine interfaceClosed-loop controlElectromyographyElectrotactile stimulationFeedbackMicro-gestureMyocontrolTextile electrodeWearable

Identifiers

PMID41388309
PMCPMC12817707

What OpenQuestion holds

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LicenceCC BY-NC-ND
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.