Intracortical Microstimulation as a Feedback Source for Brain-Computer Interface Users

Flesher, Sharlene N; Downey, John E.; Collinger, Jennifer L.; Foldes, Stephen T.; Weiss, Jeffrey M.; Tyler-Kabara, Elizabeth C.; Bensmaı̈a, Sliman J.; Schwartz, Andrew B.; Boninger, Michael L.; Gaunt, Robert A.

doi:10.1007/978-3-319-64373-1_5

Cited by 35 publications

(37 citation statements)

References 17 publications

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“…Example of CNS stimulation to evoke sensory percepts . a) Right, map of palmar side of the hand; colored areas represent all the projected fields that were reported more than once and that were not part of diffuse sensations.…”

Section: Implantable Neuroprostheses In Clinical Applicationsmentioning

confidence: 77%

“…Comparison among different strategies of stimulation of PNS to evoke tactile percepts. Parameters that have been considered are: i) number of subjects (S) and implant duration; ii) type of implanted electrode, number of channels, nerve target; iii) impedance measurement; iv) type of stimulation pattern; v) threshold charge/current over time; vi) …”

Section: Implantable Neuroprostheses In Clinical Applicationsmentioning

confidence: 99%

“…Flesher et al demonstrated that microsimulation of somatosensory cortex in a subject with long‐term spinal cord injury. In particular two arrays of silicon microelectrodes, composed of 60 shanks, 1.5 mm long and iridium oxide coated tips, were implanted in primary somatosensory cortex S1.…”

Section: Implantable Neuroprostheses In Clinical Applicationsmentioning

confidence: 99%

Section: Implantable Neuroprostheses In Clinical Applicationsmentioning

confidence: 99%

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Implantable Neural Interfaces and Wearable Tactile Systems for Bidirectional Neuroprosthetics Systems

Cutrone

Micera

2019

Adv Healthcare Materials

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functions of nervous system when damages occur. Moreover, in case of physical impairments, the use of artificial tactile sensors is also paramount to ensure the possibility to restore the sense of touch to the patient. Bioinspiration from human natural touch could help in the release of enhanced tactile sensors to be used in neuroprosthetics. Technological and biological advancements brought serious improvements in the quality of these devices, such as miniaturization and increased biocompatibility. This article reviews the essential design criteria, working principles, materials, and technical considerations on neural interfaces and tactile sensors; their clinical application in neuroprosthetics and their future progress toward optimized solutions. Neural InterfacesA neural interface device (NID) is an implantable tool that aims at restoring nervous system functions in case of impairments or communicates with external components (e.g., limb prostheses); its main objective is to record neural signal from a small group of axons/cells or to stimulate a selected area of the nervous system. The clinical potentialities of NIDs range from their applications in central nervous system (CNS) for treating epilepsy seizures, mitigating Parkinson's disease; in peripheral nervous system (PNS) for controlling limb prostheses in amputees and restore sensory feedback; in autonomous nervous system such as vagus nerve to treat drug-resistant epilepsy and chronic depression to finally auditory and vision systems to restore the perception of specific sounds and phosphenes. An ideal NID should be biocompatible, highly selective, low invasive and stable over long time. If intended for clinical applications, the functionality of a NID should be reliable and should last decades. Considering the different implantation sites of interest of the device, various solutions have been proposed to optimize the communication between the NID and the surrounding environment. Figure 1 highlights the main applications of most common used NIDs in neuroprosthetics. With a special focus on implantable neuroprostheses and motor system, this paragraph will portray the design criteria, the materials and technical considerations heretofore used for the development of diverse types of NID used in CNS and PNS.Neuroprosthetics and neuromodulation represent a promising field for several related applications in the central and peripheral nervous system, such as the treatment of neurological disorders, the control of external robotic devices, and the restoration of lost tactile functions. These actions are allowed by the neural interface, a miniaturized implantable device that most commonly exploits electrical energy to fulfill these operations. A neural interface must be biocompatible, stable over time, low invasive, and highly selective; the challenge is to develop a safe, compact, and reliable tool for clinical applications. In case of anatomical impairments, neuroprosthetics is bound to the need of exploring the surrounding environment by fast-responsive and ...

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Section: Implantable Neuroprostheses In Clinical Applicationsmentioning

confidence: 77%

Section: Implantable Neuroprostheses In Clinical Applicationsmentioning

confidence: 99%

Section: Implantable Neuroprostheses In Clinical Applicationsmentioning

confidence: 99%

Section: Implantable Neuroprostheses In Clinical Applicationsmentioning

confidence: 99%

See 2 more Smart Citations

Implantable Neural Interfaces and Wearable Tactile Systems for Bidirectional Neuroprosthetics Systems

Cutrone

Micera

2019

Adv Healthcare Materials

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“…Such online adaptability needs of course to be learned and personalized to each individual patient, a task that is ideally suited for AI/ML approaches. In the sensory domain, some have shown that BMI can be used to generate actionable entirely artificial tactile sensations to trigger complex motor decisions (O'Doherty et al, 2012;Klaes et al, 2014;Flesher et al, 2017). Most of the BMI research work has, however, focused on the sensory motor system because of the relatively focused and well-defined nature of the neural circuits.…”

Section: Introductionmentioning

confidence: 99%

Explainable Artificial Intelligence for Neuroscience: Behavioral Neurostimulation

et al. 2019

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The use of Artificial Intelligence and machine learning in basic research and clinical neuroscience is increasing. AI methods enable the interpretation of large multimodal datasets that can provide unbiased insights into the fundamental principles of brain function, potentially paving the way for earlier and more accurate detection of brain disorders and better informed intervention protocols. Despite AI's ability to create accurate predictions and classifications, in most cases it lacks the ability to provide a mechanistic understanding of how inputs and outputs relate to each other. Explainable Artificial Intelligence (XAI) is a new set of techniques that attempts to provide such an understanding, here we report on some of these practical approaches. We discuss the potential value of XAI to the field of neurostimulation for both basic scientific inquiry and therapeutic purposes, as well as, outstanding questions and obstacles to the success of the XAI approach.

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Brain–Computer Interfaces: Taking Thoughts Out of the Human Body

Gregg

2022

Digital Disruption in Health Care

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Intracortical Microstimulation as a Feedback Source for Brain-Computer Interface Users

Cited by 35 publications

References 17 publications

Implantable Neural Interfaces and Wearable Tactile Systems for Bidirectional Neuroprosthetics Systems

Implantable Neural Interfaces and Wearable Tactile Systems for Bidirectional Neuroprosthetics Systems

Explainable Artificial Intelligence for Neuroscience: Behavioral Neurostimulation

Brain–Computer Interfaces: Taking Thoughts Out of the Human Body

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