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Ding K, Rakhshan M, Paredes-Acuña N, Cheng G, Thakor NV. Sensory integration for neuroprostheses: from functional benefits to neural correlates. Med Biol Eng Comput 2024:10.1007/s11517-024-03118-8. [PMID: 38760597 DOI: 10.1007/s11517-024-03118-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2023] [Accepted: 04/19/2024] [Indexed: 05/19/2024]
Abstract
In the field of sensory neuroprostheses, one ultimate goal is for individuals to perceive artificial somatosensory information and use the prosthesis with high complexity that resembles an intact system. To this end, research has shown that stimulation-elicited somatosensory information improves prosthesis perception and task performance. While studies strive to achieve sensory integration, a crucial phenomenon that entails naturalistic interaction with the environment, this topic has not been commensurately reviewed. Therefore, here we present a perspective for understanding sensory integration in neuroprostheses. First, we review the engineering aspects and functional outcomes in sensory neuroprosthesis studies. In this context, we summarize studies that have suggested sensory integration. We focus on how they have used stimulation-elicited percepts to maximize and improve the reliability of somatosensory information. Next, we review studies that have suggested multisensory integration. These works have demonstrated that congruent and simultaneous multisensory inputs provided cognitive benefits such that an individual experiences a greater sense of authority over prosthesis movements (i.e., agency) and perceives the prosthesis as part of their own (i.e., ownership). Thereafter, we present the theoretical and neuroscience framework of sensory integration. We investigate how behavioral models and neural recordings have been applied in the context of sensory integration. Sensory integration models developed from intact-limb individuals have led the way to sensory neuroprosthesis studies to demonstrate multisensory integration. Neural recordings have been used to show how multisensory inputs are processed across cortical areas. Lastly, we discuss some ongoing research and challenges in achieving and understanding sensory integration in sensory neuroprostheses. Resolving these challenges would help to develop future strategies to improve the sensory feedback of a neuroprosthetic system.
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Affiliation(s)
- Keqin Ding
- Department of Biomedical Engineering, Johns Hopkins School of Medicine, Baltimore, MD, 21205, USA.
| | - Mohsen Rakhshan
- Department of Electrical and Computer Engineering, University of Central Florida, Orlando, FL, 32816, USA
- Disability, Aging, and Technology Cluster, University of Central Florida, Orlando, FL, 32816, USA
| | - Natalia Paredes-Acuña
- Institute for Cognitive Systems, School of Computation, Information and Technology, Technical University of Munich, 80333, Munich, Germany
| | - Gordon Cheng
- Institute for Cognitive Systems, School of Computation, Information and Technology, Technical University of Munich, 80333, Munich, Germany
| | - Nitish V Thakor
- Department of Biomedical Engineering, Johns Hopkins School of Medicine, Baltimore, MD, 21205, USA
- Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD, 21205, USA
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Ades C, Abd MA, Hutchinson DT, Tognoli E, Du E, Wei J, Engeberg ED. Biohybrid Robotic Hand to Investigate Tactile Encoding and Sensorimotor Integration. Biomimetics (Basel) 2024; 9:78. [PMID: 38392124 PMCID: PMC10886511 DOI: 10.3390/biomimetics9020078] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2023] [Revised: 01/19/2024] [Accepted: 01/22/2024] [Indexed: 02/24/2024] Open
Abstract
For people who have experienced a spinal cord injury or an amputation, the recovery of sensation and motor control could be incomplete despite noteworthy advances with invasive neural interfaces. Our objective is to explore the feasibility of a novel biohybrid robotic hand model to investigate aspects of tactile sensation and sensorimotor integration with a pre-clinical research platform. Our new biohybrid model couples an artificial hand with biological neural networks (BNN) cultured in a multichannel microelectrode array (MEA). We decoded neural activity to control a finger of the artificial hand that was outfitted with a tactile sensor. The fingertip sensations were encoded into rapidly adapting (RA) or slowly adapting (SA) mechanoreceptor firing patterns that were used to electrically stimulate the BNN. We classified the coherence between afferent and efferent electrodes in the MEA with a convolutional neural network (CNN) using a transfer learning approach. The BNN exhibited the capacity for functional specialization with the RA and SA patterns, represented by significantly different robotic behavior of the biohybrid hand with respect to the tactile encoding method. Furthermore, the CNN was able to distinguish between RA and SA encoding methods with 97.84% ± 0.65% accuracy when the BNN was provided tactile feedback, averaged across three days in vitro (DIV). This novel biohybrid research platform demonstrates that BNNs are sensitive to tactile encoding methods and can integrate robotic tactile sensations with the motor control of an artificial hand. This opens the possibility of using biohybrid research platforms in the future to study aspects of neural interfaces with minimal human risk.
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Affiliation(s)
- Craig Ades
- Department of Ocean and Mechanical Engineering, Florida Atlantic University, Boca Raton, FL 33431, USA
| | - Moaed A Abd
- Department of Ocean and Mechanical Engineering, Florida Atlantic University, Boca Raton, FL 33431, USA
| | | | - Emmanuelle Tognoli
- Center for Complex Systems and Brain Sciences, Florida Atlantic University, Boca Raton, FL 33431, USA
| | - E Du
- Department of Ocean and Mechanical Engineering, Florida Atlantic University, Boca Raton, FL 33431, USA
- Department of Biomedical Engineering, Florida Atlantic University, Boca Raton, FL 33431, USA
| | - Jianning Wei
- Center for Complex Systems and Brain Sciences, Florida Atlantic University, Boca Raton, FL 33431, USA
- Department of Biomedical Science, Charles E. Schmidt College of Medicine, Florida Atlantic University, Boca Raton, FL 33431, USA
| | - Erik D Engeberg
- Department of Ocean and Mechanical Engineering, Florida Atlantic University, Boca Raton, FL 33431, USA
- Center for Complex Systems and Brain Sciences, Florida Atlantic University, Boca Raton, FL 33431, USA
- Department of Biomedical Engineering, Florida Atlantic University, Boca Raton, FL 33431, USA
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Donati E, Valle G. Neuromorphic hardware for somatosensory neuroprostheses. Nat Commun 2024; 15:556. [PMID: 38228580 PMCID: PMC10791662 DOI: 10.1038/s41467-024-44723-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2022] [Accepted: 01/03/2024] [Indexed: 01/18/2024] Open
Abstract
In individuals with sensory-motor impairments, missing limb functions can be restored using neuroprosthetic devices that directly interface with the nervous system. However, restoring the natural tactile experience through electrical neural stimulation requires complex encoding strategies. Indeed, they are presently limited in effectively conveying or restoring tactile sensations by bandwidth constraints. Neuromorphic technology, which mimics the natural behavior of neurons and synapses, holds promise for replicating the encoding of natural touch, potentially informing neurostimulation design. In this perspective, we propose that incorporating neuromorphic technologies into neuroprostheses could be an effective approach for developing more natural human-machine interfaces, potentially leading to advancements in device performance, acceptability, and embeddability. We also highlight ongoing challenges and the required actions to facilitate the future integration of these advanced technologies.
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Affiliation(s)
- Elisa Donati
- Institute of Neuroinformatics, University of Zurich and ETH Zurich, Zurich, Switzerland.
| | - Giacomo Valle
- Department of Organismal Biology and Anatomy, University of Chicago, Chicago, IL, USA.
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Várkuti B, Halász L, Hagh Gooie S, Miklós G, Smits Serena R, van Elswijk G, McIntyre CC, Lempka SF, Lozano AM, Erōss L. Conversion of a medical implant into a versatile computer-brain interface. Brain Stimul 2024; 17:39-48. [PMID: 38145752 DOI: 10.1016/j.brs.2023.12.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2023] [Revised: 12/19/2023] [Accepted: 12/20/2023] [Indexed: 12/27/2023] Open
Abstract
BACKGROUND Information transmission into the human nervous system is the basis for a variety of prosthetic applications. Spinal cord stimulation (SCS) systems are widely available, have a well documented safety record, can be implanted minimally invasively, and are known to stimulate afferent pathways. Nonetheless, SCS devices are not yet used for computer-brain-interfacing applications. OBJECTIVE Here we aimed to establish computer-to-brain communication via medical SCS implants in a group of 20 individuals who had been operated for the treatment of chronic neuropathic pain. METHODS In the initial phase, we conducted interface calibration with the aim of determining personalized stimulation settings that yielded distinct and reproducible sensations. These settings were subsequently utilized to generate inputs for a range of behavioral tasks. We evaluated the required calibration time, task training duration, and the subsequent performance in each task. RESULTS We could establish a stable spinal computer-brain interface in 18 of the 20 participants. Each of the 18 then performed one or more of the following tasks: A rhythm-discrimination task (n = 13), a Morse-decoding task (n = 3), and/or two different balance/body-posture tasks (n = 18; n = 5). The median calibration time was 79 min. The median training time for learning to use the interface in a subsequent task was 1:40 min. In each task, every participant demonstrated successful performance, surpassing chance levels. CONCLUSION The results constitute the first proof-of-concept of a general purpose computer-brain interface paradigm that could be deployed on present-day medical SCS platforms.
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Affiliation(s)
| | - László Halász
- Albert-Szentgyörgyi Medical School, Doctoral School of Clinical Medicine, Clinical and Experimental Research for Reconstructive and Organ-Sparing Surgery, University of Szeged, Szeged, Hungary
| | | | - Gabriella Miklós
- CereGate GmbH, München, Germany; National Institute of Mental Health, Neurology, and Neurosurgery, Budapest, Hungary; János Szentágothai Doctoral School of Neurosciences, Semmelweis University, Budapest, Hungary
| | - Ricardo Smits Serena
- CereGate GmbH, München, Germany; Department of Orthopaedics and Sports Orthopaedics, Klinikum Rechts der Isar, Technical University of Munich, München, Germany
| | | | - Cameron C McIntyre
- Department of Biomedical Engineering and Department of Neurosurgery, Duke University, Durham, NC, USA
| | - Scott F Lempka
- Department of Biomedical Engineering, Department of Anesthesiology and the Biointerfaces Institute, University of Michigan, Ann Arbor, MI, USA
| | - Andres M Lozano
- Division of Neurosurgery, Department of Surgery, University of Toronto, Toronto, Ontario, Canada
| | - Loránd Erōss
- National Institute of Mental Health, Neurology, and Neurosurgery, Budapest, Hungary
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Collu R, Paolini R, Bilotta M, Demofonti A, Cordella F, Zollo L, Barbaro M. Wearable High Voltage Compliant Current Stimulator for Restoring Sensory Feedback. MICROMACHINES 2023; 14:782. [PMID: 37421015 DOI: 10.3390/mi14040782] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/13/2023] [Revised: 03/28/2023] [Accepted: 03/29/2023] [Indexed: 07/09/2023]
Abstract
Transcutaneous Electrical Nerve Stimulation (TENS) is a promising technique for eliciting referred tactile sensations in patients with limb amputation. Although several studies show the validity of this technique, its application in daily life and away from laboratories is limited by the need for more portable instrumentation that guarantees the necessary voltage and current requirements for proper sensory stimulation. This study proposes a low-cost, wearable high-voltage compliant current stimulator with four independent channels based on Components-Off-The-Shelf (COTS). This microcontroller-based system implements a voltage-current converter controllable through a digital-to-analog converter that delivers up to 25 mA to load up to 3.6 kΩ. The high-voltage compliance enables the system to adapt to variations in electrode-skin impedance, allowing it to stimulate loads over 10 kΩ with currents of 5 mA. The system was realized on a four-layer PCB (115.9 mm × 61 mm, 52 g). The functionality of the device was tested on resistive loads and on an equivalent skin-like RC circuit. Moreover, the possibility of implementing an amplitude modulation was demonstrated.
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Affiliation(s)
- Riccardo Collu
- Department of Electrical and Electronics Engineering, University of Cagliari, Piazza D'Armi, 09123 Cagliari, Italy
| | - Roberto Paolini
- Research Unit of Advanced Robotics and Human-Centred Technologies (CREO Lab), Università Campus Bio-Medico di Roma, 00128 Rome, Italy
| | - Martina Bilotta
- Research Unit of Advanced Robotics and Human-Centred Technologies (CREO Lab), Università Campus Bio-Medico di Roma, 00128 Rome, Italy
| | - Andrea Demofonti
- Research Unit of Advanced Robotics and Human-Centred Technologies (CREO Lab), Università Campus Bio-Medico di Roma, 00128 Rome, Italy
| | - Francesca Cordella
- Research Unit of Advanced Robotics and Human-Centred Technologies (CREO Lab), Università Campus Bio-Medico di Roma, 00128 Rome, Italy
| | - Loredana Zollo
- Research Unit of Advanced Robotics and Human-Centred Technologies (CREO Lab), Università Campus Bio-Medico di Roma, 00128 Rome, Italy
| | - Massimo Barbaro
- Department of Electrical and Electronics Engineering, University of Cagliari, Piazza D'Armi, 09123 Cagliari, Italy
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