These authors contributed equally to this work . Abstract: Brain - computer interfaces (BCIs) promise to extend human movement capabilities by enabling direct neural control of supernumerary effectors, yet integrating augmented commands with multi ple degrees of freedom without disrupting natural movement remains a k ey challenge. Here, we propose a tactile - encoded BCI that leverages sensory afferents through a novel tactile - evoked P300 paradigm, allowing intuitive and reliable decoding of supernumerary motor intentions even when superimposed with voluntary actions. The interface was evaluated in a multi - day experiment comprising of a single motor recognition task to validate baseline BCI performance and a dual task paradigm to assess the potential influence between the BCI and natural human movement . T he brain interface achieved real - time and reliable decoding of four supernumerary degrees of freedom, with significant performance improvement s after only three days of training. Importantly, after training, performance did not differ significantly b etween the single - and dual - BCI task conditions, and natural movement remained unimpaired during concurrent supernumerary control . Lastly, the interface was deployed in a movement augmentation task, demonstrating its ability to command two supernumerary robotic arms for functional assistance during bimanual tasks. These results establish a new neural interface paradigm for movement augmentation through stimulation of sensory afferents, expanding motor degrees of fr eedom without impairing natural movement . One - Sentence Summary: T actile - encoded neural interface enables intuitive control of supernumerary limbs without compromising natural human movement Main Text: INTRODUCTION Humans interact with their surroundings with remarkable dexterity and efficiency. Recent advances in robotics and neural interfaces hold the potential to increase these capabilities, enhancing human movement beyond its natural limits. Movement augmentation aims to increase the mechanical degrees of freedom (DoFs) an individual can exert over their surroundings ( 1), allowing movement tasks to be performed more efficiently or enable actions otherwise impossible with natural limbs alone, such as trimanual manipulation with a third arm ( 2) . A central challenge, however, lies in achieving practical control of supernumerary effectors (SEs) without compromising natural movement. Current strategies for augmenting DoFs often rely on augmentation by transfer, in which control of SEs is derived from the function of an existing body part, typically one that is task - irrelevant ( 1, 3, 4) .

A semi-autonomous prosthesis control based on computer vision can be used to improve performance while decreasing the cognitive burden, especially when using advanced systems with multiple functions. However, a drawback of this approach is that it relies on the complex processing of a significant amount of data (e.g., a point cloud provided by a depth sensor), which can be a challenge when deploying such a system onto an embedded prosthesis controller. In the present study, therefore, we propose a novel method to reconstruct the shape of the target object using minimal data. Specifically, four concurrent laser scanner lines provide partial contours of the object cross-section. Simple geometry is then used to reconstruct the dimensions and orientation of spherical, cylindrical and cuboid objects. The prototype system was implemented using depth sensor to simulate the scan lines and vibrotactile feedback to aid the user during aiming of the laser towards the target object. The prototype was tested on ten able-bodied volunteers who used the semi-autonomous prosthesis to grasp a set of ten objects of different shape, size and orientation. The novel prototype was compared against the benchmark system, which used the full depth data. The results showed that novel system could be used to successfully handle all the objects, and that the performance improved with training, although it was still somewhat worse compared to the benchmark. The present study is therefore an important step towards building a compact system for embedded depth sensing specialized for prosthesis grasping.