Brain-machine interface to control a prosthetic arm with monkey ECoGs during periodic movements

Dissecting Biological and Synthetic Soft-Hard Interfaces for Tissue-Like Systems

Soft and hard materials at interfaces exhibit mismatched behaviors, such as mismatched chemical or biochemical reactivity, mechanical response, and environmental adaptability. Leveraging or mitigating these differences can yield interfacial processes difficult to achieve, or inapplicable, in pure soft or pure hard phases. Exploration of interfacial mismatches and their associated (bio)chemical, mechanical, or other physical processes may yield numerous opportunities in both fundamental studies and applications, in a manner similar to that of semiconductor heterojunctions and their contribution to solid-state physics and the semiconductor industry over the past few decades. In this review, we explore the fundamental chemical roles and principles involved in designing these interfaces, such as the (bio)chemical evolution of adaptive or buffer zones. We discuss the spectroscopic, microscopic, (bio)chemical, and computational tools required to uncover the chemical processes in these confined or hidden soft-hard interfaces. We propose a soft-hard interaction framework and use it to discuss soft-hard interfacial processes in multiple systems and across several spatiotemporal scales, focusing on tissue-like materials and devices. We end this review by proposing several new scientific and engineering approaches to leveraging the soft-hard interfacial processes involved in biointerfacing composites and exploring new applications for these composites.

Biosignal-Based Human-Machine Interfaces for Assistance and Rehabilitation: A Survey

As a definition, Human–Machine Interface (HMI) enables a person to interact with a device. Starting from elementary equipment, the recent development of novel techniques and unobtrusive devices for biosignals monitoring paved the way for a new class of HMIs, which take such biosignals as inputs to control various applications. The current survey aims to review the large literature of the last two decades regarding biosignal-based HMIs for assistance and rehabilitation to outline state-of-the-art and identify emerging technologies and potential future research trends. PubMed and other databases were surveyed by using specific keywords. The found studies were further screened in three levels (title, abstract, full-text), and eventually, 144 journal papers and 37 conference papers were included. Four macrocategories were considered to classify the different biosignals used for HMI control: biopotential, muscle mechanical motion, body motion, and their combinations (hybrid systems). The HMIs were also classified according to their target application by considering six categories: prosthetic control, robotic control, virtual reality control, gesture recognition, communication, and smart environment control. An ever-growing number of publications has been observed over the last years. Most of the studies (about 67%) pertain to the assistive field, while 20% relate to rehabilitation and 13% to assistance and rehabilitation. A moderate increase can be observed in studies focusing on robotic control, prosthetic control, and gesture recognition in the last decade. In contrast, studies on the other targets experienced only a small increase. Biopotentials are no longer the leading control signals, and the use of muscle mechanical motion signals has experienced a considerable rise, especially in prosthetic control. Hybrid technologies are promising, as they could lead to higher performances. However, they also increase HMIs’ complexity, so their usefulness should be carefully evaluated for the specific application.

Development of an sEMG sensor composed of two-layered conductive silicone with different carbon concentrations

To achieve robust sEMG measurements in an EMG prosthetic system, this study proposes a surface electromyogram (sEMG) sensor with a novel electrode structure composed of two-layered conductive silicone with different carbon concentrations. We hypothesized there is an optimal carbon concentration for achieving a large sEMG amplitude with robustness to external perturbation, and we empirically determined this optimal concentration. We produced fourteen sets of electrodes, with the weight ratio of carbon to silicone ranging from 1.7% to 4.0%. Using these electrodes, the user sEMG and electrical properties of the electrodes were measured. An external perturbation was applied on one side of the electrode to introduce a condition of unbalanced contact to the sEMG sensor. We defined an index of robustness for the sEMG sensor based on the signal-to-noise ratio in the balanced and unbalanced contact conditions. Based on the results of the robustness index, two optimal carbon concentrations, at weight ratios of 2.0%–2.1% and 2.6%–2.7%, were observed. Moreover, the double-peak property was correlated to the capacitance. Our results clearly demonstrate an optimal carbon concentration for robust sEMG measurements, and suggest that the robust measurement of sEMG is supported by the capacitance component of the sensor system.

Strategies for neural control of prosthetic limbs: from electrode interfacing to 3D printing

Limb amputation is a major cause of disability in our community, for which motorised prosthetic devices offer a return to function and independence. With the commercialisation and increasing availability of advanced motorised prosthetic technologies, there is a consumer need and clinical drive for intuitive user control. In this context, rapid additive fabrication/prototyping capacities and biofabrication protocols embrace a highly-personalised medicine doctrine that marries specific patient biology and anatomy to high-end prosthetic design, manufacture and functionality. Commercially-available prosthetic models utilise surface electrodes that are limited by their disconnect between mind and device. As such, alternative strategies of mind-prosthetic interfacing have been explored to purposefully drive the prosthetic limb. This review investigates mind to machine interfacing strategies, with a focus on the biological challenges of long-term harnessing of the user's cerebral commands to drive actuation/movement in electronic prostheses. It covers the limitations of skin, peripheral nerve and brain interfacing electrodes, and in particular the challenges of minimising the foreign-body response, as well as a new strategy of grafting muscle onto residual peripheral nerves. In conjunction, this review also investigates the applicability of additive tissue engineering at the nerve-electrode boundary, which has led to pioneering work in neural regeneration and bioelectrode development for applications at the neuroprosthetic interface.

Progress in the Field of Micro-Electrocorticography

Since the 1940s electrocorticography (ECoG) devices and, more recently, in the last decade, micro-electrocorticography (µECoG) cortical electrode arrays were used for a wide set of experimental and clinical applications, such as epilepsy localization and brain⁻computer interface (BCI) technologies. Miniaturized implantable µECoG devices have the advantage of providing greater-density neural signal acquisition and stimulation capabilities in a minimally invasive fashion. An increased spatial resolution of the µECoG array will be useful for greater specificity diagnosis and treatment of neuronal diseases and the advancement of basic neuroscience and BCI research. In this review, recent achievements of ECoG and µECoG are discussed. The electrode configurations and varying material choices used to design µECoG arrays are discussed, including advantages and disadvantages of µECoG technology compared to electroencephalography (EEG), ECoG, and intracortical electrode arrays. Electrode materials that are the primary focus include platinum, iridium oxide, poly(3,4-ethylenedioxythiophene) (PEDOT), indium tin oxide (ITO), and graphene. We discuss the biological immune response to µECoG devices compared to other electrode array types, the role of µECoG in clinical pathology, and brain⁻computer interface technology. The information presented in this review will be helpful to understand the current status, organize available knowledge, and guide future clinical and research applications of µECoG technologies.

Mapping ECoG channel contributions to trajectory and muscle activity prediction in human sensorimotor cortex

Studies on brain-machine interface techniques have shown that electrocorticography (ECoG) is an effective modality for predicting limb trajectories and muscle activity in humans. Motor control studies have also identified distributions of “extrinsic-like” and “intrinsic-like” neurons in the premotor (PM) and primary motor (M1) cortices. Here, we investigated whether trajectories and muscle activity predicted from ECoG were obtained based on signals derived from extrinsic-like or intrinsic-like neurons. Three participants carried objects of three different masses along the same counterclockwise path on a table. Trajectories of the object and upper arm muscle activity were predicted using a sparse linear regression. Weight matrices for the predictors were then compared to determine if the ECoG channels contributed more information about trajectory or muscle activity. We found that channels over both PM and M1 contributed highly to trajectory prediction, while a channel over M1 was the highest contributor for muscle activity prediction.

sEMG Sensor Using Polypyrrole-Coated Nonwoven Fabric Sheet for Practical Control of Prosthetic Hand

One of the greatest challenges of using a myoelectric prosthetic hand in daily life is to conveniently measure stable myoelectric signals. This study proposes a novel surface electromyography (sEMG) sensor using polypyrrole-coated nonwoven fabric sheet as electrodes (PPy electrodes) to allow people with disabilities to control prosthetic limbs. The PPy electrodes are sewn on an elastic band to guarantee close contact with the skin and thus reduce the contact electrical impedance between the electrodes and the skin. The sensor is highly customizable to fit the size and the shape of the stump so that people with disabilities can attach the sensor by themselves. The performance of the proposed sensor was investigated experimentally by comparing measurements of Ag/AgCl electrodes with electrolytic gel and the sEMG from the same muscle fibers. The high correlation coefficient (0.87) between the two types of sensors suggests the effectiveness of the proposed sensor. Another experiment of sEMG pattern recognition to control myoelectric prosthetic hands showed that the PPy electrodes are as effective as Ag/AgCl electrodes for measuring sEMG signals for practical myoelectric control. We also investigated the relation between the myoelectric signals' signal-to-noise ratio and the source impedances by simultaneously measuring the source impedances and the myoelectric signals with a switching circuit. The results showed that differences in both the norm and the phase of the source impedance greatly affect the common mode noise in the signal.