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Ghazizadeh E, Naseri Z, Deigner HP, Rahimi H, Altintas Z. Approaches of wearable and implantable biosensor towards of developing in precision medicine. Front Med (Lausanne) 2024; 11:1390634. [PMID: 39091290 PMCID: PMC11293309 DOI: 10.3389/fmed.2024.1390634] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2024] [Accepted: 04/30/2024] [Indexed: 08/04/2024] Open
Abstract
In the relentless pursuit of precision medicine, the intersection of cutting-edge technology and healthcare has given rise to a transformative era. At the forefront of this revolution stands the burgeoning field of wearable and implantable biosensors, promising a paradigm shift in how we monitor, analyze, and tailor medical interventions. As these miniature marvels seamlessly integrate with the human body, they weave a tapestry of real-time health data, offering unprecedented insights into individual physiological landscapes. This log embarks on a journey into the realm of wearable and implantable biosensors, where the convergence of biology and technology heralds a new dawn in personalized healthcare. Here, we explore the intricate web of innovations, challenges, and the immense potential these bioelectronics sentinels hold in sculpting the future of precision medicine.
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Affiliation(s)
- Elham Ghazizadeh
- Department of Bioinspired Materials and Biosensor Technologies, Faculty of Engineering, Institute of Materials Science, Kiel University, Kiel, Germany
- Department of Medical Biotechnology, School of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Zahra Naseri
- Department of Medical Biotechnology, School of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Hans-Peter Deigner
- Institute of Precision Medicine, Furtwangen University, Villingen-Schwenningen, Germany
- Fraunhofer Institute IZI (Leipzig), Rostock, Germany
- Faculty of Science, Eberhard-Karls-University Tuebingen, Tuebingen, Germany
| | - Hossein Rahimi
- Department of Medicine, University of Pittsburgh, Pittsburgh, PA, United States
| | - Zeynep Altintas
- Department of Bioinspired Materials and Biosensor Technologies, Faculty of Engineering, Institute of Materials Science, Kiel University, Kiel, Germany
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2
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Kelly AR, Glover DJ. Information Transmission through Biotic-Abiotic Interfaces to Restore or Enhance Human Function. ACS APPLIED BIO MATERIALS 2024; 7:3605-3628. [PMID: 38729914 DOI: 10.1021/acsabm.4c00435] [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] [Indexed: 05/12/2024]
Abstract
Advancements in reliable information transfer across biotic-abiotic interfaces have enabled the restoration of lost human function. For example, communication between neuronal cells and electrical devices restores the ability to walk to a tetraplegic patient and vision to patients blinded by retinal disease. These impactful medical achievements are aided by tailored biotic-abiotic interfaces that maximize information transfer fidelity by considering the physical properties of the underlying biological and synthetic components. This Review develops a modular framework to define and describe the engineering of biotic and abiotic components as well as the design of interfaces to facilitate biotic-abiotic information transfer using light or electricity. Delineating the properties of the biotic, interface, and abiotic components that enable communication can serve as a guide for future research in this highly interdisciplinary field. Application of synthetic biology to engineer light-sensitive proteins has facilitated the control of neural signaling and the restoration of rudimentary vision after retinal blindness. Electrophysiological methodologies that use brain-computer interfaces and stimulating implants to bypass spinal column injuries have led to the rehabilitation of limb movement and walking ability. Cellular interfacing methodologies and on-chip learning capability have been made possible by organic transistors that mimic the information processing capacity of neurons. The collaboration of molecular biologists, material scientists, and electrical engineers in the emerging field of biotic-abiotic interfacing will lead to the development of prosthetics capable of responding to thought and experiencing touch sensation via direct integration into the human nervous system. Further interdisciplinary research will improve electrical and optical interfacing technologies for the restoration of vision, offering greater visual acuity and potentially color vision in the near future.
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Affiliation(s)
- Alexander R Kelly
- School of Biotechnology and Biomolecular Sciences, University of New South Wales, Sydney, NSW 2052, Australia
| | - Dominic J Glover
- School of Biotechnology and Biomolecular Sciences, University of New South Wales, Sydney, NSW 2052, Australia
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3
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Wan Y, Wang C, Zhang B, Liu Y, Yang H, Liu F, Xu J, Xu S. Biocompatible Electrical and Optical Interfaces for Implantable Sensors and Devices. SENSORS (BASEL, SWITZERLAND) 2024; 24:3799. [PMID: 38931581 PMCID: PMC11207811 DOI: 10.3390/s24123799] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/06/2024] [Revised: 06/05/2024] [Accepted: 06/06/2024] [Indexed: 06/28/2024]
Abstract
Implantable bioelectronics hold tremendous potential in the field of healthcare, yet the performance of these systems heavily relies on the interfaces between artificial machines and living tissues. In this paper, we discuss the recent developments of tethered interfaces, as well as those of non-tethered interfaces. Among them, systems that study neural activity receive significant attention due to their innovative developments and high relevance in contemporary research, but other functional types of interface systems are also explored to provide a comprehensive overview of the field. We also analyze the key considerations, including perforation site selection, fixing strategies, long-term retention, and wireless communication, highlighting the challenges and opportunities with stable, effective, and biocompatible interfaces. Furthermore, we propose a primitive model of biocompatible electrical and optical interfaces for implantable systems, which simultaneously possesses biocompatibility, stability, and convenience. Finally, we point out the future directions of interfacing strategies.
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Affiliation(s)
- Yuxin Wan
- Key Laboratory for the Physics and Chemistry of Nanodevices, School of Electronics, Peking University, Beijing 100871, China
| | - Caiyi Wang
- School of Integrated Circuits, Shandong University, Jinan 250100, China (J.X.)
| | - Bingao Zhang
- Key Laboratory for the Physics and Chemistry of Nanodevices, School of Electronics, Peking University, Beijing 100871, China
| | - Yixuan Liu
- Key Laboratory for Neuroscience, Neuroscience Research Institute, Department of Neurobiology, School of Basic Medical Sciences, Ministry of Education and National Health Commission, Peking University, Beijing 100191, China (F.L.)
| | - Hailong Yang
- Key Laboratory for Neuroscience, Neuroscience Research Institute, Department of Neurobiology, School of Basic Medical Sciences, Ministry of Education and National Health Commission, Peking University, Beijing 100191, China (F.L.)
| | - Fengyu Liu
- Key Laboratory for Neuroscience, Neuroscience Research Institute, Department of Neurobiology, School of Basic Medical Sciences, Ministry of Education and National Health Commission, Peking University, Beijing 100191, China (F.L.)
| | - Jingjing Xu
- School of Integrated Circuits, Shandong University, Jinan 250100, China (J.X.)
| | - Shengyong Xu
- Key Laboratory for the Physics and Chemistry of Nanodevices, School of Electronics, Peking University, Beijing 100871, China
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Jung T, Zeng N, Fabbri JD, Eichler G, Li Z, Willeke K, Wingel KE, Dubey A, Huq R, Sharma M, Hu Y, Ramakrishnan G, Tien K, Mantovani P, Parihar A, Yin H, Oswalt D, Misdorp A, Uguz I, Shinn T, Rodriguez GJ, Nealley C, Gonzales I, Roukes M, Knecht J, Yoshor D, Canoll P, Spinazzi E, Carloni LP, Pesaran B, Patel S, Youngerman B, Cotton RJ, Tolias A, Shepard KL. Stable, chronic in-vivo recordings from a fully wireless subdural-contained 65,536-electrode brain-computer interface device. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.17.594333. [PMID: 38798494 PMCID: PMC11118429 DOI: 10.1101/2024.05.17.594333] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/29/2024]
Abstract
Minimally invasive, high-bandwidth brain-computer-interface (BCI) devices can revolutionize human applications. With orders-of-magnitude improvements in volumetric efficiency over other BCI technologies, we developed a 50-μm-thick, mechanically flexible micro-electrocorticography (μECoG) BCI, integrating 256×256 electrodes, signal processing, data telemetry, and wireless powering on a single complementary metal-oxide-semiconductor (CMOS) substrate containing 65,536 recording and 16,384 stimulation channels, from which we can simultaneously record up to 1024 channels at a given time. Fully implanted below the dura, our chip is wirelessly powered, communicating bi-directionally with an external relay station outside the body. We demonstrated chronic, reliable recordings for up to two weeks in pigs and up to two months in behaving non-human primates from somatosensory, motor, and visual cortices, decoding brain signals at high spatiotemporal resolution.
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Bhatia A, Hanna J, Stuart T, Kasper KA, Clausen DM, Gutruf P. Wireless Battery-free and Fully Implantable Organ Interfaces. Chem Rev 2024; 124:2205-2280. [PMID: 38382030 DOI: 10.1021/acs.chemrev.3c00425] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/23/2024]
Abstract
Advances in soft materials, miniaturized electronics, sensors, stimulators, radios, and battery-free power supplies are resulting in a new generation of fully implantable organ interfaces that leverage volumetric reduction and soft mechanics by eliminating electrochemical power storage. This device class offers the ability to provide high-fidelity readouts of physiological processes, enables stimulation, and allows control over organs to realize new therapeutic and diagnostic paradigms. Driven by seamless integration with connected infrastructure, these devices enable personalized digital medicine. Key to advances are carefully designed material, electrophysical, electrochemical, and electromagnetic systems that form implantables with mechanical properties closely matched to the target organ to deliver functionality that supports high-fidelity sensors and stimulators. The elimination of electrochemical power supplies enables control over device operation, anywhere from acute, to lifetimes matching the target subject with physical dimensions that supports imperceptible operation. This review provides a comprehensive overview of the basic building blocks of battery-free organ interfaces and related topics such as implantation, delivery, sterilization, and user acceptance. State of the art examples categorized by organ system and an outlook of interconnection and advanced strategies for computation leveraging the consistent power influx to elevate functionality of this device class over current battery-powered strategies is highlighted.
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Affiliation(s)
- Aman Bhatia
- Department of Biomedical Engineering, The University of Arizona, Tucson, Arizona 85721, United States
| | - Jessica Hanna
- Department of Biomedical Engineering, The University of Arizona, Tucson, Arizona 85721, United States
| | - Tucker Stuart
- Department of Biomedical Engineering, The University of Arizona, Tucson, Arizona 85721, United States
| | - Kevin Albert Kasper
- Department of Biomedical Engineering, The University of Arizona, Tucson, Arizona 85721, United States
| | - David Marshall Clausen
- Department of Biomedical Engineering, The University of Arizona, Tucson, Arizona 85721, United States
| | - Philipp Gutruf
- Department of Biomedical Engineering, The University of Arizona, Tucson, Arizona 85721, United States
- Department of Electrical and Computer Engineering, The University of Arizona, Tucson, Arizona 85721, United States
- Bio5 Institute, The University of Arizona, Tucson, Arizona 85721, United States
- Neuroscience Graduate Interdisciplinary Program (GIDP), The University of Arizona, Tucson, Arizona 85721, United States
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Ayyoubi AH, Fazli Besheli B, Quach MM, Gavvala JR, Goldman AM, Swamy CP, Bartoli E, Curry DJ, Sheth SA, Francis DJ, Ince NF. Benchmarking signal quality and spatiotemporal distribution of interictal spikes in prolonged human iEEG recordings using CorTec wireless brain interchange. Sci Rep 2024; 14:2652. [PMID: 38332136 PMCID: PMC10853182 DOI: 10.1038/s41598-024-52487-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2023] [Accepted: 01/19/2024] [Indexed: 02/10/2024] Open
Abstract
Neuromodulation through implantable pulse generators (IPGs) represents an important treatment approach for neurological disorders. While the field has observed the success of state-of-the-art interventions, such as deep brain stimulation (DBS) or responsive neurostimulation (RNS), implantable systems face various technical challenges, including the restriction of recording from a limited number of brain sites, power management, and limited external access to the assessed neural data in a continuous fashion. To the best of our knowledge, for the first time in this study, we investigated the feasibility of recording human intracranial EEG (iEEG) using a benchtop version of the Brain Interchange (BIC) unit of CorTec, which is a portable, wireless, and externally powered implant with sensing and stimulation capabilities. We developed a MATLAB/SIMULINK-based rapid prototyping environment and a graphical user interface (GUI) to acquire and visualize the iEEG captured from all 32 channels of the BIC unit. We recorded prolonged iEEG (~ 24 h) from three human subjects with externalized depth leads using the BIC and commercially available clinical amplifiers simultaneously in the epilepsy monitoring unit (EMU). The iEEG signal quality of both streams was compared, and the results demonstrated a comparable power spectral density (PSD) in all the systems in the low-frequency band (< 80 Hz). However, notable differences were primarily observed above 100 Hz, where the clinical amplifiers were associated with lower noise floor (BIC-17 dB vs. clinical amplifiers < - 25 dB). We employed an established spike detector to assess and compare the spike rates in each iEEG stream. We observed over 90% conformity between the spikes rates and their spatial distribution captured with BIC and clinical systems. Additionally, we quantified the packet loss characteristic in the iEEG signal during the wireless data transfer and conducted a series of simulations to compare the performance of different interpolation methods for recovering the missing packets in signals at different frequency bands. We noted that simple linear interpolation has the potential to recover the signal and reduce the noise floor with modest packet loss levels reaching up to 10%. Overall, our results indicate that while tethered clinical amplifiers exhibited noticeably better noise floor above 80 Hz, epileptic spikes can still be detected successfully in the iEEG recorded with the externally powered wireless BIC unit opening the road for future closed-loop neuromodulation applications with continuous access to brain activity.
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Affiliation(s)
- Amir Hossein Ayyoubi
- Department of Biomedical Engineering, University of Houston, Houston, TX, USA
- Department of Neurologic Surgery, Mayo Clinic, Rochester, MN, USA
| | - Behrang Fazli Besheli
- Department of Biomedical Engineering, University of Houston, Houston, TX, USA
- Department of Neurologic Surgery, Mayo Clinic, Rochester, MN, USA
| | - Michael M Quach
- Department of Neurology, Texas Children's Hospital, Houston, TX, USA
| | | | - Alica M Goldman
- Department of Neurology, Baylor College of Medicine, Houston, TX, USA
| | | | - Eleonora Bartoli
- Department of Neurosurgery, Baylor College of Medicine, Houston, TX, USA
| | - Daniel J Curry
- Department of Neurosurgery, Texas Children's Hospital, Houston, TX, USA
| | - Sameer A Sheth
- Department of Neurosurgery, Baylor College of Medicine, Houston, TX, USA
| | - David J Francis
- Department of Psychology, University of Houston, Houston, TX, USA
| | - Nuri F Ince
- Department of Biomedical Engineering, University of Houston, Houston, TX, USA.
- Department of Neurologic Surgery, Mayo Clinic, Rochester, MN, USA.
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7
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Nair V, Dalrymple AN, Yu Z, Balakrishnan G, Bettinger CJ, Weber DJ, Yang K, Robinson JT. Miniature battery-free bioelectronics. Science 2023; 382:eabn4732. [PMID: 37943926 DOI: 10.1126/science.abn4732] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2022] [Accepted: 09/28/2023] [Indexed: 11/12/2023]
Abstract
Miniature wireless bioelectronic implants that can operate for extended periods of time can transform how we treat disorders by acting rapidly on precise nerves and organs in a way that drugs cannot. To reach this goal, materials and methods are needed to wirelessly transfer energy through the body or harvest energy from the body itself. We review some of the capabilities of emerging energy transfer methods to identify the performance envelope for existing technology and discover where opportunities lie to improve how much-and how efficiently-we can deliver energy to the tiny bioelectronic implants that can support emerging medical technologies.
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Affiliation(s)
- Vishnu Nair
- Rice Neuroengineering Initiative, Rice University, Houston, TX, USA
| | - Ashley N Dalrymple
- Department of Mechanical Engineering, Carnegie Mellon University, Pittsburgh, PA, USA
- Department of Biomedical Engineering, University of Utah, Salt Lake City, UT, USA
- Department of Physical Medicine and Rehabilitation, University of Utah, Salt Lake City, UT, USA
| | - Zhanghao Yu
- Department of Electrical and Computer Engineering, Rice University, Houston, TX, USA
| | - Gaurav Balakrishnan
- Department of Materials Science & Engineering, Carnegie Mellon University, Pittsburgh, PA, USA
| | - Christopher J Bettinger
- Department of Materials Science & Engineering, Carnegie Mellon University, Pittsburgh, PA, USA
| | - Douglas J Weber
- Department of Mechanical Engineering, Carnegie Mellon University, Pittsburgh, PA, USA
- Neuroscience Institute, Carnegie Mellon University, Pittsburgh, PA, USA
- Center for Neural Basis of Cognition, Pittsburgh, PA, USA
| | - Kaiyuan Yang
- Department of Electrical and Computer Engineering, Rice University, Houston, TX, USA
| | - Jacob T Robinson
- Rice Neuroengineering Initiative, Rice University, Houston, TX, USA
- Department of Electrical and Computer Engineering, Rice University, Houston, TX, USA
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8
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Yan T, Suzuki K, Kameda S, Maeda M, Mihara T, Hirata M. Chronic subdural electrocorticography in nonhuman primates by an implantable wireless device for brain-machine interfaces. Front Neurosci 2023; 17:1260675. [PMID: 37841689 PMCID: PMC10568031 DOI: 10.3389/fnins.2023.1260675] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2023] [Accepted: 09/12/2023] [Indexed: 10/17/2023] Open
Abstract
Background Subdural electrocorticography (ECoG) signals have been proposed as a stable, good-quality source for brain-machine interfaces (BMIs), with a higher spatial and temporal resolution than electroencephalography (EEG). However, long-term implantation may lead to chronic inflammatory reactions and connective tissue encapsulation, resulting in a decline in signal recording quality. However, no study has reported the effects of the surrounding tissue on signal recording and device functionality thus far. Methods In this study, we implanted a wireless recording device with a customized 32-electrode-ECoG array subdurally in two nonhuman primates for 15 months. We evaluated the neural activities recorded from and wirelessly transmitted to the devices and the chronic tissue reactions around the electrodes. In addition, we measured the gain factor of the newly formed ventral fibrous tissue in vivo. Results Time-frequency analyses of the acute and chronic phases showed similar signal features. The average root mean square voltage and power spectral density showed relatively stable signal quality after chronic implantation. Histological examination revealed thickening of the reactive tissue around the electrode array; however, no evident inflammation in the cortex. From gain factor analysis, we found that tissue proliferation under electrodes reduced the amplitude power of signals. Conclusion This study suggests that subdural ECoG may provide chronic signal recordings for future clinical applications and neuroscience research. This study also highlights the need to reduce proliferation of reactive tissue ventral to the electrodes to enhance long-term stability.
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Affiliation(s)
- Tianfang Yan
- Department of Neurological Diagnosis and Restoration, Osaka University Graduate School of Medicine, Suita, Japan
| | | | - Seiji Kameda
- Department of Neurological Diagnosis and Restoration, Osaka University Graduate School of Medicine, Suita, Japan
| | - Masashi Maeda
- Candidate Discovery Science Labs, Astellas Pharma Inc., Tokyo, Japan
| | - Takuma Mihara
- Candidate Discovery Science Labs, Astellas Pharma Inc., Tokyo, Japan
| | - Masayuki Hirata
- Department of Neurological Diagnosis and Restoration, Osaka University Graduate School of Medicine, Suita, Japan
- Department of Neurosurgery, Osaka University Graduate School of Medicine, Suita, Japan
- Global Center for Medical Engineering and Informatics, Osaka University, Suita, Japan
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Lorach H, Galvez A, Spagnolo V, Martel F, Karakas S, Intering N, Vat M, Faivre O, Harte C, Komi S, Ravier J, Collin T, Coquoz L, Sakr I, Baaklini E, Hernandez-Charpak SD, Dumont G, Buschman R, Buse N, Denison T, van Nes I, Asboth L, Watrin A, Struber L, Sauter-Starace F, Langar L, Auboiroux V, Carda S, Chabardes S, Aksenova T, Demesmaeker R, Charvet G, Bloch J, Courtine G. Walking naturally after spinal cord injury using a brain-spine interface. Nature 2023; 618:126-133. [PMID: 37225984 PMCID: PMC10232367 DOI: 10.1038/s41586-023-06094-5] [Citation(s) in RCA: 87] [Impact Index Per Article: 87.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2022] [Accepted: 04/17/2023] [Indexed: 05/26/2023]
Abstract
A spinal cord injury interrupts the communication between the brain and the region of the spinal cord that produces walking, leading to paralysis1,2. Here, we restored this communication with a digital bridge between the brain and spinal cord that enabled an individual with chronic tetraplegia to stand and walk naturally in community settings. This brain-spine interface (BSI) consists of fully implanted recording and stimulation systems that establish a direct link between cortical signals3 and the analogue modulation of epidural electrical stimulation targeting the spinal cord regions involved in the production of walking4-6. A highly reliable BSI is calibrated within a few minutes. This reliability has remained stable over one year, including during independent use at home. The participant reports that the BSI enables natural control over the movements of his legs to stand, walk, climb stairs and even traverse complex terrains. Moreover, neurorehabilitation supported by the BSI improved neurological recovery. The participant regained the ability to walk with crutches overground even when the BSI was switched off. This digital bridge establishes a framework to restore natural control of movement after paralysis.
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Affiliation(s)
- Henri Lorach
- NeuroX Institute, School of Life Sciences, Ecole Polytechnique Fédérale de Lausanne (EPFL), Geneva, Switzerland
- Department of Clinical Neuroscience, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland
- NeuroRestore, Defitech Center for Interventional Neurotherapies, EPFL/CHUV/UNIL, Lausanne, Switzerland
| | - Andrea Galvez
- NeuroX Institute, School of Life Sciences, Ecole Polytechnique Fédérale de Lausanne (EPFL), Geneva, Switzerland
- Department of Clinical Neuroscience, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland
- NeuroRestore, Defitech Center for Interventional Neurotherapies, EPFL/CHUV/UNIL, Lausanne, Switzerland
| | - Valeria Spagnolo
- NeuroX Institute, School of Life Sciences, Ecole Polytechnique Fédérale de Lausanne (EPFL), Geneva, Switzerland
- Department of Clinical Neuroscience, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland
- NeuroRestore, Defitech Center for Interventional Neurotherapies, EPFL/CHUV/UNIL, Lausanne, Switzerland
| | - Felix Martel
- Univ. Grenoble Alpes, CEA, LETI, Clinatec, Grenoble, France
| | - Serpil Karakas
- Univ. Grenoble Alpes, CEA, LETI, Clinatec, Grenoble, France
| | - Nadine Intering
- NeuroX Institute, School of Life Sciences, Ecole Polytechnique Fédérale de Lausanne (EPFL), Geneva, Switzerland
- Department of Clinical Neuroscience, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland
- NeuroRestore, Defitech Center for Interventional Neurotherapies, EPFL/CHUV/UNIL, Lausanne, Switzerland
| | - Molywan Vat
- NeuroX Institute, School of Life Sciences, Ecole Polytechnique Fédérale de Lausanne (EPFL), Geneva, Switzerland
- Department of Clinical Neuroscience, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland
- NeuroRestore, Defitech Center for Interventional Neurotherapies, EPFL/CHUV/UNIL, Lausanne, Switzerland
| | - Olivier Faivre
- Univ. Grenoble Alpes, CEA, LETI, Clinatec, Grenoble, France
| | - Cathal Harte
- NeuroX Institute, School of Life Sciences, Ecole Polytechnique Fédérale de Lausanne (EPFL), Geneva, Switzerland
- Department of Clinical Neuroscience, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland
- NeuroRestore, Defitech Center for Interventional Neurotherapies, EPFL/CHUV/UNIL, Lausanne, Switzerland
| | - Salif Komi
- NeuroX Institute, School of Life Sciences, Ecole Polytechnique Fédérale de Lausanne (EPFL), Geneva, Switzerland
- Department of Clinical Neuroscience, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland
- NeuroRestore, Defitech Center for Interventional Neurotherapies, EPFL/CHUV/UNIL, Lausanne, Switzerland
| | - Jimmy Ravier
- NeuroX Institute, School of Life Sciences, Ecole Polytechnique Fédérale de Lausanne (EPFL), Geneva, Switzerland
- Department of Clinical Neuroscience, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland
- NeuroRestore, Defitech Center for Interventional Neurotherapies, EPFL/CHUV/UNIL, Lausanne, Switzerland
| | - Thibault Collin
- NeuroX Institute, School of Life Sciences, Ecole Polytechnique Fédérale de Lausanne (EPFL), Geneva, Switzerland
- Department of Clinical Neuroscience, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland
- NeuroRestore, Defitech Center for Interventional Neurotherapies, EPFL/CHUV/UNIL, Lausanne, Switzerland
| | - Laure Coquoz
- NeuroX Institute, School of Life Sciences, Ecole Polytechnique Fédérale de Lausanne (EPFL), Geneva, Switzerland
- Department of Clinical Neuroscience, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland
- NeuroRestore, Defitech Center for Interventional Neurotherapies, EPFL/CHUV/UNIL, Lausanne, Switzerland
| | - Icare Sakr
- NeuroX Institute, School of Life Sciences, Ecole Polytechnique Fédérale de Lausanne (EPFL), Geneva, Switzerland
- Department of Clinical Neuroscience, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland
- NeuroRestore, Defitech Center for Interventional Neurotherapies, EPFL/CHUV/UNIL, Lausanne, Switzerland
| | - Edeny Baaklini
- NeuroX Institute, School of Life Sciences, Ecole Polytechnique Fédérale de Lausanne (EPFL), Geneva, Switzerland
- Department of Clinical Neuroscience, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland
- NeuroRestore, Defitech Center for Interventional Neurotherapies, EPFL/CHUV/UNIL, Lausanne, Switzerland
| | - Sergio Daniel Hernandez-Charpak
- NeuroX Institute, School of Life Sciences, Ecole Polytechnique Fédérale de Lausanne (EPFL), Geneva, Switzerland
- Department of Clinical Neuroscience, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland
- NeuroRestore, Defitech Center for Interventional Neurotherapies, EPFL/CHUV/UNIL, Lausanne, Switzerland
| | - Gregory Dumont
- NeuroX Institute, School of Life Sciences, Ecole Polytechnique Fédérale de Lausanne (EPFL), Geneva, Switzerland
- Department of Clinical Neuroscience, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland
- NeuroRestore, Defitech Center for Interventional Neurotherapies, EPFL/CHUV/UNIL, Lausanne, Switzerland
| | | | | | - Tim Denison
- Medtronic, Minneapolis, MN, USA
- Department of Engineering Science, University of Oxford, Oxford, UK
| | - Ilse van Nes
- Department of Rehabilitation, Sint Maartenskliniek, Nijmegen, the Netherlands
| | - Leonie Asboth
- NeuroX Institute, School of Life Sciences, Ecole Polytechnique Fédérale de Lausanne (EPFL), Geneva, Switzerland
- Department of Clinical Neuroscience, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland
- NeuroRestore, Defitech Center for Interventional Neurotherapies, EPFL/CHUV/UNIL, Lausanne, Switzerland
| | | | - Lucas Struber
- Univ. Grenoble Alpes, CEA, LETI, Clinatec, Grenoble, France
| | | | - Lilia Langar
- Univ. Grenoble Alpes, CHU Grenoble Alpes, Clinatec, Grenoble, France
| | | | - Stefano Carda
- Department of Clinical Neuroscience, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland
| | - Stephan Chabardes
- Univ. Grenoble Alpes, CEA, LETI, Clinatec, Grenoble, France
- Univ. Grenoble Alpes, CHU Grenoble Alpes, Clinatec, Grenoble, France
| | | | - Robin Demesmaeker
- NeuroX Institute, School of Life Sciences, Ecole Polytechnique Fédérale de Lausanne (EPFL), Geneva, Switzerland
- Department of Clinical Neuroscience, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland
- NeuroRestore, Defitech Center for Interventional Neurotherapies, EPFL/CHUV/UNIL, Lausanne, Switzerland
| | | | - Jocelyne Bloch
- NeuroX Institute, School of Life Sciences, Ecole Polytechnique Fédérale de Lausanne (EPFL), Geneva, Switzerland.
- Department of Clinical Neuroscience, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland.
- NeuroRestore, Defitech Center for Interventional Neurotherapies, EPFL/CHUV/UNIL, Lausanne, Switzerland.
| | - Grégoire Courtine
- NeuroX Institute, School of Life Sciences, Ecole Polytechnique Fédérale de Lausanne (EPFL), Geneva, Switzerland.
- Department of Clinical Neuroscience, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland.
- NeuroRestore, Defitech Center for Interventional Neurotherapies, EPFL/CHUV/UNIL, Lausanne, Switzerland.
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10
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Branco MP, Geukes SH, Aarnoutse EJ, Ramsey NF, Vansteensel MJ. Nine decades of electrocorticography: A comparison between epidural and subdural recordings. Eur J Neurosci 2023; 57:1260-1288. [PMID: 36843389 DOI: 10.1111/ejn.15941] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2022] [Revised: 02/10/2023] [Accepted: 02/18/2023] [Indexed: 02/28/2023]
Abstract
In recent years, electrocorticography (ECoG) has arisen as a neural signal recording tool in the development of clinically viable neural interfaces. ECoG electrodes are generally placed below the dura mater (subdural) but can also be placed on top of the dura (epidural). In deciding which of these modalities best suits long-term implants, complications and signal quality are important considerations. Conceptually, epidural placement may present a lower risk of complications as the dura is left intact but also a lower signal quality due to the dura acting as a signal attenuator. The extent to which complications and signal quality are affected by the dura, however, has been a matter of debate. To improve our understanding of the effects of the dura on complications and signal quality, we conducted a literature review. We inventorized the effect of the dura on signal quality, decodability and longevity of acute and chronic ECoG recordings in humans and non-human primates. Also, we compared the incidence and nature of serious complications in studies that employed epidural and subdural ECoG. Overall, we found that, even though epidural recordings exhibit attenuated signal amplitude over subdural recordings, particularly for high-density grids, the decodability of epidural recorded signals does not seem to be markedly affected. Additionally, we found that the nature of serious complications was comparable between epidural and subdural recordings. These results indicate that both epidural and subdural ECoG may be suited for long-term neural signal recordings, at least for current generations of clinical and high-density ECoG grids.
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Affiliation(s)
- Mariana P Branco
- Department of Neurology and Neurosurgery, University Medical Center Utrecht Brain Center, Utrecht University, Utrecht, The Netherlands
| | - Simon H Geukes
- Department of Neurology and Neurosurgery, University Medical Center Utrecht Brain Center, Utrecht University, Utrecht, The Netherlands
| | - Erik J Aarnoutse
- Department of Neurology and Neurosurgery, University Medical Center Utrecht Brain Center, Utrecht University, Utrecht, The Netherlands
| | - Nick F Ramsey
- Department of Neurology and Neurosurgery, University Medical Center Utrecht Brain Center, Utrecht University, Utrecht, The Netherlands
| | - Mariska J Vansteensel
- Department of Neurology and Neurosurgery, University Medical Center Utrecht Brain Center, Utrecht University, Utrecht, The Netherlands
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11
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Śliwowski M, Martin M, Souloumiac A, Blanchart P, Aksenova T. Impact of dataset size and long-term ECoG-based BCI usage on deep learning decoders performance. Front Hum Neurosci 2023; 17:1111645. [PMID: 37007675 PMCID: PMC10061076 DOI: 10.3389/fnhum.2023.1111645] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2022] [Accepted: 02/27/2023] [Indexed: 03/18/2023] Open
Abstract
IntroductionIn brain-computer interfaces (BCI) research, recording data is time-consuming and expensive, which limits access to big datasets. This may influence the BCI system performance as machine learning methods depend strongly on the training dataset size. Important questions arise: taking into account neuronal signal characteristics (e.g., non-stationarity), can we achieve higher decoding performance with more data to train decoders? What is the perspective for further improvement with time in the case of long-term BCI studies? In this study, we investigated the impact of long-term recordings on motor imagery decoding from two main perspectives: model requirements regarding dataset size and potential for patient adaptation.MethodsWe evaluated the multilinear model and two deep learning (DL) models on a long-term BCI & Tetraplegia (ClinicalTrials.gov identifier: NCT02550522) clinical trial dataset containing 43 sessions of ECoG recordings performed with a tetraplegic patient. In the experiment, a participant executed 3D virtual hand translation using motor imagery patterns. We designed multiple computational experiments in which training datasets were increased or translated to investigate the relationship between models' performance and different factors influencing recordings.ResultsOur results showed that DL decoders showed similar requirements regarding the dataset size compared to the multilinear model while demonstrating higher decoding performance. Moreover, high decoding performance was obtained with relatively small datasets recorded later in the experiment, suggesting motor imagery patterns improvement and patient adaptation during the long-term experiment. Finally, we proposed UMAP embeddings and local intrinsic dimensionality as a way to visualize the data and potentially evaluate data quality.DiscussionDL-based decoding is a prospective approach in BCI which may be efficiently applied with real-life dataset size. Patient-decoder co-adaptation is an important factor to consider in long-term clinical BCI.
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Affiliation(s)
- Maciej Śliwowski
- Université Grenoble Alpes, CEA, LETI, Clinatec, Grenoble, France
- Université Paris-Saclay, CEA, List, Palaiseau, France
| | - Matthieu Martin
- Université Grenoble Alpes, CEA, LETI, Clinatec, Grenoble, France
| | | | | | - Tetiana Aksenova
- Université Grenoble Alpes, CEA, LETI, Clinatec, Grenoble, France
- *Correspondence: Tetiana Aksenova
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12
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Fekete Z, Zátonyi A, Kaszás A, Madarász M, Slézia A. Transparent neural interfaces: challenges and solutions of microengineered multimodal implants designed to measure intact neuronal populations using high-resolution electrophysiology and microscopy simultaneously. MICROSYSTEMS & NANOENGINEERING 2023; 9:66. [PMID: 37213820 PMCID: PMC10195795 DOI: 10.1038/s41378-023-00519-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/07/2022] [Revised: 02/03/2023] [Accepted: 03/01/2023] [Indexed: 05/23/2023]
Abstract
The aim of this review is to present a comprehensive overview of the feasibility of using transparent neural interfaces in multimodal in vivo experiments on the central nervous system. Multimodal electrophysiological and neuroimaging approaches hold great potential for revealing the anatomical and functional connectivity of neuronal ensembles in the intact brain. Multimodal approaches are less time-consuming and require fewer experimental animals as researchers obtain denser, complex data during the combined experiments. Creating devices that provide high-resolution, artifact-free neural recordings while facilitating the interrogation or stimulation of underlying anatomical features is currently one of the greatest challenges in the field of neuroengineering. There are numerous articles highlighting the trade-offs between the design and development of transparent neural interfaces; however, a comprehensive overview of the efforts in material science and technology has not been reported. Our present work fills this gap in knowledge by introducing the latest micro- and nanoengineered solutions for fabricating substrate and conductive components. Here, the limitations and improvements in electrical, optical, and mechanical properties, the stability and longevity of the integrated features, and biocompatibility during in vivo use are discussed.
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Affiliation(s)
- Z. Fekete
- Research Group for Implantable Microsystems, Faculty of Information Technology & Bionics, Pázmány Péter Catholic University, Budapest, Hungary
- Institute of Cognitive Neuroscience & Psychology, Eotvos Lorand Research Network, Budapest, Hungary
| | - A. Zátonyi
- Research Group for Implantable Microsystems, Faculty of Information Technology & Bionics, Pázmány Péter Catholic University, Budapest, Hungary
| | - A. Kaszás
- Mines Saint-Etienne, Centre CMP, Département BEL, F - 13541 Gardanne, France
- Institut de Neurosciences de la Timone, CNRS UMR 7289 & Aix-Marseille Université, 13005 Marseille, France
| | - M. Madarász
- János Szentágothai PhD Program of Semmelweis University, Budapest, Hungary
- BrainVision Center, Budapest, Hungary
| | - A. Slézia
- Institut de Neurosciences de la Timone, CNRS UMR 7289 & Aix-Marseille Université, 13005 Marseille, France
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13
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Rouanne V, Costecalde T, Benabid AL, Aksenova T. Unsupervised adaptation of an ECoG based brain-computer interface using neural correlates of task performance. Sci Rep 2022; 12:21316. [PMID: 36494390 PMCID: PMC9734147 DOI: 10.1038/s41598-022-25049-w] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2022] [Accepted: 11/23/2022] [Indexed: 12/13/2022] Open
Abstract
Brain-computer interfaces (BCIs) translate brain signals into commands to external effectors, and mainly target severely disabled users. The usability of BCIs may be improved by reducing their major constraints, such as the necessity for special training sessions to initially calibrate and later keep up to date the neural signal decoders. In this study, we show that it is possible to train and update BCI decoders during free use of motor BCIs. In addition to the neural signal decoder controlling effectors (control decoder), one more classifier is proposed to detect neural correlates of BCI motor task performances (MTP). MTP decoders reveal whether the actions performed by BCI effectors matched the user's intentions. The combined outputs of MTP and control decoders allow forming training datasets to update the control decoder online and in real time during free use of BCIs. The usability of the proposed auto-adaptive BCI (aaBCI) is demonstrated for two principle BCIs paradigms: with discrete outputs (4 classes BCI, virtual 4-limb exoskeleton), and with continuous outputs (cursor 2D control). The proof of concept was performed in an online simulation study using an ECoG dataset collected from a tetraplegic during a BCI clinical trial. The control decoder reached a multiclass area under the ROC curve of 0.7404 using aaBCI, compared to a chance level of 0.5173 and to 0.8187 for supervised training for the multiclass BCI, and a cosine similarity of 0.1211 using aaBCI, compared to a chance level of 0.0036 and to 0.2002 for supervised training for the continuous BCI.
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Affiliation(s)
- Vincent Rouanne
- grid.457348.90000 0004 0630 1517Univ. Grenoble Alpes, CEA, LETI, Clinatec, 38000 Grenoble, France
| | - Thomas Costecalde
- grid.457348.90000 0004 0630 1517Univ. Grenoble Alpes, CEA, LETI, Clinatec, 38000 Grenoble, France
| | - Alim Louis Benabid
- grid.457348.90000 0004 0630 1517Univ. Grenoble Alpes, CEA, LETI, Clinatec, 38000 Grenoble, France ,grid.410529.b0000 0001 0792 4829CHU Grenoble Alpes, Grenoble, France
| | - Tetiana Aksenova
- grid.457348.90000 0004 0630 1517Univ. Grenoble Alpes, CEA, LETI, Clinatec, 38000 Grenoble, France
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14
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Girão AF, Serrano MC, Completo A, Marques PAAP. Is Graphene Shortening the Path toward Spinal Cord Regeneration? ACS NANO 2022; 16:13430-13467. [PMID: 36000717 PMCID: PMC9776589 DOI: 10.1021/acsnano.2c04756] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/09/2023]
Abstract
Along with the development of the next generation of biomedical platforms, the inclusion of graphene-based materials (GBMs) into therapeutics for spinal cord injury (SCI) has potential to nourish topmost neuroprotective and neuroregenerative strategies for enhancing neural structural and physiological recovery. In the context of SCI, contemplated as one of the most convoluted challenges of modern medicine, this review first provides an overview of its characteristics and pathophysiological features. Then, the most relevant ongoing clinical trials targeting SCI, including pharmaceutical, robotics/neuromodulation, and scaffolding approaches, are introduced and discussed in sequence with the most important insights brought by GBMs into each particular topic. The current role of these nanomaterials on restoring the spinal cord microenvironment after injury is critically contextualized, while proposing future concepts and desirable outputs for graphene-based technologies aiming to reach clinical significance for SCI.
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Affiliation(s)
- André F. Girão
- Centre
for Mechanical Technology and Automation (TEMA), Department of Mechanical
Engineering, University of Aveiro (UA), Aveiro, 3810-193, Portugal
- Instituto
de Ciencia de Materiales de Madrid (ICMM), Consejo Superior de Investigaciones Científicas (CSIC), Calle Sor Juana Inés de la
Cruz 3, Madrid, 28049, Spain
- (A.F.G.)
| | - María Concepcion Serrano
- Instituto
de Ciencia de Materiales de Madrid (ICMM), Consejo Superior de Investigaciones Científicas (CSIC), Calle Sor Juana Inés de la
Cruz 3, Madrid, 28049, Spain
- (M.C.S.)
| | - António Completo
- Centre
for Mechanical Technology and Automation (TEMA), Department of Mechanical
Engineering, University of Aveiro (UA), Aveiro, 3810-193, Portugal
| | - Paula A. A. P. Marques
- Centre
for Mechanical Technology and Automation (TEMA), Department of Mechanical
Engineering, University of Aveiro (UA), Aveiro, 3810-193, Portugal
- (P.A.A.P.M.)
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15
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Wang H, Ma Q, Chen K, Zhang H, Yang Y, Zheng N, Hong H. An Ultra-Low-Noise, Low Power and Miniaturized Dual-Channel Wireless Neural Recording Microsystem. BIOSENSORS 2022; 12:bios12080613. [PMID: 36005009 PMCID: PMC9405808 DOI: 10.3390/bios12080613] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/24/2022] [Revised: 08/01/2022] [Accepted: 08/04/2022] [Indexed: 12/04/2022]
Abstract
As the basic tools for neuroscience research, invasive neural recording devices can obtain high-resolution neuronal activity signals through electrodes connected to the subject’s brain. Existing wireless neural recording devices are large in size or need external large-scale equipment for wireless power supply, which limits their application. Here, we developed an ultra-low-noise, low power and miniaturized dual-channel wireless neural recording microsystem. With the full-differential front-end structure of the dual operational amplifiers (op-amps), the noise level and power consumption are notably reduced. The hierarchical microassembly technology, which integrates wafer-level packaged op-amps and the miniaturized Bluetooth module, dramatically reduces the size of the wireless neural recording microsystem. The microsystem shows a less than 100 nV/Hz ultra-low noise level, about 10 mW low power consumption, and 9 × 7 × 5 mm3 small size. The neural recording ability was then demonstrated in saline and a chronic rat model. Because of its miniaturization, it can be applied to freely behaving small animals, such as rats. Its features of ultra-low noise and high bandwidth are conducive to low-amplitude neural signal recording, which may help advance neuroscientific discovery.
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Affiliation(s)
- Haochuan Wang
- Key Laboratory of Radio Frequency Circuit and System, Hangzhou Dianzi University, Hangzhou 310018, China
| | - Qian Ma
- Qiushi Academy for Advanced Studies, Zhejiang University, Hangzhou 310027, China
- College of Computer Science and Technology, Zhejiang University, Hangzhou 310027, China
| | - Keming Chen
- Key Laboratory of Radio Frequency Circuit and System, Hangzhou Dianzi University, Hangzhou 310018, China
| | - Hanqing Zhang
- Key Laboratory of Radio Frequency Circuit and System, Hangzhou Dianzi University, Hangzhou 310018, China
| | - Yinyan Yang
- Key Laboratory of Radio Frequency Circuit and System, Hangzhou Dianzi University, Hangzhou 310018, China
| | - Nenggan Zheng
- Qiushi Academy for Advanced Studies, Zhejiang University, Hangzhou 310027, China
- College of Computer Science and Technology, Zhejiang University, Hangzhou 310027, China
- Correspondence: (N.Z.); (H.H.)
| | - Hui Hong
- Key Laboratory of Radio Frequency Circuit and System, Hangzhou Dianzi University, Hangzhou 310018, China
- Correspondence: (N.Z.); (H.H.)
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16
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Mariello M, Kim K, Wu K, Lacour SP, Leterrier Y. Recent Advances in Encapsulation of Flexible Bioelectronic Implants: Materials, Technologies, and Characterization Methods. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2022; 34:e2201129. [PMID: 35353928 DOI: 10.1002/adma.202201129] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/03/2022] [Revised: 03/13/2022] [Indexed: 06/14/2023]
Abstract
Bioelectronic implantable systems (BIS) targeting biomedical and clinical research should combine long-term performance and biointegration in vivo. Here, recent advances in novel encapsulations to protect flexible versions of such systems from the surrounding biological environment are reviewed, focusing on material strategies and synthesis techniques. Considerable effort is put on thin-film encapsulation (TFE), and specifically organic-inorganic multilayer architectures as a flexible and conformal alternative to conventional rigid cans. TFE is in direct contact with the biological medium and thus must exhibit not only biocompatibility, inertness, and hermeticity but also mechanical robustness, conformability, and compatibility with the manufacturing of microfabricated devices. Quantitative characterization methods of the barrier and mechanical performance of the TFE are reviewed with a particular emphasis on water-vapor transmission rate through electrical, optical, or electrochemical principles. The integrability and functionalization of TFE into functional bioelectronic interfaces are also discussed. TFE represents a must-have component for the next-generation bioelectronic implants with diagnostic or therapeutic functions in human healthcare and precision medicine.
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Affiliation(s)
- Massimo Mariello
- Laboratory for Processing of Advanced Composites (LPAC), École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, CH-1015, Switzerland
| | - Kyungjin Kim
- Bertarelli Foundation Chair in Neuroprosthetic Technology, Laboratory for Soft Bioelectronic Interfaces, Institute of Electrical and MicroEngineering, Institute of Bioengineering, Centre for Neuroprosthetics, École Polytechnique Fédérale de Lausanne, Geneva, Switzerland
| | - Kangling Wu
- Bertarelli Foundation Chair in Neuroprosthetic Technology, Laboratory for Soft Bioelectronic Interfaces, Institute of Electrical and MicroEngineering, Institute of Bioengineering, Centre for Neuroprosthetics, École Polytechnique Fédérale de Lausanne, Geneva, Switzerland
| | - Stéphanie P Lacour
- Bertarelli Foundation Chair in Neuroprosthetic Technology, Laboratory for Soft Bioelectronic Interfaces, Institute of Electrical and MicroEngineering, Institute of Bioengineering, Centre for Neuroprosthetics, École Polytechnique Fédérale de Lausanne, Geneva, Switzerland
| | - Yves Leterrier
- Laboratory for Processing of Advanced Composites (LPAC), École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, CH-1015, Switzerland
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17
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Śliwowski M, Martin M, Souloumiac A, Blanchart P, Aksenova T. Decoding ECoG signal into 3D hand translation using deep learning. J Neural Eng 2022; 19. [PMID: 35287119 DOI: 10.1088/1741-2552/ac5d69] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2021] [Accepted: 03/14/2022] [Indexed: 12/29/2022]
Abstract
Objective.Motor brain-computer interfaces (BCIs) are a promising technology that may enable motor-impaired people to interact with their environment. BCIs would potentially compensate for arm and hand function loss, which is the top priority for individuals with tetraplegia. Designing real-time and accurate BCI is crucial to make such devices useful, safe, and easy to use by patients in a real-life environment. Electrocorticography (ECoG)-based BCIs emerge as a good compromise between invasiveness of the recording device and good spatial and temporal resolution of the recorded signal. However, most ECoG signal decoders used to predict continuous hand movements are linear models. These models have a limited representational capacity and may fail to capture the relationship between ECoG signal features and continuous hand movements. Deep learning (DL) models, which are state-of-the-art in many problems, could be a solution to better capture this relationship.Approach.In this study, we tested several DL-based architectures to predict imagined 3D continuous hand translation using time-frequency features extracted from ECoG signals. The dataset used in the analysis is a part of a long-term clinical trial (ClinicalTrials.gov identifier: NCT02550522) and was acquired during a closed-loop experiment with a tetraplegic subject. The proposed architectures include multilayer perceptron, convolutional neural networks (CNNs), and long short-term memory networks (LSTM). The accuracy of the DL-based and multilinear models was compared offline using cosine similarity.Main results.Our results show that CNN-based architectures outperform the current state-of-the-art multilinear model. The best architecture exploited the spatial correlation between neighboring electrodes with CNN and benefited from the sequential character of the desired hand trajectory by using LSTMs. Overall, DL increased the average cosine similarity, compared to the multilinear model, by up to 60%, from 0.189 to 0.302 and from 0.157 to 0.249 for the left and right hand, respectively.Significance.This study shows that DL-based models could increase the accuracy of BCI systems in the case of 3D hand translation prediction in a tetraplegic subject.
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Affiliation(s)
- Maciej Śliwowski
- Université Grenoble Alpes, CEA, LETI, Clinatec, F-38000 Grenoble, France.,Université Paris-Saclay, CEA, List, F-91120 Palaiseau, France
| | - Matthieu Martin
- Université Grenoble Alpes, CEA, LETI, Clinatec, F-38000 Grenoble, France
| | | | | | - Tetiana Aksenova
- Université Grenoble Alpes, CEA, LETI, Clinatec, F-38000 Grenoble, France
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18
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Moly A, Costecalde T, Martel F, Martin M, Larzabal C, Karakas S, Verney A, Charvet G, Chabardès S, Benabid AL, Aksenova T. An adaptive closed-loop ECoG decoder for long-term and stable bimanual control of an exoskeleton by a tetraplegic. J Neural Eng 2022; 19. [PMID: 35234665 DOI: 10.1088/1741-2552/ac59a0] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2021] [Accepted: 03/01/2022] [Indexed: 11/12/2022]
Abstract
OBJECTIVE The article aims at addressing 2 challenges to step motor BCI out of laboratories: asynchronous control of complex bimanual effectors with large numbers of degrees of freedom, using chronic and safe recorders, and the decoding performance stability over time without frequent decoder recalibration. APPROACH Closed-loop adaptive/incremental decoder training is one strategy to create a model stable over time. Adaptive decoders update their parameters with new incoming data, optimizing the model parameters in real time. It allows cross-session training with multiple recording conditions during closed loop BCI experiments. In the article, an adaptive tensor-based Recursive Exponentially Weighted Markov-Switching multi-Linear Model (REW-MSLM) decoder is proposed. REW-MSLM uses a Mixture of Expert (ME) architecture, mixing or switching independent decoders (experts) according to the probability estimated by a "gating" model. A Hidden Markov model approach is employed as gating model to improve the decoding robustness and to provide strong idle state support. The ME architecture fits the multi-limb paradigm associating an expert to a particular limb or action. MAIN RESULTS Asynchronous control of an exoskeleton by a tetraplegic patient using a chronically implanted epidural electrocorticography (EpiCoG) recorder is reported. The stable over a period of 6 months (without decoder recalibration) 8-dimensional alternative bimanual control of the exoskeleton and its virtual avatar is demonstrated. SIGNIFICANCE Based on the long-term (>36 months) chronic bilateral epidural ECoG recordings in a tetraplegic (ClinicalTrials.gov, NCT02550522), we addressed the poorly explored field of asynchronous bimanual BCI. The new decoder was designed to meet to several challenges: the high-dimensional control of a complex effector in experiments closer to real-world behaviour (point-to-point pursuit versus conventional center-out tasks), with the ability of the BCI system to act as a stand-alone device switching between idle and control states, and a stable performance over a long period of time without decoder recalibration.
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Affiliation(s)
- Alexandre Moly
- Univ. Grenoble Alpes, CEA, LETI, Clinatec,, 17 av. des martyrs, Grenoble, Auvergne-Rhône-Alpes, 38000, FRANCE
| | - Thomas Costecalde
- Univ. Grenoble Alpes, CEA, LETI, Clinatec,, 17 av. des martyrs, Grenoble, Auvergne-Rhône-Alpes, 38000, FRANCE
| | - Félix Martel
- Univ. Grenoble Alpes, CEA, LETI, Clinatec,, 17 av. des martyrs, Grenoble, Auvergne-Rhône-Alpes, 38000, FRANCE
| | - Matthieu Martin
- Univ. Grenoble Alpes, CEA, LETI, Clinatec,, 17 av. des Martyrs, Grenoble, 38000, FRANCE
| | - Christelle Larzabal
- Univ. Grenoble Alpes, CEA, LETI, Clinatec,, 17 av. des martyrs, Grenoble, Auvergne-Rhône-Alpes, 38000, FRANCE
| | - Serpil Karakas
- Univ. Grenoble Alpes, CEA, LETI, Clinatec,, 17 av. des martyrs, Grenoble, Auvergne-Rhône-Alpes, 38000, FRANCE
| | - Alexandre Verney
- Université Paris-Saclay, Palaiseau, Palaiseau, Île-de-France, 91120, FRANCE
| | - Guillaume Charvet
- Univ. Grenoble Alpes, CEA, LETI, Clinatec,, 17 av. des martyrs, Grenoble, Auvergne-Rhône-Alpes, 38000, FRANCE
| | - Stephan Chabardès
- CHU Grenoble Alpes, Boulevard de la Chantourne, La Tronche, Auvergne-Rhône-Alpes, 38700, FRANCE
| | - Alim-Louis Benabid
- Univ. Grenoble Alpes, CEA, LETI, Clinatec, 17, avenue des Martyrs, Grenoble, 38000, FRANCE
| | - Tatiana Aksenova
- Univ. Grenoble Alpes, CEA, LETI, Clinatec,, 17 av. des martyrs, Grenoble, Auvergne-Rhône-Alpes, 38000, FRANCE
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19
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Balakrishnan G, Song J, Mou C, Bettinger CJ. Recent Progress in Materials Chemistry to Advance Flexible Bioelectronics in Medicine. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2022; 34:e2106787. [PMID: 34751987 PMCID: PMC8917047 DOI: 10.1002/adma.202106787] [Citation(s) in RCA: 24] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/27/2021] [Revised: 10/15/2021] [Indexed: 05/09/2023]
Abstract
Designing bioelectronic devices that seamlessly integrate with the human body is a technological pursuit of great importance. Bioelectronic medical devices that reliably and chronically interface with the body can advance neuroscience, health monitoring, diagnostics, and therapeutics. Recent major efforts focus on investigating strategies to fabricate flexible, stretchable, and soft electronic devices, and advances in materials chemistry have emerged as fundamental to the creation of the next generation of bioelectronics. This review summarizes contemporary advances and forthcoming technical challenges related to three principal components of bioelectronic devices: i) substrates and structural materials, ii) barrier and encapsulation materials, and iii) conductive materials. Through notable illustrations from the literature, integration and device fabrication strategies and associated challenges for each material class are highlighted.
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Affiliation(s)
| | - Jiwoo Song
- Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, PA, 15213, USA
| | - Chenchen Mou
- Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, PA, 15213, USA
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Siegel A, Sauter-Starace F, Laporte S. Insights into the mechanical interaction between an active cranial implant and the skull subjected to moderate impact loadings. J Mech Behav Biomed Mater 2022; 126:105048. [PMID: 34999489 DOI: 10.1016/j.jmbbm.2021.105048] [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: 09/20/2021] [Revised: 12/06/2021] [Accepted: 12/12/2021] [Indexed: 11/18/2022]
Abstract
In the context of cochlear implants, which are now widely used, and innovative active devices, the cranial implantation of electronic devices raises new questions about the mechanical interactions between the implant and the skull. The aim of this study was to build a methodology using experimental data and numerical simulations to evaluate the mechanical interactions between the skull and the WIMAGINE® active cranial implant intended for use for tetraplegic patients. A finite element model of the implant housing and a simplified model of the three-layered skull were developed. 2.5 J-hammer impact tests were performed on implant housings and ovine cadaver heads for model calibration. The two models were then combined to analyze the interactions between the skull and the implant and compared against impact tests. The implant dissipates a certain amount of the impact energy which could be a parameter to include in implant design in addition to the implant integrity, tending to increase the implant stiffness. The non-implanted as well as the implanted lamb heads demonstrated an overall good resistance to the impact tests. The models correlated well with the experimental data, and improvements of the model through more realistic geometry (CT-scans) and more complex material behavior could now be implemented. Such a model could then be used with human head geometries and help for future implant design optimizations using numerical models of the implant-skull and even implant-head complex.
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Affiliation(s)
- Alice Siegel
- Univ. Grenoble Alpes, CEA, LETI, Clinatec, 38000, Grenoble, France; Arts et Métiers Institute of Technology, Université Sorbonne Paris Nord, IBHGC - Institut de Biomécanique Humaine Georges Charpak, HESAM Université, F-75013, Paris, France.
| | | | - Sébastien Laporte
- Arts et Métiers Institute of Technology, Université Sorbonne Paris Nord, IBHGC - Institut de Biomécanique Humaine Georges Charpak, HESAM Université, F-75013, Paris, France
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21
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Schalk G, Worrell S, Mivalt F, Belsten A, Kim I, Morris JM, Hermes D, Klassen BT, Staff NP, Messina S, Kaufmann T, Rickert J, Brunner P, Worrell GA, Miller KJ. Toward a fully implantable ecosystem for adaptive neuromodulation in humans: Preliminary experience with the CorTec BrainInterchange device in a canine model. Front Neurosci 2022; 16:932782. [PMID: 36601593 PMCID: PMC9806357 DOI: 10.3389/fnins.2022.932782] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2022] [Accepted: 10/24/2022] [Indexed: 12/23/2022] Open
Abstract
This article describes initial work toward an ecosystem for adaptive neuromodulation in humans by documenting the experience of implanting CorTec's BrainInterchange (BIC) device in a beagle canine and using the BCI2000 environment to interact with the BIC device. It begins with laying out the substantial opportunity presented by a useful, easy-to-use, and widely available hardware/software ecosystem in the current landscape of the field of adaptive neuromodulation, and then describes experience with implantation, software integration, and post-surgical validation of recording of brain signals and implant parameters. Initial experience suggests that the hardware capabilities of the BIC device are fully supported by BCI2000, and that the BIC/BCI2000 device can record and process brain signals during free behavior. With further development and validation, the BIC/BCI2000 ecosystem could become an important tool for research into new adaptive neuromodulation protocols in humans.
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Affiliation(s)
- Gerwin Schalk
- Department of Neurosurgery, Mayo Clinic, Rochester, MN, United States
- Chen Frontier Lab for Applied Neurotechnology, Tianqiao and Chrissy Chen Institute, Shanghai, China
- *Correspondence: Gerwin Schalk
| | - Samuel Worrell
- Department of Neurosurgery, Mayo Clinic, Rochester, MN, United States
| | - Filip Mivalt
- Department of Neurology, Mayo Clinic, Rochester, MN, United States
- Department of Biomedical Engineering, Brno University of Technology, Brno, Czechia
| | - Alexander Belsten
- Department of Neurosurgery, Washington University in St. Louis, St. Louis, MO, United States
- National Center for Adaptive Neurotechnologies, Albany, NY, United States
| | - Inyong Kim
- Department of Neurology, Mayo Clinic, Rochester, MN, United States
| | | | - Dora Hermes
- Department of Physiology and Biomedical Engineering, Mayo Clinic, Rochester, MN, United States
| | - Bryan T. Klassen
- Department of Neurology, Mayo Clinic, Rochester, MN, United States
| | - Nathan P. Staff
- Department of Neurology, Mayo Clinic, Rochester, MN, United States
| | - Steven Messina
- Department of Neuroradiology, Mayo Clinic, Rochester, MN, United States
| | - Timothy Kaufmann
- Department of Neuroradiology, Mayo Clinic, Rochester, MN, United States
| | | | - Peter Brunner
- Department of Neurosurgery, Washington University in St. Louis, St. Louis, MO, United States
- National Center for Adaptive Neurotechnologies, Albany, NY, United States
| | - Gregory A. Worrell
- Department of Neurology, Mayo Clinic, Rochester, MN, United States
- Department of Physiology and Biomedical Engineering, Mayo Clinic, Rochester, MN, United States
| | - Kai J. Miller
- Department of Neurosurgery, Mayo Clinic, Rochester, MN, United States
- Department of Physiology and Biomedical Engineering, Mayo Clinic, Rochester, MN, United States
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22
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Larzabal C, Bonnet S, Costecalde T, Auboiroux V, Charvet G, Chabardes S, Aksenova T, Sauter-Starace F. Long-term stability of the chronic epidural wireless recorder WIMAGINE in tetraplegic patients. J Neural Eng 2021; 18. [PMID: 34425566 DOI: 10.1088/1741-2552/ac2003] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2021] [Accepted: 08/23/2021] [Indexed: 11/12/2022]
Abstract
Objective.The evaluation of the long-term stability of ElectroCorticoGram (ECoG) signals is an important scientific question as new implantable recording devices can be used for medical purposes such as Brain-Computer Interface (BCI) or brain monitoring.Approach.The long-term clinical validation of wireless implantable multi-channel acquisition system for generic interface with neurons (WIMAGINE), a wireless 64-channel epidural ECoG recorder was investigated. The WIMAGINE device was implanted in two quadriplegic patients within the context of a BCI protocol. This study focused on the ECoG signal stability in two patients bilaterally implanted in June 2017 (P1) and in November 2019 (P2).Methods. The ECoG signal was recorded at rest prior to each BCI session resulting in a 32 month and in a 14 month follow-up for P1 and P2 respectively. State-of-the-art signal evaluation metrics such as root mean square (RMS), the band power (BP), the signal to noise ratio (SNR), the effective bandwidth (EBW) and the spectral edge frequency (SEF) were used to evaluate stability of signal over the implantation time course. The time-frequency maps obtained from task-related motor activations were also studied to investigate the long-term selectivity of the electrodes.Mainresults.Based on temporal linear regressions, we report a limited decrease of the signal average level (RMS), spectral distribution (BP) and SNR, and a remarkable steadiness of the EBW and SEF. Time-frequency maps obtained during motor imagery, showed a high level of discrimination 1 month after surgery and also after 2 years.Conclusions.The WIMAGINE epidural device showed high stability over time. The signal evaluation metrics of two quadriplegic patients during 32 months and 14 months respectively provide strong evidence that this wireless implant is well-suited for long-term ECoG recording.Significance.These findings are relevant for the future of implantable BCIs, and could benefit other patients with spinal cord injury, amyotrophic lateral sclerosis, neuromuscular diseases or drug-resistant epilepsy.
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Affiliation(s)
| | - Stéphane Bonnet
- University Grenoble Alpes, CEA, LETI, DTBS, Grenoble 38000, France
| | - Thomas Costecalde
- University Grenoble Alpes, CEA, LETI, Clinatec, Grenoble 38000, France
| | - Vincent Auboiroux
- University Grenoble Alpes, CEA, LETI, Clinatec, Grenoble 38000, France
| | - Guillaume Charvet
- University Grenoble Alpes, CEA, LETI, Clinatec, Grenoble 38000, France
| | - Stéphan Chabardes
- University Grenoble Alpes, Grenoble University Hospital, Grenoble 38000, France
| | - Tetiana Aksenova
- University Grenoble Alpes, CEA, LETI, Clinatec, Grenoble 38000, France
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23
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Shokur S, Mazzoni A, Schiavone G, Weber DJ, Micera S. A modular strategy for next-generation upper-limb sensory-motor neuroprostheses. MED 2021; 2:912-937. [DOI: 10.1016/j.medj.2021.05.002] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2021] [Revised: 04/28/2021] [Accepted: 05/10/2021] [Indexed: 02/06/2023]
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24
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Siegel A, Laporte S, Sauter-Starace F. Johnson-Cook Parameter Identification for Commercially Pure Titanium at Room Temperature under Quasi-Static Strain Rates. MATERIALS (BASEL, SWITZERLAND) 2021; 14:3887. [PMID: 34300807 PMCID: PMC8304854 DOI: 10.3390/ma14143887] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/01/2021] [Revised: 06/18/2021] [Accepted: 06/22/2021] [Indexed: 11/17/2022]
Abstract
BACKGROUND To simulate mechanical shocks on an intracranial implant called WIMAGINE®, Clinatec chose a Johnson-Cook model to account for the viscoplastic behavior of grade 2 titanium in a dynamic study using Radioss©. METHODS Thirty tensile specimens were subjected to tensile tests at room temperature, and the influence of the strain rate (8 × 10-3 and 8 × 10-2 s-1) and sandblasting was analyzed. Relaxations were included in the tests to analyze viscosity phenomena. RESULTS A whole set of parameters was identified for the elastic and plastic parts. Strain rate influence on stress was negligible at these strain rates. As expected, the sandblasting hardened the material during the tests by decreasing the hardening parameters, while local necking occurred at an earlier strain. CONCLUSIONS This article provides the parameters of a Johnson-Cook model to simulate the elastoplastic behavior of pure titanium (T40, grade 2) in Finite Element Model (FEM) software.
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Affiliation(s)
- Alice Siegel
- Clinatec, CEA, LETI, Université Grenoble Alpes, F-38000 Grenoble, France;
- Institut de Biomécanique Humaine Georges Charpak, Arts et Métiers Institute of Technology, F-75013 Paris, France;
| | - Sébastien Laporte
- Institut de Biomécanique Humaine Georges Charpak, Arts et Métiers Institute of Technology, F-75013 Paris, France;
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25
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Larzabal C, Auboiroux V, Karakas S, Charvet G, Benabid AL, Chabardès S, Costecalde T, Bonnet S. The Riemannian Spatial Pattern method: mapping and clustering movement imagery using Riemannian geometry. J Neural Eng 2021; 18. [PMID: 33770779 DOI: 10.1088/1741-2552/abf291] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2020] [Accepted: 03/26/2021] [Indexed: 11/11/2022]
Abstract
OBJECTIVE Over the last decade, Riemannian geometry has shown promising results for motor imagery classification. However, extracting the underlying spatial features is not as straightforward as for applying Common Spatial Pattern (CSP) filtering prior to classification. In this article, we propose a simple way to extract the spatial patterns obtained from Riemannian classification: the Riemannian Spatial Pattern (RSP) method, which is based on the backward channel selection procedure. APPROACH The RSP method was compared to the CSP approach on ECoG data obtained from a quadriplegic patient while performing imagined movements of arm articulations and fingers. MAIN RESULTS Similar results were found between the RSP and CSP methods for mapping each motor imagery task with activations following the classical somatotopic organization. Clustering obtained by pairwise comparisons of imagined motor movements however, revealed higher differentiation for the RSP method compared to the CSP approach. Importantly, the RSP approach could provide a precise comparison of the imagined finger flexions which added supplementary information to the mapping results. SIGNIFICANCE Our new RSP method illustrates the interest of the Riemannian framework in the spatial domain and as such offers new avenues for the neuroimaging community. This study is part of an ongoing clinical trial registered with ClinicalTrials.gov, NCT02550522.
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Affiliation(s)
| | - Vincent Auboiroux
- CEA de Grenoble, Clinatec, Grenoble, Auvergne-Rhône-Alpes, 38054, FRANCE
| | - Serpil Karakas
- CEA de Grenoble, Clinatec, Grenoble, Auvergne-Rhône-Alpes, 38054, FRANCE
| | - Guillaume Charvet
- CEA de Grenoble, Clinatec, Grenoble, Auvergne-Rhône-Alpes, 38054, FRANCE
| | - Alim-Louis Benabid
- CEA de Grenoble, Clinatec, Grenoble, Auvergne-Rhône-Alpes, 38054, FRANCE
| | | | - Thomas Costecalde
- CEA de Grenoble, Clinatec, Grenoble, Auvergne-Rhône-Alpes, 38054, FRANCE
| | - Stephane Bonnet
- CEA de Grenoble, DTBS, Grenoble, Auvergne-Rhône-Alpes, 38000, FRANCE
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26
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Saha S, Mamun KA, Ahmed K, Mostafa R, Naik GR, Darvishi S, Khandoker AH, Baumert M. Progress in Brain Computer Interface: Challenges and Opportunities. Front Syst Neurosci 2021; 15:578875. [PMID: 33716680 PMCID: PMC7947348 DOI: 10.3389/fnsys.2021.578875] [Citation(s) in RCA: 82] [Impact Index Per Article: 27.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2020] [Accepted: 01/06/2021] [Indexed: 12/13/2022] Open
Abstract
Brain computer interfaces (BCI) provide a direct communication link between the brain and a computer or other external devices. They offer an extended degree of freedom either by strengthening or by substituting human peripheral working capacity and have potential applications in various fields such as rehabilitation, affective computing, robotics, gaming, and neuroscience. Significant research efforts on a global scale have delivered common platforms for technology standardization and help tackle highly complex and non-linear brain dynamics and related feature extraction and classification challenges. Time-variant psycho-neurophysiological fluctuations and their impact on brain signals impose another challenge for BCI researchers to transform the technology from laboratory experiments to plug-and-play daily life. This review summarizes state-of-the-art progress in the BCI field over the last decades and highlights critical challenges.
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Affiliation(s)
- Simanto Saha
- School of Electrical and Electronic Engineering, The University of Adelaide, Adelaide, SA, Australia
- Department of Electrical and Electronic Engineering, United International University, Dhaka, Bangladesh
| | - Khondaker A. Mamun
- Advanced Intelligent Multidisciplinary Systems (AIMS) Lab, Department of Computer Science and Engineering, United International University, Dhaka, Bangladesh
| | - Khawza Ahmed
- Department of Electrical and Electronic Engineering, United International University, Dhaka, Bangladesh
| | - Raqibul Mostafa
- Department of Electrical and Electronic Engineering, United International University, Dhaka, Bangladesh
| | - Ganesh R. Naik
- Adelaide Institute for Sleep Health, College of Medicine and Public Health, Flinders University, Adelaide, SA, Australia
| | - Sam Darvishi
- School of Electrical and Electronic Engineering, The University of Adelaide, Adelaide, SA, Australia
| | - Ahsan H. Khandoker
- Healthcare Engineering Innovation Center, Department of Biomedical Engineering, Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates
| | - Mathias Baumert
- School of Electrical and Electronic Engineering, The University of Adelaide, Adelaide, SA, Australia
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27
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Wang P, Zhou Y, Li Z, Zhang D. GBCI: Adaptive Frequency Band Learning for Gender Recognition in Brain-Computer Interfaces. ARTIF INTELL 2021. [DOI: 10.1007/978-3-030-93046-2_19] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2022]
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28
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Yan T, Kameda S, Suzuki K, Kaiju T, Inoue M, Suzuki T, Hirata M. Minimal Tissue Reaction after Chronic Subdural Electrode Implantation for Fully Implantable Brain-Machine Interfaces. SENSORS 2020; 21:s21010178. [PMID: 33383864 PMCID: PMC7795822 DOI: 10.3390/s21010178] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/07/2020] [Revised: 12/12/2020] [Accepted: 12/25/2020] [Indexed: 11/16/2022]
Abstract
There is a growing interest in the use of electrocorticographic (ECoG) signals in brain–machine interfaces (BMIs). However, there is still a lack of studies involving the long-term evaluation of the tissue response related to electrode implantation. Here, we investigated biocompatibility, including chronic tissue response to subdural electrodes and a fully implantable wireless BMI device. We implanted a half-sized fully implantable device with subdural electrodes in six beagles for 6 months. Histological analysis of the surrounding tissues, including the dural membrane and cortices, was performed to evaluate the effects of chronic implantation. Our results showed no adverse events, including infectious signs, throughout the 6-month implantation period. Thick connective tissue proliferation was found in the surrounding tissues in the epidural space and subcutaneous space. Quantitative measures of subdural reactive tissues showed minimal encapsulation between the electrodes and the underlying cortex. Immunohistochemical evaluation showed no significant difference in the cell densities of neurons, astrocytes, and microglia between the implanted sites and contralateral sites. In conclusion, we established a beagle model to evaluate cortical implantable devices. We confirmed that a fully implantable wireless device and subdural electrodes could be stably maintained with sufficient biocompatibility in vivo.
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Affiliation(s)
- Tianfang Yan
- Department of Neurological Diagnosis and Restoration, Osaka University Graduate School of Medicine, Suita 565-0871, Japan; (T.Y.); (S.K.)
| | - Seiji Kameda
- Department of Neurological Diagnosis and Restoration, Osaka University Graduate School of Medicine, Suita 565-0871, Japan; (T.Y.); (S.K.)
- Global Center for Medical Engineering and Informatics, Osaka University, Suita 565-0871, Japan
- Department of Neurosurgery, Osaka University Graduate School of Medicine, Suita 565-0871, Japan
| | - Katsuyoshi Suzuki
- Ogino Memorial Laboratory, Nihon Kohden Corporation, Tokorozawa 359-0037, Japan;
| | - Taro Kaiju
- Center for Information and Neural Networks, National Institute of Information and Communications Technology, Suita 565-0871, Japan; (T.K.); (M.I.); (T.S.)
| | - Masato Inoue
- Center for Information and Neural Networks, National Institute of Information and Communications Technology, Suita 565-0871, Japan; (T.K.); (M.I.); (T.S.)
| | - Takafumi Suzuki
- Center for Information and Neural Networks, National Institute of Information and Communications Technology, Suita 565-0871, Japan; (T.K.); (M.I.); (T.S.)
| | - Masayuki Hirata
- Department of Neurological Diagnosis and Restoration, Osaka University Graduate School of Medicine, Suita 565-0871, Japan; (T.Y.); (S.K.)
- Global Center for Medical Engineering and Informatics, Osaka University, Suita 565-0871, Japan
- Department of Neurosurgery, Osaka University Graduate School of Medicine, Suita 565-0871, Japan
- Correspondence:
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29
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Keramatzadeh K, Kiakojouri A, Nahvi MS, Khazaei Y, Feizi-Nejad A, Maghami MH, Mohammadi R, Sharifshazileh M, Nasiri S, Akbari Boroumand F, Nadimi E, Rezaei M, Shojaei A, Mirnajafi-Zadeh J, Sodagar AM. Wireless, miniaturized, semi-implantable electrocorticography microsystem validated in vivo. Sci Rep 2020; 10:21261. [PMID: 33277523 PMCID: PMC7718888 DOI: 10.1038/s41598-020-77953-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2020] [Accepted: 10/29/2020] [Indexed: 11/09/2022] Open
Abstract
This paper reports on the design, development, and test of a multi-channel wireless micro-electrocorticography (µECoG) system. The system consists of a semi-implantable, ultra-compact recording unit and an external unit, interfaced through a 2.4 GHz radio frequency data telemetry link with 2 Mbps (partially used) data transfer rate. Encased in a 3D-printed 2.9 cm × 2.9 cm × 2.5 cm cubic package, the semi-implantable recording unit consists of a microelectrode array, a vertically-stacked PCB platform containing off-the-shelf components, and commercially-available small-size 3.7-V, 50 mAh lithium-ion batteries. Two versions of microelectrode array were developed for the recording unit: a rigid 4 × 2 microelectrode array, and a flexible 12 × 6 microelectrode array, 36 of which routed to bonding pads for actual recording. The external unit comprises a transceiver board, a data acquisition board, and a host computer, on which reconstruction of the received signals is performed. After development, assembly, and integration, the system was tested and validated in vivo on anesthetized rats. The system successfully recorded both spontaneous and evoked activities from the brain of the subject.
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Affiliation(s)
- Keivan Keramatzadeh
- Research Labarotory for Integrated Circuits and Systems (ICAS), Faculty of EE, K.N. Toosi University of Technology, Tehran, Iran
| | - Ali Kiakojouri
- Research Labarotory for Integrated Circuits and Systems (ICAS), Faculty of EE, K.N. Toosi University of Technology, Tehran, Iran
| | - Mohammad Sadegh Nahvi
- Research Labarotory for Integrated Circuits and Systems (ICAS), Faculty of EE, K.N. Toosi University of Technology, Tehran, Iran
| | - Yousef Khazaei
- Research Labarotory for Integrated Circuits and Systems (ICAS), Faculty of EE, K.N. Toosi University of Technology, Tehran, Iran
| | - Ali Feizi-Nejad
- Research Labarotory for Integrated Circuits and Systems (ICAS), Faculty of EE, K.N. Toosi University of Technology, Tehran, Iran
| | - Mohammad Hossein Maghami
- Research Labarotory for Integrated Circuits and Systems (ICAS), Faculty of EE, K.N. Toosi University of Technology, Tehran, Iran.,Faculty of Electrical Engineering, Shahid Rajaee Teacher Training University, Tehran, Iran
| | - Reza Mohammadi
- Department of ECE, University of Waterloo, Waterloo, ON, Canada
| | | | - Soraya Nasiri
- Research Labarotory for Integrated Circuits and Systems (ICAS), Faculty of EE, K.N. Toosi University of Technology, Tehran, Iran
| | | | - Ebrahim Nadimi
- Faculty of EE, K.N. Toosi University of Technology, Tehran, Iran
| | - Mahmoud Rezaei
- Department of Physiology, Faculty of Medical Sciences, Tarbiat Modares University, Tehran, Iran
| | - Amir Shojaei
- Department of Physiology, Faculty of Medical Sciences, Tarbiat Modares University, Tehran, Iran
| | - Javad Mirnajafi-Zadeh
- Department of Physiology, Faculty of Medical Sciences, Tarbiat Modares University, Tehran, Iran
| | - Amir M Sodagar
- Department of EECS, York University, Toronto, ON, Canada.
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30
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Nason SR, Vaskov AK, Willsey MS, Welle EJ, An H, Vu PP, Bullard AJ, Nu CS, Kao JC, Shenoy KV, Jang T, Kim HS, Blaauw D, Patil PG, Chestek CA. A low-power band of neuronal spiking activity dominated by local single units improves the performance of brain-machine interfaces. Nat Biomed Eng 2020; 4:973-983. [PMID: 32719512 DOI: 10.1038/s41551-020-0591-0] [Citation(s) in RCA: 53] [Impact Index Per Article: 13.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2018] [Accepted: 06/24/2020] [Indexed: 12/18/2022]
Abstract
The large power requirement of current brain-machine interfaces is a major hindrance to their clinical translation. In basic behavioural tasks, the downsampled magnitude of the 300-1,000 Hz band of spiking activity can predict movement similarly to the threshold crossing rate (TCR) at 30 kilo-samples per second. However, the relationship between such a spiking-band power (SBP) and neural activity remains unclear, as does the capability of using the SBP to decode complicated behaviour. By using simulations of recordings of neural activity, here we show that the SBP is dominated by local single-unit spikes with spatial specificity comparable to or better than that of the TCR, and that the SBP correlates better with the firing rates of lower signal-to-noise-ratio units than the TCR. With non-human primates, in an online task involving the one-dimensional decoding of the movement of finger groups and in an offline two-dimensional cursor-control task, the SBP performed equally well or better than the TCR. The SBP may enhance the decoding performance of neural interfaces while enabling substantial cuts in power consumption.
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Affiliation(s)
- Samuel R Nason
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, USA
| | - Alex K Vaskov
- Robotics Graduate Program, University of Michigan, Ann Arbor, MI, USA
| | - Matthew S Willsey
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, USA.,Department of Neurosurgery, University of Michigan Medical School, Ann Arbor, MI, USA
| | - Elissa J Welle
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, USA
| | - Hyochan An
- Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI, USA
| | - Philip P Vu
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, USA
| | - Autumn J Bullard
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, USA
| | - Chrono S Nu
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, USA
| | - Jonathan C Kao
- Department of Electrical and Computer Engineering, University of California, Los Angeles, Los Angeles, CA, USA.,Neurosciences Program, University of California, Los Angeles, Los Angeles, CA, USA
| | - Krishna V Shenoy
- Department of Electrical Engineering, Stanford University, Stanford, CA, USA.,Department of Bioengineering, Stanford University, Stanford, CA, USA.,Department of Neurobiology, Stanford University, Stanford, CA, USA.,The Bio-X Program, Stanford University, Stanford, CA, USA.,Wu Tsai Neuroscience Institute, Stanford University, Stanford, CA, USA.,Howard Hughes Medical Institute, Stanford University, Stanford, CA, USA
| | - Taekwang Jang
- Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI, USA.,Department of Information Technology and Electrical Engineering, ETH Zürich, Zürich, Switzerland
| | - Hun-Seok Kim
- Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI, USA
| | - David Blaauw
- Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI, USA
| | - Parag G Patil
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, USA.,Department of Neurosurgery, University of Michigan Medical School, Ann Arbor, MI, USA.,Department of Neurology, University of Michigan Medical School, Ann Arbor, MI, USA.,Neuroscience Graduate Program, University of Michigan, Ann Arbor, MI, USA
| | - Cynthia A Chestek
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, USA. .,Robotics Graduate Program, University of Michigan, Ann Arbor, MI, USA. .,Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI, USA. .,Neuroscience Graduate Program, University of Michigan, Ann Arbor, MI, USA.
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31
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Laiwalla F, Lee J, Lee AH, Mok E, Leung V, Shellhammer S, Song YK, Larson L, Nurmikko A. A Distributed Wireless Network of Implantable Sub-mm Cortical Microstimulators for Brain-Computer Interfaces. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2020; 2019:6876-6879. [PMID: 31947420 DOI: 10.1109/embc.2019.8857217] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
Scalability of implantable neural interface devices is a critical bottleneck in enhancing the performance of cortical Brain-Computer Interfaces (BCIs) through access to high density and multi-areal cortical signals. This is challenging to achieve through current monolithic constructs with 100-200 channels, often with bulky tethering and packaging, and a spatially distributed sensor approach has recently been explored by a few groups, including our laboratories [1]. In this paper, we describe a microscale (500 μm) programmable neural stimulator in the context of an epicortical wireless networked system of sub-mm "Neurograins" with wireless energy harvesting (near 1 GHz) and bidirectional telemetry. Stimulation neurograins are post-processed to integrate poly(3,4-ethylenedioxythiophene) polystyrene sulfonate (PEDOT:PSS) planar electrodes or intracortical penetrating microwires, and ensembles of microdevices are hermetically encapsulated using liquid-crystal polymer (LCP) thermocompression for chronic implantability. Radio-frequency power and telecommunications management are handled by a wearable external "Epidermal Skinpatch" unit to cater to chronic clinical implant considerations. We describe the stimulation neurograin performance specifications and proof-of-concept in bench top and ex vivo rodent platforms.
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Khan MA, Das R, Iversen HK, Puthusserypady S. Review on motor imagery based BCI systems for upper limb post-stroke neurorehabilitation: From designing to application. Comput Biol Med 2020; 123:103843. [PMID: 32768038 DOI: 10.1016/j.compbiomed.2020.103843] [Citation(s) in RCA: 66] [Impact Index Per Article: 16.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2020] [Revised: 05/18/2020] [Accepted: 06/02/2020] [Indexed: 12/21/2022]
Abstract
Strokes are a growing cause of mortality and many stroke survivors suffer from motor impairment as well as other types of disabilities in their daily life activities. To treat these sequelae, motor imagery (MI) based brain-computer interface (BCI) systems have shown potential to serve as an effective neurorehabilitation tool for post-stroke rehabilitation therapy. In this review, different MI-BCI based strategies, including "Functional Electric Stimulation, Robotics Assistance and Hybrid Virtual Reality based Models," have been comprehensively reported for upper-limb neurorehabilitation. Each of these approaches have been presented to illustrate the in-depth advantages and challenges of the respective BCI systems. Additionally, the current state-of-the-art and main concerns regarding BCI based post-stroke neurorehabilitation devices have also been discussed. Finally, recommendations for future developments have been proposed while discussing the BCI neurorehabilitation systems.
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Affiliation(s)
- Muhammad Ahmed Khan
- Department of Health Technology, Technical University of Denmark, 2800, Kgs. Lyngby, Denmark.
| | - Rig Das
- Department of Health Technology, Technical University of Denmark, 2800, Kgs. Lyngby, Denmark
| | - Helle K Iversen
- Department of Neurology, University of Copenhagen, Rigshospitalet, 2600, Glostrup, Denmark
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Fallegger F, Schiavone G, Lacour SP. Conformable Hybrid Systems for Implantable Bioelectronic Interfaces. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2020; 32:e1903904. [PMID: 31608508 DOI: 10.1002/adma.201903904] [Citation(s) in RCA: 45] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/19/2019] [Revised: 08/20/2019] [Indexed: 05/27/2023]
Abstract
Conformable bioelectronic systems are promising tools that may aid the understanding of diseases, alleviate pathological symptoms such as chronic pain, heart arrhythmia, and dysfunctions, and assist in reversing conditions such as deafness, blindness, and paralysis. Combining reduced invasiveness with advanced electronic functions, hybrid bioelectronic systems have evolved tremendously in the last decade, pushed by progress in materials science, micro- and nanofabrication, system assembly and packaging, and biomedical engineering. Hybrid integration refers here to a technological approach to embed within mechanically compliant carrier substrates electronic components and circuits prepared with traditional electronic materials. This combination leverages mechanical and electronic performance of polymer substrates and device materials, respectively, and offers many opportunities for man-made systems to communicate with the body with unmet precision. However, trade-offs between materials selection, manufacturing processes, resolution, electrical function, mechanical integrity, biointegration, and reliability should be considered. Herein, prominent trends in manufacturing conformable hybrid systems are analyzed and key design, function, and validation principles are outlined together with the remaining challenges to produce reliable conformable, hybrid bioelectronic systems.
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Affiliation(s)
- Florian Fallegger
- Bertarelli Foundation Chair in Neuroprosthetic Technology, Laboratory for Soft Bioelectronic Interfaces, Institute of Microengineering, Institute of Bioengineering, Center for Neuroprosthetics, Ecole Polytechnique Fédérale de Lausanne, 1202, Geneva, Switzerland
| | - Giuseppe Schiavone
- Bertarelli Foundation Chair in Neuroprosthetic Technology, Laboratory for Soft Bioelectronic Interfaces, Institute of Microengineering, Institute of Bioengineering, Center for Neuroprosthetics, Ecole Polytechnique Fédérale de Lausanne, 1202, Geneva, Switzerland
| | - Stéphanie P Lacour
- Bertarelli Foundation Chair in Neuroprosthetic Technology, Laboratory for Soft Bioelectronic Interfaces, Institute of Microengineering, Institute of Bioengineering, Center for Neuroprosthetics, Ecole Polytechnique Fédérale de Lausanne, 1202, Geneva, Switzerland
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Araki T, Uemura T, Yoshimoto S, Takemoto A, Noda Y, Izumi S, Sekitani T. Wireless Monitoring Using a Stretchable and Transparent Sensor Sheet Containing Metal Nanowires. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2020; 32:e1902684. [PMID: 31782576 DOI: 10.1002/adma.201902684] [Citation(s) in RCA: 38] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/26/2019] [Revised: 09/02/2019] [Indexed: 05/24/2023]
Abstract
Mechanically and visually imperceptible sensor sheets integrated with lightweight wireless loggers are employed in ultimate flexible hybrid electronics (FHE) to reduce vital stress/nervousness and monitor natural biosignal responses. The key technologies and applications for conceptual sensor system fabrication are reported, as exemplified by the use of a stretchable sensor sheet completely conforming to an individual's body surface to realize a low-noise wireless monitoring system (<1 µV) that can be attached to the human forehead for recording electroencephalograms. The above system can discriminate between Alzheimer's disease and the healthy state, thus offering a rapid in-home brain diagnosis possibility. Moreover, the introduction of metal nanowires to improve the transparency of the biocompatible sensor sheet allows one to wirelessly acquire electrocorticograms of nonhuman primates and simultaneously offers optogenetic stimulation such as toward-the-brain-machine interface under free movement. Also discussed are effective methods of improving electrical reliability, biocompatibility, miniaturization, etc., for metal nanowire based tracks and exploring the use of an organic amplifier as an important component to realize a flexible active probe with a high signal-to-noise ratio. Overall, ultimate FHE technologies are demonstrated to achieve efficient closed-loop systems for healthcare management, medical diagnostics, and preclinical studies in neuroscience and neuroengineering.
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Affiliation(s)
- Teppei Araki
- The Institute of Scientific and Industrial Research (ISIR), Osaka University, 8-1 Mihogaoka, Ibaraki, Osaka, 567-0047, Japan
- Advanced Photonics and Biosensing Open Innovation Laboratory, National Institute of Advanced Industrial Science and Technology (AIST), Suita, Osaka, 565-0871, Japan
- Graduate School of Engineering, Osaka University, Suita, Osaka, 565-0871, Japan
| | - Takafumi Uemura
- The Institute of Scientific and Industrial Research (ISIR), Osaka University, 8-1 Mihogaoka, Ibaraki, Osaka, 567-0047, Japan
- Advanced Photonics and Biosensing Open Innovation Laboratory, National Institute of Advanced Industrial Science and Technology (AIST), Suita, Osaka, 565-0871, Japan
| | - Shusuke Yoshimoto
- The Institute of Scientific and Industrial Research (ISIR), Osaka University, 8-1 Mihogaoka, Ibaraki, Osaka, 567-0047, Japan
| | - Ashuya Takemoto
- The Institute of Scientific and Industrial Research (ISIR), Osaka University, 8-1 Mihogaoka, Ibaraki, Osaka, 567-0047, Japan
- Advanced Photonics and Biosensing Open Innovation Laboratory, National Institute of Advanced Industrial Science and Technology (AIST), Suita, Osaka, 565-0871, Japan
- Graduate School of Engineering, Osaka University, Suita, Osaka, 565-0871, Japan
| | - Yuki Noda
- The Institute of Scientific and Industrial Research (ISIR), Osaka University, 8-1 Mihogaoka, Ibaraki, Osaka, 567-0047, Japan
- Artificial Intelligence Research Center, The Institute of Scientific and Industrial Research (ISIR), Osaka University, 8-1 Mihogaoka, Ibaraki, Osaka, 567-0047, Japan
| | - Shintaro Izumi
- The Institute of Scientific and Industrial Research (ISIR), Osaka University, 8-1 Mihogaoka, Ibaraki, Osaka, 567-0047, Japan
| | - Tsuyoshi Sekitani
- The Institute of Scientific and Industrial Research (ISIR), Osaka University, 8-1 Mihogaoka, Ibaraki, Osaka, 567-0047, Japan
- Advanced Photonics and Biosensing Open Innovation Laboratory, National Institute of Advanced Industrial Science and Technology (AIST), Suita, Osaka, 565-0871, Japan
- Graduate School of Engineering, Osaka University, Suita, Osaka, 565-0871, Japan
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Krucoff MO, Miller JP, Saxena T, Bellamkonda R, Rahimpour S, Harward SC, Lad SP, Turner DA. Toward Functional Restoration of the Central Nervous System: A Review of Translational Neuroscience Principles. Neurosurgery 2020; 84:30-40. [PMID: 29800461 DOI: 10.1093/neuros/nyy128] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2018] [Accepted: 03/15/2018] [Indexed: 01/09/2023] Open
Abstract
Injury to the central nervous system (CNS) can leave patients with devastating neurological deficits that may permanently impair independence and diminish quality of life. Recent insights into how the CNS responds to injury and reacts to critically timed interventions are being translated into clinical applications that have the capacity to drastically improve outcomes for patients suffering from permanent neurological deficits due to spinal cord injury, stroke, or other CNS disorders. The translation of such knowledge into practical and impactful treatments involves the strategic collaboration between neurosurgeons, clinicians, therapists, scientists, and industry. Therefore, a common understanding of key neuroscientific principles is crucial. Conceptually, current approaches to CNS revitalization can be divided by scale into macroscopic (systems-circuitry) and microscopic (cellular-molecular). Here we review both emerging and well-established tenets that are being utilized to enhance CNS recovery on both levels, and we explore the role of neurosurgeons in developing therapies moving forward. Key principles include plasticity-driven functional recovery, cellular signaling mechanisms in axonal sprouting, critical timing for recovery after injury, and mechanisms of action underlying cellular replacement strategies. We then discuss integrative approaches aimed at synergizing interventions across scales, and we make recommendations for the basis of future clinical trial design. Ultimately, we argue that strategic modulation of microscopic cellular behavior within a macroscopic framework of functional circuitry re-establishment should provide the foundation for most neural restoration strategies, and the early involvement of neurosurgeons in the process will be crucial to successful clinical translation.
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Affiliation(s)
- Max O Krucoff
- Department of Neurosurgery, Duke University Medical Center, Durham, North Carolina
| | - Jonathan P Miller
- Department of Neurosurgery, Case Western Reserve University, Cleve-land, Ohio
| | - Tarun Saxena
- Department of Biomedical Engineering, Duke University, Durham, North Carolina
| | - Ravi Bellamkonda
- Department of Biomedical Engineering, Duke University, Durham, North Carolina
| | - Shervin Rahimpour
- Department of Neurosurgery, Duke University Medical Center, Durham, North Carolina
| | - Stephen C Harward
- Department of Neurosurgery, Duke University Medical Center, Durham, North Carolina
| | - Shivanand P Lad
- Department of Neurosurgery, Duke University Medical Center, Durham, North Carolina.,Department of Mechan-ical Engineering and Material Sciences, Pratt School of Engineering, Duke Uni-versity, Durham, North Carolina.,Duke Institute for Brain Sciences, Duke Univer-sity, Durham, North Carolina.,Research and Surgery Services, Durham Veterans Affairs Medical Center, Durham, North Carolina
| | - Dennis A Turner
- Department of Neurosurgery, Duke University Medical Center, Durham, North Carolina.,Department of Biomedical Engineering, Duke University, Durham, North Carolina.,Depart-ment of Neurobiology, Duke University, Durham, North Carolina.,Research and Surgery Services, Durham Veterans Affairs Medical Center, Durham, North Carolina
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Yang W, Gong Y, Li W. A Review: Electrode and Packaging Materials for Neurophysiology Recording Implants. Front Bioeng Biotechnol 2020; 8:622923. [PMID: 33585422 PMCID: PMC7873964 DOI: 10.3389/fbioe.2020.622923] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2020] [Accepted: 12/10/2020] [Indexed: 01/28/2023] Open
Abstract
To date, a wide variety of neural tissue implants have been developed for neurophysiology recording from living tissues. An ideal neural implant should minimize the damage to the tissue and perform reliably and accurately for long periods of time. Therefore, the materials utilized to fabricate the neural recording implants become a critical factor. The materials of these devices could be classified into two broad categories: electrode materials as well as packaging and substrate materials. In this review, inorganic (metals and semiconductors), organic (conducting polymers), and carbon-based (graphene and carbon nanostructures) electrode materials are reviewed individually in terms of various neural recording devices that are reported in recent years. Properties of these materials, including electrical properties, mechanical properties, stability, biodegradability/bioresorbability, biocompatibility, and optical properties, and their critical importance to neural recording quality and device capabilities, are discussed. For the packaging and substrate materials, different material properties are desired for the chronic implantation of devices in the complex environment of the body, such as biocompatibility and moisture and gas hermeticity. This review summarizes common solid and soft packaging materials used in a variety of neural interface electrode designs, as well as their packaging performances. Besides, several biopolymers typically applied over the electrode package to reinforce the mechanical rigidity of devices during insertion, or to reduce the immune response and inflammation at the device-tissue interfaces are highlighted. Finally, a benchmark analysis of the discussed materials and an outlook of the future research trends are concluded.
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Affiliation(s)
- Weiyang Yang
- Microtechnology Lab, Department of Electrical and Computer Engineering, Michigan State University, East Lansing, MI, United States
| | - Yan Gong
- Microtechnology Lab, Department of Electrical and Computer Engineering, Michigan State University, East Lansing, MI, United States
| | - Wen Li
- Microtechnology Lab, Department of Electrical and Computer Engineering, Michigan State University, East Lansing, MI, United States
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Liu S, Moncion C, Zhang J, Balachandar L, Kwaku D, Riera JJ, Volakis JL, Chae J. Fully Passive Flexible Wireless Neural Recorder for the Acquisition of Neuropotentials from a Rat Model. ACS Sens 2019; 4:3175-3185. [PMID: 31670508 DOI: 10.1021/acssensors.9b01491] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
Abstract
Wireless implantable neural interfaces can record high-resolution neuropotentials without constraining patient movement. Existing wireless systems often require intracranial wires to connect implanted electrodes to an external head stage or/and deploy an application-specific integrated circuit (ASIC), which is battery-powered or externally power-transferred, raising safety concerns such as infection, electronics failure, or heat-induced tissue damage. This work presents a biocompatible, flexible, implantable neural recorder capable of wireless acquisition of neuropotentials without wires, batteries, energy harvesting units, or active electronics. The recorder, fabricated on a thin polyimide substrate, features a small footprint of 9 mm × 8 mm × 0.3 mm and is composed of passive electronic components. The absence of active electronics on the device leads to near zero power consumption, inherently avoiding the catastrophic failure of active electronics. We performed both in vitro validation in a tissue-simulating phantom and in vivo validation in an epileptic rat. The fully passive wireless recorder was implanted under rat scalp to measure neuropotentials from its contact electrodes. The implanted wireless recorder demonstrated its capability to capture low voltage neuropotentials, including somatosensory evoked potentials (SSEPs), and interictal epileptiform discharges (IEDs). Wirelessly recorded SSEP and IED signals were directly compared to those from wired electrodes to demonstrate the efficacy of the wireless data. In addition, a convoluted neural network-based machine learning algorithm successfully achieved IED signal recognition accuracy as high as 100 and 91% in wired and wireless IED data, respectively. These results strongly support the fully passive wireless neural recorder's capability to measure neuropotentials as low as tens of microvolts. With further improvement, the recorder system presented in this work may find wide applications in future brain machine interface systems.
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Affiliation(s)
- Shiyi Liu
- School of Electrical, Computer and Energy Engineering, Arizona State University, Tempe, Arizona 85287, United States
| | - Carolina Moncion
- NMD Laboratory, Department of Biomedical Engineering, Florida International University, Miami, Florida 33174, United States
| | - Jianwei Zhang
- School of Electrical, Computer and Energy Engineering, Arizona State University, Tempe, Arizona 85287, United States
| | - Lakshmini Balachandar
- NMD Laboratory, Department of Biomedical Engineering, Florida International University, Miami, Florida 33174, United States
| | - Dzifa Kwaku
- School of Electrical, Computer and Energy Engineering, Arizona State University, Tempe, Arizona 85287, United States
| | - Jorge J. Riera
- NMD Laboratory, Department of Biomedical Engineering, Florida International University, Miami, Florida 33174, United States
| | - John L. Volakis
- NMD Laboratory, Department of Biomedical Engineering, Florida International University, Miami, Florida 33174, United States
| | - Junseok Chae
- School of Electrical, Computer and Energy Engineering, Arizona State University, Tempe, Arizona 85287, United States
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38
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Benabid AL, Costecalde T, Eliseyev A, Charvet G, Verney A, Karakas S, Foerster M, Lambert A, Morinière B, Abroug N, Schaeffer MC, Moly A, Sauter-Starace F, Ratel D, Moro C, Torres-Martinez N, Langar L, Oddoux M, Polosan M, Pezzani S, Auboiroux V, Aksenova T, Mestais C, Chabardes S. An exoskeleton controlled by an epidural wireless brain–machine interface in a tetraplegic patient: a proof-of-concept demonstration. Lancet Neurol 2019; 18:1112-1122. [DOI: 10.1016/s1474-4422(19)30321-7] [Citation(s) in RCA: 140] [Impact Index Per Article: 28.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2019] [Revised: 07/11/2019] [Accepted: 07/18/2019] [Indexed: 11/16/2022]
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Abstract
The technological ability to capture electrophysiological activity of populations of cortical neurons through chronic implantable devices has led to significant advancements in the field of brain-computer interfaces. Recent progress in the field has been driven by developments in integrated microelectronics, wireless communications, materials science, and computational neuroscience. Here, we review major device development landmarks in the arena of neural interfaces from FDA-approved clinical systems to prototype head-mounted and fully implantable wireless systems for multi-channel neural recording. Additionally, we provide an outlook toward next-generation, highly miniaturized technologies for minimally invasive, vastly parallel neural interfaces for naturalistic, closed-loop neuroprostheses.
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Loza CA, Reddy CG, Akella S, Príncipe JC. Discrimination of Movement-Related Cortical Potentials Exploiting Unsupervised Learned Representations From ECoGs. Front Neurosci 2019; 13:1248. [PMID: 31824249 PMCID: PMC6882771 DOI: 10.3389/fnins.2019.01248] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2019] [Accepted: 11/05/2019] [Indexed: 11/13/2022] Open
Abstract
Brain–Computer Interfaces (BCI) aim to bypass the peripheral nervous system to link the brain to external devices via successful modeling of decoding mechanisms. BCI based on electrocorticogram or ECoG represent a viable compromise between clinical practicality, spatial resolution, and signal quality when it comes to extracellular electrical potentials from local neuronal assemblies. Classic analysis of ECoG traces usually falls under the umbrella of Time-Frequency decompositions with adaptations from Fourier analysis and wavelets as its most prominent variants. However, analyzing such high-dimensional, multivariate time series demands for specialized signal processing and neurophysiological principles. We propose a generative model for single-channel ECoGs that is able to fully characterize reoccurring rhythm–specific neuromodulations as weighted activations of prototypical templates over time. The set of timings, weights and indexes comprise a temporal marked point process (TMPP) that accesses a set of bases from vector spaces of different dimensions—a dictionary. The shallow nature of the model admits the equivalence between latent variables and representations. In this way, learning the model parameters is a case of unsupervised representation learning. We exploit principles of Minimum Description Length (MDL) encoding to effectively yield a data-driven framework where prototypical neuromodulations (not restricted to a particular duration) can be estimated alongside the timings and features of the TMPP. We validate the proposed methodology on discrimination of movement-related tasks utilizing 32-electrode grids implanted in the frontal cortex of six epileptic subjects. We show that the learned representations from the high-gamma band (85–145 Hz) are not only interpretable, but also discriminant in a lower dimensional space. The results also underscore the practicality of our algorithm, i.e., 2 main hyperparameters that can be readily set via neurophysiology, and emphasize the need of principled and interpretable representation learning in order to model encoding mechanisms in the brain.
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Affiliation(s)
- Carlos A. Loza
- Department of Mathematics, Universidad San Francisco de Quito, Quito, Ecuador
- Instituto de Neurociencias, Universidad San Francisco de Quito, Quito, Ecuador
- *Correspondence: Carlos A. Loza
| | - Chandan G. Reddy
- Department of Neurosurgery, University of Iowa, Iowa City, IA, United States
- Department of Neurosurgery, University of Florida, Gainesville, FL, United States
- Computational NeuroEngineering Lab, Electrical and Computer Engineering Department, University of Florida, Gainesville, FL, United States
| | - Shailaja Akella
- Computational NeuroEngineering Lab, Electrical and Computer Engineering Department, University of Florida, Gainesville, FL, United States
| | - José C. Príncipe
- Computational NeuroEngineering Lab, Electrical and Computer Engineering Department, University of Florida, Gainesville, FL, United States
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Torres-Martinez N, Ratel D, Crétallaz C, Gaude C, Maubert S, Divoux JL, Henry C, Guiraud D, Sauter-Starace F. Reliability of parylene-based multi-electrode arrays chronically implanted in adult rat brains, and evidence of electrical stimulation on contact impedance. J Neural Eng 2019; 16:066047. [PMID: 31374559 DOI: 10.1088/1741-2552/ab3836] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
OBJECTIVE The goal of this study was to evaluate the long-term behavior of the surface electrode through electrochemical characterization and follow-up of implanted parylene/platinum microelectrodes. APPROACH To this aim, we designed and manufactured specific planar electrodes for cortical implantation for a rat model. This work was included in the INTENSE® project, one of the goals of which was to prove the feasibility of selective neural recording or stimulation with cuff electrodes around the vagus nerve. MAIN RESULTS After a 12-week implantation in a rat model, we can report that these microelectrodes have withstood in vivo use. Regarding the biocompatibility of the electrodes (materials and manufacturing process), no adverse effect was reported. Indeed, after the three-month implantation, we characterized limited tissue reaction beneath the electrodes and showed an increase and a stabilization of their impedance. Interestingly, the follow-up of the electrochemical impedance combined with electrical stimulation highlighted a drop in the impedance up to 60% at 1 kHz after ten minutes of electrical stimulation at 110 Hz. SIGNIFICANCE This study gives evidence of the biocompatibility of the parylene platinum contact array designed for the project and confirms the effect of stimulation on the contact impedance.
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Wang PT, Camacho E, Wang M, Li Y, Shaw SJ, Armacost M, Gong H, Kramer D, Lee B, Andersen RA, Liu CY, Heydari P, Nenadic Z, Do AH. A benchtop system to assess the feasibility of a fully independent and implantable brain-machine interface. J Neural Eng 2019; 16:066043. [PMID: 31585451 DOI: 10.1088/1741-2552/ab4b0c] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
Abstract
OBJECTIVE State-of-the-art invasive brain-machine interfaces (BMIs) have shown significant promise, but rely on external electronics and wired connections between the brain and these external components. This configuration presents health risks and limits practical use. These limitations can be addressed by designing a fully implantable BMI similar to existing FDA-approved implantable devices. Here, a prototype BMI system whose size and power consumption are comparable to those of fully implantable medical devices was designed and implemented, and its performance was tested at the benchtop and bedside. APPROACH A prototype of a fully implantable BMI system was designed and implemented as a miniaturized embedded system. This benchtop analogue was tested in its ability to acquire signals, train a decoder, perform online decoding, wirelessly control external devices, and operate independently on battery. Furthermore, performance metrics such as power consumption were benchmarked. MAIN RESULTS An analogue of a fully implantable BMI was fabricated with a miniaturized form factor. A patient undergoing epilepsy surgery evaluation with an electrocorticogram (ECoG) grid implanted over the primary motor cortex was recruited to operate the system. Seven online runs were performed with an average binary state decoding accuracy of 87.0% (lag optimized, or 85.0% at fixed latency). The system was powered by a wirelessly rechargeable battery, consumed ∼150 mW, and operated for >60 h on a single battery cycle. SIGNIFICANCE The BMI analogue achieved immediate and accurate decoding of ECoG signals underlying hand movements. A wirelessly rechargeable battery and other supporting functions allowed the system to function independently. In addition to the small footprint and acceptable power and heat dissipation, these results suggest that fully implantable BMI systems are feasible.
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Affiliation(s)
- Po T Wang
- Department of Biomedical Engineering, University of California, Irvine, CA 92697, United States of America
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Koch J, Schuettler M, Pasluosta C, Stieglitz T. Electrical connectors for neural implants: design, state of the art and future challenges of an underestimated component. J Neural Eng 2019; 16:061002. [PMID: 31362277 DOI: 10.1088/1741-2552/ab36df] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
Abstract
Technological advances in electrically active implantable devices have increased the complexity of hardware design. In particular, the increasing number of stimulation and recording channels requires innovative approaches for connectors that interface electrodes with the implant circuitry. OBJECTIVE This work aims to provide a common theoretical ground for implantable connector development with a focus on neural applications. APPROACH Aspects and experiences from several disciplines are compiled from an engineering perspective to discuss the state of the art of connector solutions. Whenever available, we also present general design guidelines. MAIN RESULTS Degradation mechanisms, material stability and design rules in terms of biocompatibility and biostability are introduced. Considering contact physics, we address the design and characterization of the contact zone and review contaminants, wear and contact degradation. For high-channel counts and body-like environments, insulation can be even more crucial than the electrical connection itself. Therefore, we also introduce the requirements for electrical insulation to prevent signal loss and distortion and discuss its impact on the practical implementation. SIGNIFICANCE A final review is dedicated to the state of the art connector concepts, their mechanical setup, electrical performance and the interface to other implant components. We conclude with an outlook for possible approaches for the future generations of implants.
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Affiliation(s)
- Julia Koch
- Laboratory for Biomedical Microtechnology, Department of Microsystems Engineering, University of Freiburg, Freiburg, Germany
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Torres-Martinez N, Cretallaz C, Ratel D, Mailley P, Gaude C, Costecalde T, Hebert C, Bergonzo P, Scorsone E, Mazellier JP, Divoux JL, Sauter-Starace F. Evaluation of chronically implanted subdural boron doped diamond/CNT recording electrodes in miniature swine brain. Bioelectrochemistry 2019; 129:79-89. [DOI: 10.1016/j.bioelechem.2019.05.007] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2018] [Revised: 05/10/2019] [Accepted: 05/10/2019] [Indexed: 11/29/2022]
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45
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Sauter-Starace F, Ratel D, Cretallaz C, Foerster M, Lambert A, Gaude C, Costecalde T, Bonnet S, Charvet G, Aksenova T, Mestais C, Benabid AL, Torres-Martinez N. Long-Term Sheep Implantation of WIMAGINE ®, a Wireless 64-Channel Electrocorticogram Recorder. Front Neurosci 2019; 13:847. [PMID: 31496929 PMCID: PMC6712079 DOI: 10.3389/fnins.2019.00847] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2019] [Accepted: 07/30/2019] [Indexed: 11/23/2022] Open
Abstract
This article deals with the long-term preclinical validation of WIMAGINE® (Wireless Implantable Multi-channel Acquisition system for Generic Interface with Neurons), a 64-channel wireless implantable recorder that measures the electrical activity at the cortical surface (electrocorticography, ECoG). The WIMAGINE® implant was designed for chronic wireless neuronal signal acquisition, to be used e.g., as an intracranial Brain–Computer Interface (BCI) for severely motor-impaired patients. Due to the size and shape of WIMAGINE®, sheep appeared to be the best animal model on which to carry out long-term in vivo validation. The devices were implanted in two sheep for a follow-up period of 10 months, including idle state cortical recordings and Somato-Sensory Evoked Potential (SSEP) sessions. ECoG and SSEP demonstrated relatively stable behavior during the 10-month observation period. Information recorded from the SensoriMotor Cortex (SMC) showed an SSEP phase reversal, indicating the cortical site of the sensorimotor activity was retained after 10 months of contact. Based on weekly recordings of raw ECoG signals, the effective bandwidth was in the range of 230 Hz for both animals and remarkably stable over time, meaning preservation of the high frequency bands valuable for decoding of the brain activity using BCIs. The power spectral density (in dB/Hz), on a log scale, was of the order of 2.2, –4.5 and –18 for the frequency bands (10–40), (40–100), and (100–200) Hz, respectively. The outcome of this preclinical work is the first long-term in vivo validation of the WIMAGINE® implant, highlighting its ability to record the brain electrical activity through the dura mater and to send wireless digitized data to the external base station. Apart from local adhesion of the dura to the skull, the neurosurgeon did not face any difficulty in the implantation of the WIMAGINE® device and post-mortem analysis of the brain revealed no side effect related to the implantation. We also report on the reliability of the system; including the implantable device, the antennas module and the external base station.
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Affiliation(s)
| | - D Ratel
- Univ. Grenoble Alpes, CEA, Leti, CLINATEC, Grenoble, France
| | - C Cretallaz
- Univ. Grenoble Alpes, CEA, Leti, CLINATEC, Grenoble, France
| | - M Foerster
- Univ. Grenoble Alpes, CEA, Leti, CLINATEC, Grenoble, France
| | - A Lambert
- Univ. Grenoble Alpes, CEA, Leti, CLINATEC, Grenoble, France
| | - C Gaude
- Univ. Grenoble Alpes, CEA, Leti, CLINATEC, Grenoble, France
| | - T Costecalde
- Univ. Grenoble Alpes, CEA, Leti, CLINATEC, Grenoble, France
| | - S Bonnet
- Univ. Grenoble Alpes, CEA, Leti, DTBS, Grenoble, France
| | - G Charvet
- Univ. Grenoble Alpes, CEA, Leti, CLINATEC, Grenoble, France
| | - T Aksenova
- Univ. Grenoble Alpes, CEA, Leti, CLINATEC, Grenoble, France
| | - C Mestais
- Univ. Grenoble Alpes, CEA, Leti, CLINATEC, Grenoble, France
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Eliseyev A, Aksenova T. Personalized adaptive instruction design (PAID) for brain–computer interface using reinforcement learning and deep learning: simulated data study. BRAIN-COMPUTER INTERFACES 2019. [DOI: 10.1080/2326263x.2019.1651570] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
Affiliation(s)
- A. Eliseyev
- University Grenoble Alpes, CEA, LETI, CLINATEC, Grenoble, France
| | - T. Aksenova
- University Grenoble Alpes, CEA, LETI, CLINATEC, Grenoble, France
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Lee MB, Kramer DR, Peng T, Barbaro MF, Liu CY, Kellis S, Lee B. Clinical neuroprosthetics: Today and tomorrow. J Clin Neurosci 2019; 68:13-19. [PMID: 31375306 DOI: 10.1016/j.jocn.2019.07.056] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2019] [Revised: 06/27/2019] [Accepted: 07/16/2019] [Indexed: 12/19/2022]
Abstract
Implantable neurostimulation devices provide a direct therapeutic link to the nervous system and can be considered brain-computer interfaces (BCI). Under this definition, BCI are not simply science fiction, they are part of existing neurosurgical practice. Clinical BCI are standard of care for historically difficult to treat neurological disorders. These systems target the central and peripheral nervous system and include Vagus Nerve Stimulation, Responsive Neurostimulation, and Deep Brain Stimulation. Recent advances in clinical BCI have focused on creating "closed-loop" systems. These systems rely on biomarker feedback and promise individualized therapy with optimal stimulation delivery and minimal side effects. Success of clinical BCI has paralleled research efforts to create BCI that restore upper extremity motor and sensory function to patients. Efforts to develop closed loop motor/sensory BCI is linked to the successes of today's clinical BCI.
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Affiliation(s)
- Morgan B Lee
- Department of Neurological Surgery, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, USA; USC Neurorestoration Center, Keck School of Medicine of USC, Los Angeles, CA, USA.
| | - Daniel R Kramer
- Department of Neurological Surgery, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, USA; USC Neurorestoration Center, Keck School of Medicine of USC, Los Angeles, CA, USA
| | - Terrance Peng
- Department of Neurological Surgery, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, USA; USC Neurorestoration Center, Keck School of Medicine of USC, Los Angeles, CA, USA
| | - Michael F Barbaro
- Department of Neurological Surgery, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, USA; USC Neurorestoration Center, Keck School of Medicine of USC, Los Angeles, CA, USA
| | - Charles Y Liu
- Department of Neurological Surgery, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, USA; USC Neurorestoration Center, Keck School of Medicine of USC, Los Angeles, CA, USA; Department of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, USA
| | - Spencer Kellis
- Department of Neurological Surgery, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, USA; USC Neurorestoration Center, Keck School of Medicine of USC, Los Angeles, CA, USA; T&C Chen Brain Machine Interface Center, California Institute of Technology, Pasadena, CA, USA; Department of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, USA
| | - Brian Lee
- Department of Neurological Surgery, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, USA; USC Neurorestoration Center, Keck School of Medicine of USC, Los Angeles, CA, USA; Department of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, USA
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48
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Thies J, Alimohammad A. Compact and Low-Power Neural Spike Compression Using Undercomplete Autoencoders. IEEE Trans Neural Syst Rehabil Eng 2019; 27:1529-1538. [PMID: 31331895 DOI: 10.1109/tnsre.2019.2929081] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
Implantable microsystems that collect and transmit neural data are becoming very useful entities in the field of neuroscience. Limited by high data rates, on-chip compression is often required to transmit the recorded data without causing power dissipation at levels that would damage sensitive brain tissue. This paper presents a data compression system designed for brain-computer interfaces (BCIs) based on undercomplete autoencoders. To the best of our knowledge, the proposed system is the first to achieve an average spike reconstruction quality of 14-dB signal-to-noise-and-distortion ratio (SNDR) at a 32× compression ratio (CR), 18-dB SNDR at a 16× CR, 22-dB SNDR at an 8× CR, and 35-dB SNDR at a 4× CR of neural spikes. The spike detection and autoencoder-based compression modules are designed and implemented in a standard 45-nm CMOS process. The post-synthesis simulation results report that the compression module consumes between 1.4 and 222.5 [Formula: see text] of power per channel and takes between 0.018 and 0.082mm2 of silicon area, depending on the desired CR and number of channels.
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49
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Flexible electronic/optoelectronic microsystems with scalable designs for chronic biointegration. Proc Natl Acad Sci U S A 2019; 116:15398-15406. [PMID: 31308234 DOI: 10.1073/pnas.1907697116] [Citation(s) in RCA: 27] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2023] Open
Abstract
Flexible biocompatible electronic systems that leverage key materials and manufacturing techniques associated with the consumer electronics industry have potential for broad applications in biomedicine and biological research. This study reports scalable approaches to technologies of this type, where thin microscale device components integrate onto flexible polymer substrates in interconnected arrays to provide multimodal, high performance operational capabilities as intimately coupled biointerfaces. Specificially, the material options and engineering schemes summarized here serve as foundations for diverse, heterogeneously integrated systems. Scaled examples incorporate >32,000 silicon microdie and inorganic microscale light-emitting diodes derived from wafer sources distributed at variable pitch spacings and fill factors across large areas on polymer films, at full organ-scale dimensions such as human brain, over ∼150 cm2 In vitro studies and accelerated testing in simulated biofluids, together with theoretical simulations of underlying processes, yield quantitative insights into the key materials aspects. The results suggest an ability of these systems to operate in a biologically safe, stable fashion with projected lifetimes of several decades without leakage currents or reductions in performance. The versatility of these combined concepts suggests applicability to many classes of biointegrated semiconductor devices.
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Cho N, Squair JW, Bloch J, Courtine G. Neurorestorative interventions involving bioelectronic implants after spinal cord injury. Bioelectron Med 2019; 5:10. [PMID: 32232100 PMCID: PMC7098222 DOI: 10.1186/s42234-019-0027-x] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2019] [Accepted: 06/13/2019] [Indexed: 12/15/2022] Open
Abstract
In the absence of approved treatments to repair damage to the central nervous system, the role of neurosurgeons after spinal cord injury (SCI) often remains confined to spinal cord decompression and vertebral fracture stabilization. However, recent advances in bioelectronic medicine are changing this landscape. Multiple neuromodulation therapies that target circuits located in the brain, midbrain, or spinal cord have been able to improve motor and autonomic functions. The spectrum of implantable brain-computer interface technologies is also expanding at a fast pace, and all these neurotechnologies are being progressively embedded within rehabilitation programs in order to augment plasticity of spared circuits and residual projections with training. Here, we summarize the impending arrival of bioelectronic medicine in the field of SCI. We also discuss the new role of functional neurosurgeons in neurorestorative interventional medicine, a new discipline at the intersection of neurosurgery, neuro-engineering, and neurorehabilitation.
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Affiliation(s)
- Newton Cho
- École polytechnique fédérale de Lausanne (EPFL), Campus Biotech, Center for Neuroprosthetics and Brain Mind Institute, 1202 Genève, Switzerland.,2Department of Neurosurgery, University of Toronto, Toronto, Ontario Canada
| | - Jordan W Squair
- École polytechnique fédérale de Lausanne (EPFL), Campus Biotech, Center for Neuroprosthetics and Brain Mind Institute, 1202 Genève, Switzerland.,3Cumming School of Medicine, University of Calgary, Calgary, Canada.,4MD/PhD Training Program, University of British Columbia, Vancouver, Canada
| | - Jocelyne Bloch
- 5Department of Neurosurgery, University Hospital of Lausanne (CHUV), Lausanne, Switzerland.,6Defitech Center for Interventional Neurotherapies, EPFL / CHUV, Lausanne, Switzerland
| | - Grégoire Courtine
- École polytechnique fédérale de Lausanne (EPFL), Campus Biotech, Center for Neuroprosthetics and Brain Mind Institute, 1202 Genève, Switzerland.,5Department of Neurosurgery, University Hospital of Lausanne (CHUV), Lausanne, Switzerland.,6Defitech Center for Interventional Neurotherapies, EPFL / CHUV, Lausanne, Switzerland
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