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

Background: Myoelectric 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. Methods: NeuraLoop 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. Results: The 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). Conclusions: NeuraLoop 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.