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Wu Y, Temple BA, Sevilla N, Zhang J, Zhu H, Zolotavin P, Jin Y, Duarte D, Sanders E, Azim E, Nimmerjahn A, Pfaff SL, Luan L, Xie C. Ultraflexible electrodes for recording neural activity in the mouse spinal cord during motor behavior. Cell Rep 2024; 43:114199. [PMID: 38728138 DOI: 10.1016/j.celrep.2024.114199] [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: 11/10/2023] [Revised: 03/10/2024] [Accepted: 04/22/2024] [Indexed: 05/12/2024] Open
Abstract
Implantable electrode arrays are powerful tools for directly interrogating neural circuitry in the brain, but implementing this technology in the spinal cord in behaving animals has been challenging due to the spinal cord's significant motion with respect to the vertebral column during behavior. Consequently, the individual and ensemble activity of spinal neurons processing motor commands remains poorly understood. Here, we demonstrate that custom ultraflexible 1-μm-thick polyimide nanoelectronic threads can conduct laminar recordings of many neuronal units within the lumbar spinal cord of unrestrained, freely moving mice. The extracellular action potentials have high signal-to-noise ratio, exhibit well-isolated feature clusters, and reveal diverse patterns of activity during locomotion. Furthermore, chronic recordings demonstrate the stable tracking of single units and their functional tuning over multiple days. This technology provides a path for elucidating how spinal circuits compute motor actions.
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Affiliation(s)
- Yu Wu
- Department of Electrical and Computer Engineering, Rice University, Houston, TX 77005, USA; Rice Neuroengineering Initiative, Rice University, Houston, TX 77030, USA
| | - Benjamin A Temple
- Gene Expression Laboratory, Salk Institute for Biological Studies, La Jolla, CA 92037, USA; Neurosciences Graduate Program, University of California, San Diego, La Jolla, CA 92037, USA
| | - Nicole Sevilla
- Rice Neuroengineering Initiative, Rice University, Houston, TX 77030, USA; Department of Bioengineering, Rice University, Houston, TX 77030, USA
| | - Jiaao Zhang
- Department of Electrical and Computer Engineering, Rice University, Houston, TX 77005, USA; Rice Neuroengineering Initiative, Rice University, Houston, TX 77030, USA
| | - Hanlin Zhu
- Department of Electrical and Computer Engineering, Rice University, Houston, TX 77005, USA; Rice Neuroengineering Initiative, Rice University, Houston, TX 77030, USA
| | - Pavlo Zolotavin
- Department of Electrical and Computer Engineering, Rice University, Houston, TX 77005, USA; Rice Neuroengineering Initiative, Rice University, Houston, TX 77030, USA
| | - Yifu Jin
- Department of Electrical and Computer Engineering, Rice University, Houston, TX 77005, USA; Rice Neuroengineering Initiative, Rice University, Houston, TX 77030, USA
| | - Daniela Duarte
- Waitt Advanced Biophotonics Center, Salk Institute for Biological Studies, La Jolla, CA 92037, USA
| | - Elischa Sanders
- Molecular Neurobiology Laboratory, Salk Institute for Biological Studies, La Jolla, CA 92037, USA
| | - Eiman Azim
- Molecular Neurobiology Laboratory, Salk Institute for Biological Studies, La Jolla, CA 92037, USA
| | - Axel Nimmerjahn
- Waitt Advanced Biophotonics Center, Salk Institute for Biological Studies, La Jolla, CA 92037, USA
| | - Samuel L Pfaff
- Gene Expression Laboratory, Salk Institute for Biological Studies, La Jolla, CA 92037, USA.
| | - Lan Luan
- Department of Electrical and Computer Engineering, Rice University, Houston, TX 77005, USA; Rice Neuroengineering Initiative, Rice University, Houston, TX 77030, USA; Department of Bioengineering, Rice University, Houston, TX 77030, USA.
| | - Chong Xie
- Department of Electrical and Computer Engineering, Rice University, Houston, TX 77005, USA; Rice Neuroengineering Initiative, Rice University, Houston, TX 77030, USA; Department of Bioengineering, Rice University, Houston, TX 77030, USA.
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2
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Liang F, Yu S, Pang S, Wang X, Jie J, Gao F, Song Z, Li B, Liao WH, Yin M. Non-human primate models and systems for gait and neurophysiological analysis. Front Neurosci 2023; 17:1141567. [PMID: 37188006 PMCID: PMC10175625 DOI: 10.3389/fnins.2023.1141567] [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: 01/10/2023] [Accepted: 04/11/2023] [Indexed: 05/17/2023] Open
Abstract
Brain-computer interfaces (BCIs) have garnered extensive interest and become a groundbreaking technology to restore movement, tactile sense, and communication in patients. Prior to their use in human subjects, clinical BCIs require rigorous validation and verification (V&V). Non-human primates (NHPs) are often considered the ultimate and widely used animal model for neuroscience studies, including BCIs V&V, due to their proximity to humans. This literature review summarizes 94 NHP gait analysis studies until 1 June, 2022, including seven BCI-oriented studies. Due to technological limitations, most of these studies used wired neural recordings to access electrophysiological data. However, wireless neural recording systems for NHPs enabled neuroscience research in humans, and many on NHP locomotion, while posing numerous technical challenges, such as signal quality, data throughout, working distance, size, and power constraint, that have yet to be overcome. Besides neurological data, motion capture (MoCap) systems are usually required in BCI and gait studies to capture locomotion kinematics. However, current studies have exclusively relied on image processing-based MoCap systems, which have insufficient accuracy (error: ≥4° and 9 mm). While the role of the motor cortex during locomotion is still unclear and worth further exploration, future BCI and gait studies require simultaneous, high-speed, accurate neurophysiological, and movement measures. Therefore, the infrared MoCap system which has high accuracy and speed, together with a high spatiotemporal resolution neural recording system, may expand the scope and improve the quality of the motor and neurophysiological analysis in NHPs.
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Affiliation(s)
- Fengyan Liang
- Key Laboratory of Biomedical Engineering of Hainan Province, School of Biomedical Engineering, Hainan University, Haikou, China
- Department of Rehabilitation Medicine, Affiliated Haikou Hospital of Xiangya Medical College, Central South University, Haikou, China
| | - Shanshan Yu
- Key Laboratory of Biomedical Engineering of Hainan Province, School of Biomedical Engineering, Hainan University, Haikou, China
| | - Siqi Pang
- Key Laboratory of Biomedical Engineering of Hainan Province, School of Biomedical Engineering, Hainan University, Haikou, China
| | - Xiao Wang
- Key Laboratory of Biomedical Engineering of Hainan Province, School of Biomedical Engineering, Hainan University, Haikou, China
| | - Jing Jie
- Key Laboratory of Biomedical Engineering of Hainan Province, School of Biomedical Engineering, Hainan University, Haikou, China
| | - Fei Gao
- Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Zhenhua Song
- Department of Rehabilitation Medicine, Affiliated Haikou Hospital of Xiangya Medical College, Central South University, Haikou, China
| | - Binbin Li
- Department of Rehabilitation Medicine, Affiliated Haikou Hospital of Xiangya Medical College, Central South University, Haikou, China
| | - Wei-Hsin Liao
- Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong, Shatin, China
| | - Ming Yin
- Key Laboratory of Biomedical Engineering of Hainan Province, School of Biomedical Engineering, Hainan University, Haikou, China
- *Correspondence: Ming Yin,
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3
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Shupe LE, Miles FP, Jones G, Yun R, Mishler J, Rembado I, Murphy RL, Perlmutter SI, Fetz EE. Neurochip3: An Autonomous Multichannel Bidirectional Brain-Computer Interface for Closed-Loop Activity-Dependent Stimulation. Front Neurosci 2021; 15:718465. [PMID: 34489634 PMCID: PMC8417105 DOI: 10.3389/fnins.2021.718465] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2021] [Accepted: 07/22/2021] [Indexed: 11/13/2022] Open
Abstract
Toward addressing many neuroprosthetic applications, the Neurochip3 (NC3) is a multichannel bidirectional brain-computer interface that operates autonomously and can support closed-loop activity-dependent stimulation. It consists of four circuit boards populated with off-the-shelf components and is sufficiently compact to be carried on the head of a non-human primate (NHP). NC3 has six main components: (1) an analog front-end with an Intan biophysical signal amplifier (16 differential or 32 single-ended channels) and a 3-axis accelerometer, (2) a digital control system comprised of a Cyclone V FPGA and Atmel SAM4 MCU, (3) a micro SD Card for 128 GB or more storage, (4) a 6-channel differential stimulator with ±60 V compliance, (5) a rechargeable battery pack supporting autonomous operation for up to 24 h and, (6) infrared transceiver and serial ports for communication. The NC3 and earlier versions have been successfully deployed in many closed-loop operations to induce synaptic plasticity and bridge lost biological connections, as well as deliver activity-dependent intracranial reinforcement. These paradigms to strengthen or replace impaired connections have many applications in neuroprosthetics and neurorehabilitation.
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Affiliation(s)
- Larry E Shupe
- Department of Physiology & Biophysics, University of Washington, Seattle, WA, United States.,Washington National Primate Research Center, University of Washington, Seattle, WA, United States
| | - Frank P Miles
- Washington National Primate Research Center, University of Washington, Seattle, WA, United States
| | - Geoff Jones
- Independent Researcher, Seattle, CA, United States
| | - Richy Yun
- Department of Bioengineering, University of Washington, Seattle, WA, United States
| | - Jonathan Mishler
- Department of Bioengineering, University of Washington, Seattle, WA, United States
| | - Irene Rembado
- Department of Physiology & Biophysics, University of Washington, Seattle, WA, United States
| | - R Logan Murphy
- Department of Physiology & Biophysics, University of Washington, Seattle, WA, United States
| | - Steve I Perlmutter
- Department of Physiology & Biophysics, University of Washington, Seattle, WA, United States.,Washington National Primate Research Center, University of Washington, Seattle, WA, United States
| | - Eberhard E Fetz
- Department of Physiology & Biophysics, University of Washington, Seattle, WA, United States.,Washington National Primate Research Center, University of Washington, Seattle, WA, United States.,Department of Bioengineering, University of Washington, Seattle, WA, United States
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4
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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: 52] [Impact Index Per Article: 13.0] [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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5
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Vizvari Z, Toth A, Sari Z, Klincsik M, Kuljic B, Szakall T, Odry A, Mathe K, Szabo I, Karadi Z, Odry P. Measurement system with real time data converter for conversion of I 2 S data stream to UDP protocol data. Heliyon 2020; 6:e03760. [PMID: 32346631 PMCID: PMC7182730 DOI: 10.1016/j.heliyon.2020.e03760] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2019] [Revised: 01/01/2020] [Accepted: 04/06/2020] [Indexed: 01/24/2023] Open
Abstract
A central goal of systems neuroscience is to simultaneously measure the activities of all achievable neurons in the brain at millisecond resolution in freely moving animals. This paper describes a protocol converter which is part of a measurement acquisition system for multichannel real time recording of brain signals. In practice, in such techniques, a primary consideration of reliability leads to great necessity towards increasing the sampling rate of these signals while simultaneously increasing the resolution of A/D conversion to 24 bits or even to the unprecedented 32 bits per sample. In fact, this was the guiding principle for our team in the present study. By increasing the temporal and amplitude resolution, it is supposed that we get enabled to discover or recognize and identify new signal components which have previously been masked at a "low" temporal and amplitude resolution, and these new signal components, in the future, are likely to contribute to a deeper understanding of the workings of the brain.
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Affiliation(s)
- Zoltan Vizvari
- Department of Environmental Engineering, Faculty of Engineering and Information Technology, University of Pécs, Boszorkány str. 2, H-7624 Pécs, Hungary
| | - Attila Toth
- Institute of Physiology, Medical School, University of Pécs, Szigeti str 12, H-7624 Pécs, Hungary
| | - Zoltan Sari
- Department of Information Technology, Faculty of Engineering and Information Technology, University of Pécs, Boszorkány str. 2, H-7624 Pécs, Hungary
| | - Mihaly Klincsik
- Department of Mathematics, Faculty of Engineering and Information Technology, University of Pécs, Boszorkány str. 2, H-7624 Pécs, Hungary
| | - Bojan Kuljic
- College of Applied Sciences, Subotica Tech, Marka Oreškoviċa 16, 24000 Subotica, Serbia
| | - Tibor Szakall
- College of Applied Sciences, Subotica Tech, Marka Oreškoviċa 16, 24000 Subotica, Serbia
| | - Akos Odry
- Institute of Information Technology, University of Dunaujvaros, Táncsics M. str. 1/A, H-2401 Dunaújváros, Hungary
| | - Kalman Mathe
- Faculty of Engineering and Information Technology, University of Pécs, Boszorkány str. 2, H-7624 Pécs, Hungary
| | - Imre Szabo
- Department of Behavioural Sciences, Medical School, University of Pécs, Szigeti str 12, H-7624 Pécs, Hungary
| | - Zoltan Karadi
- Institute of Physiology, Medical School, University of Pécs, Szigeti str 12, H-7624 Pécs, Hungary
| | - Peter Odry
- Institute of Information Technology, University of Dunaujvaros, Táncsics M. str. 1/A, H-2401 Dunaújváros, Hungary
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6
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Nam J, Lim HK, Kim NH, Park JK, Kang ES, Kim YT, Heo C, Lee OS, Kim SG, Yun WS, Suh M, Kim YH. Supramolecular Peptide Hydrogel-Based Soft Neural Interface Augments Brain Signals through a Three-Dimensional Electrical Network. ACS NANO 2020; 14:664-675. [PMID: 31895542 DOI: 10.1021/acsnano.9b07396] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/29/2023]
Abstract
Recording neural activity from the living brain is of great interest in neuroscience for interpreting cognitive processing or neurological disorders. Despite recent advances in neural technologies, development of a soft neural interface that integrates with neural tissues, increases recording sensitivity, and prevents signal dissipation still remains a major challenge. Here, we introduce a biocompatible, conductive, and biostable neural interface, a supramolecular β-peptide-based hydrogel that allows signal amplification via tight neural/hydrogel contact without neuroinflammation. The non-biodegradable β-peptide forms a multihierarchical structure with conductive nanomaterial, creating a three-dimensional electrical network, which can augment brain signal efficiently. By achieving seamless integration in brain tissue with increased contact area and tight neural tissue coupling, the epidural and intracortical neural signals recorded with the hydrogel were augmented, especially in the high frequency range. Overall, our tissuelike chronic neural interface will facilitate a deeper understanding of brain oscillation in broad brain states and further lead to more efficient brain-computer interfaces.
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Affiliation(s)
- Jiyoung Nam
- Center for Neuroscience Imaging Research , Institute for Basic Science (IBS) , Suwon 16419 , Korea
- SKKU Advanced Institute of Nanotechnology (SAINT) , Sungkyunkwan University , Suwon 16419 , Korea
| | - Hyun-Kyoung Lim
- Center for Neuroscience Imaging Research , Institute for Basic Science (IBS) , Suwon 16419 , Korea
- Department of Biological Sciences , Sungkyunkwan University , Suwon 16419 , Korea
| | - Nam Hyeong Kim
- SKKU Advanced Institute of Nanotechnology (SAINT) , Sungkyunkwan University , Suwon 16419 , Korea
| | - Jong Kwan Park
- Department of Chemistry , Sungkyunkwan University , Suwon 16419 , Korea
| | - Eun Sung Kang
- SKKU Advanced Institute of Nanotechnology (SAINT) , Sungkyunkwan University , Suwon 16419 , Korea
| | - Yong-Tae Kim
- SKKU Advanced Institute of Nanotechnology (SAINT) , Sungkyunkwan University , Suwon 16419 , Korea
| | - Chaejeong Heo
- Center for Neuroscience Imaging Research , Institute for Basic Science (IBS) , Suwon 16419 , Korea
| | - One-Sun Lee
- Qatar Environment and Energy Research Institute , Hamad Bin Khalifa University , Doha , Qatar
| | - Seong-Gi Kim
- Center for Neuroscience Imaging Research , Institute for Basic Science (IBS) , Suwon 16419 , Korea
- Department of Biomedical Engineering , Sungkyunkwan University , Suwon 16419 , Korea
| | - Wan Soo Yun
- Department of Chemistry , Sungkyunkwan University , Suwon 16419 , Korea
| | - Minah Suh
- Center for Neuroscience Imaging Research , Institute for Basic Science (IBS) , Suwon 16419 , Korea
- Department of Biomedical Engineering , Sungkyunkwan University , Suwon 16419 , Korea
- Biomedical Institute for Convergence at SKKU (BICS) , Sungkyunkwan University , Suwon 16419 , Korea
| | - Yong Ho Kim
- Center for Neuroscience Imaging Research , Institute for Basic Science (IBS) , Suwon 16419 , Korea
- SKKU Advanced Institute of Nanotechnology (SAINT) , Sungkyunkwan University , Suwon 16419 , Korea
- Department of Chemistry , Sungkyunkwan University , Suwon 16419 , Korea
- Biomedical Institute for Convergence at SKKU (BICS) , Sungkyunkwan University , Suwon 16419 , Korea
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7
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Advances in Penetrating Multichannel Microelectrodes Based on the Utah Array Platform. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2019; 1101:1-40. [PMID: 31729670 DOI: 10.1007/978-981-13-2050-7_1] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/10/2023]
Abstract
The Utah electrode array (UEA) and its many derivatives have become a gold standard for high-channel count bi-directional neural interfaces, in particular in human subject applications. The chapter provides a brief overview of leading electrode concepts and the context in which the UEA has to be understood. It goes on to discuss the key advances and developments of the UEA platform in the past 15 years, as well as novel wireless and system integration technologies that will merge into future generations of fully integrated devices. Aspects covered include novel device architectures that allow scaling of channel count and density of electrode contacts, material improvements to substrate, electrode contacts, and encapsulation. Further subjects are adaptations of the UEA platform to support IR and optogenetic simulation as well as an improved understanding of failure modes and methods to test and accelerate degradation in vitro such as to better predict device failure and lifetime in vivo.
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8
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An Implantable Wireless Neural Interface System for Simultaneous Recording and Stimulation of Peripheral Nerve with a Single Cuff Electrode. SENSORS 2017; 18:s18010001. [PMID: 29267230 PMCID: PMC5795569 DOI: 10.3390/s18010001] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/27/2017] [Revised: 12/15/2017] [Accepted: 12/15/2017] [Indexed: 12/02/2022]
Abstract
Recently, implantable devices have become widely used in neural prostheses because they eliminate endemic drawbacks of conventional percutaneous neural interface systems. However, there are still several issues to be considered: low-efficiency wireless power transmission; wireless data communication over restricted operating distance with high power consumption; and limited functionality, working either as a neural signal recorder or as a stimulator. To overcome these issues, we suggest a novel implantable wireless neural interface system for simultaneous neural signal recording and stimulation using a single cuff electrode. By using widely available commercial off-the-shelf (COTS) components, an easily reconfigurable implantable wireless neural interface system was implemented into one compact module. The implantable device includes a wireless power consortium (WPC)-compliant power transmission circuit, a medical implant communication service (MICS)-band-based radio link and a cuff-electrode path controller for simultaneous neural signal recording and stimulation. During in vivo experiments with rabbit models, the implantable device successfully recorded and stimulated the tibial and peroneal nerves while communicating with the external device. The proposed system can be modified for various implantable medical devices, especially such as closed-loop control based implantable neural prostheses requiring neural signal recording and stimulation at the same time.
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9
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Im C, Koh CS, Park HY, Shin J, Jun S, Jung HH, Ahn JM, Chang JW, Kim YJ, Shin HC. Development of wireless neural interface system. Biomed Eng Lett 2017. [DOI: 10.1007/s13534-016-0232-4] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022] Open
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10
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Kohler F, Gkogkidis CA, Bentler C, Wang X, Gierthmuehlen M, Fischer J, Stolle C, Reindl LM, Rickert J, Stieglitz T, Ball T, Schuettler M. Closed-loop interaction with the cerebral cortex: a review of wireless implant technology. BRAIN-COMPUTER INTERFACES 2017. [DOI: 10.1080/2326263x.2017.1338011] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
Affiliation(s)
- Fabian Kohler
- CorTec GmbH, Freiburg, Germany
- Laboratory for Biomedical Microtechnology, Department of Microsystems Engineering, Faculty of Engineering, University of Freiburg, Freiburg, Germany
| | - C. Alexis Gkogkidis
- Translational Neurotechnology Lab, Department of Neurosurgery, Medical Center – University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
- Laboratory for Biomedical Microtechnology, Department of Microsystems Engineering, Faculty of Engineering, University of Freiburg, Freiburg, Germany
| | - Christian Bentler
- Laboratory for Biomedical Microtechnology, Department of Microsystems Engineering, Faculty of Engineering, University of Freiburg, Freiburg, Germany
| | - Xi Wang
- Translational Neurotechnology Lab, Department of Neurosurgery, Medical Center – University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
- Laboratory for Biomedical Microtechnology, Department of Microsystems Engineering, Faculty of Engineering, University of Freiburg, Freiburg, Germany
| | - Mortimer Gierthmuehlen
- Translational Neurotechnology Lab, Department of Neurosurgery, Medical Center – University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | | | | | - Leonhard M. Reindl
- Laboratory for Electrical Instrumentation, Department of Microsystems Engineering, Faculty of Engineering, University of Freiburg, Freiburg, Germany
| | | | - Thomas Stieglitz
- Laboratory for Biomedical Microtechnology, Department of Microsystems Engineering, Faculty of Engineering, University of Freiburg, Freiburg, Germany
| | - Tonio Ball
- Translational Neurotechnology Lab, Department of Neurosurgery, Medical Center – University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
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11
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Moorjani S. Miniaturized Technologies for Enhancement of Motor Plasticity. Front Bioeng Biotechnol 2016; 4:30. [PMID: 27148525 PMCID: PMC4834582 DOI: 10.3389/fbioe.2016.00030] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2016] [Accepted: 03/21/2016] [Indexed: 11/13/2022] Open
Abstract
The idea that the damaged brain can functionally reorganize itself – so when one part fails, there lies the possibility for another to substitute – is an exciting discovery of the twentieth century. We now know that motor circuits once presumed to be hardwired are not, and motor-skill learning, exercise, and even mental rehearsal of motor tasks can turn genes on or off to shape brain architecture, function, and, consequently, behavior. This is a very significant alteration from our previously static view of the brain and has profound implications for the rescue of function after a motor injury. Presentation of the right cues, applied in relevant spatiotemporal geometries, is required to awaken the dormant plastic forces essential for repair. The focus of this review is to highlight some of the recent progress in neural interfaces designed to harness motor plasticity, and the role of miniaturization in development of strategies that engage diverse elements of the neuronal machinery to synergistically facilitate recovery of function after motor damage.
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Affiliation(s)
- Samira Moorjani
- Department of Physiology and Biophysics, and the Washington National Primate Research Center, University of Washington , Seattle, WA , USA
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12
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Cao Y, Rakhilin N, Gordon PH, Shen X, Kan EC. A real-time spike classification method based on dynamic time warping for extracellular enteric neural recording with large waveform variability. J Neurosci Methods 2016; 261:97-109. [PMID: 26719239 PMCID: PMC4749467 DOI: 10.1016/j.jneumeth.2015.12.006] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2015] [Revised: 11/05/2015] [Accepted: 12/12/2015] [Indexed: 12/16/2022]
Abstract
BACKGROUND Computationally efficient spike recognition methods are required for real-time analysis of extracellular neural recordings. The enteric nervous system (ENS) is important to human health but less well-understood with few appropriate spike recognition algorithms due to large waveform variability. NEW METHOD Here we present a method based on dynamic time warping (DTW) with high tolerance to variability in time and magnitude. Adaptive temporal gridding for "fastDTW" in similarity calculation significantly reduces the computational cost. The automated threshold selection allows for real-time classification for extracellular recordings. RESULTS Our method is first evaluated on synthesized data at different noise levels, improving both classification accuracy and computational complexity over the conventional cross-correlation based template-matching method (CCTM) and PCA+k-means clustering without time warping. Our method is then applied to analyze the mouse enteric neural recording with mechanical and chemical stimuli. Successful classification of biphasic and monophasic spikes is achieved even when the spike variability is larger than millisecond in width and millivolt in magnitude. COMPARISON WITH EXISTING METHOD(S) In comparison with conventional template matching and clustering methods, the fastDTW method is computationally efficient with high tolerance to waveform variability. CONCLUSIONS We have developed an adaptive fastDTW algorithm for real-time spike classification of ENS recording with large waveform variability against colony motility, ambient changes and cellular heterogeneity.
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Affiliation(s)
- Yingqiu Cao
- School of Electrical and Computer Engineering, Cornell University, Ithaca, NY 14853, USA.
| | - Nikolai Rakhilin
- School of Electrical and Computer Engineering, Cornell University, Ithaca, NY 14853, USA
| | - Philip H Gordon
- School of Electrical and Computer Engineering, Cornell University, Ithaca, NY 14853, USA
| | - Xiling Shen
- Department of Biomedical Engineering, Duke University, Durham, NC 27708, USA
| | - Edwin C Kan
- School of Electrical and Computer Engineering, Cornell University, Ithaca, NY 14853, USA
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Irwin ZT, Thompson DE, Schroeder KE, Tat DM, Hassani A, Bullard AJ, Woo SL, Urbanchek MG, Sachs AJ, Cederna PS, Stacey WC, Patil PG, Chestek CA. Enabling Low-Power, Multi-Modal Neural Interfaces Through a Common, Low-Bandwidth Feature Space. IEEE Trans Neural Syst Rehabil Eng 2015; 24:521-31. [PMID: 26600160 DOI: 10.1109/tnsre.2015.2501752] [Citation(s) in RCA: 31] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
Brain-Machine Interfaces (BMIs) have shown great potential for generating prosthetic control signals. Translating BMIs into the clinic requires fully implantable, wireless systems; however, current solutions have high power requirements which limit their usability. Lowering this power consumption typically limits the system to a single neural modality, or signal type, and thus to a relatively small clinical market. Here, we address both of these issues by investigating the use of signal power in a single narrow frequency band as a decoding feature for extracting information from electrocorticographic (ECoG), electromyographic (EMG), and intracortical neural data. We have designed and tested the Multi-modal Implantable Neural Interface (MINI), a wireless recording system which extracts and transmits signal power in a single, configurable frequency band. In prerecorded datasets, we used the MINI to explore low frequency signal features and any resulting tradeoff between power savings and decoding performance losses. When processing intracortical data, the MINI achieved a power consumption 89.7% less than a more typical system designed to extract action potential waveforms. When processing ECoG and EMG data, the MINI achieved similar power reductions of 62.7% and 78.8%. At the same time, using the single signal feature extracted by the MINI, we were able to decode all three modalities with less than a 9% drop in accuracy relative to using high-bandwidth, modality-specific signal features. We believe this system architecture can be used to produce a viable, cost-effective, clinical BMI.
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Fernandez-Leon JA, Parajuli A, Franklin R, Sorenson M, Felleman DJ, Hansen BJ, Hu M, Dragoi V. A wireless transmission neural interface system for unconstrained non-human primates. J Neural Eng 2015; 12:056005. [PMID: 26269496 DOI: 10.1088/1741-2560/12/5/056005] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
OBJECTIVE Studying the brain in large animal models in a restrained laboratory rig severely limits our capacity to examine brain circuits in experimental and clinical applications. APPROACH To overcome these limitations, we developed a high-fidelity 96-channel wireless system to record extracellular spikes and local field potentials from the neocortex. A removable, external case of the wireless device is attached to a titanium pedestal placed in the animal skull. Broadband neural signals are amplified, multiplexed, and continuously transmitted as TCP/IP data at a sustained rate of 24 Mbps. A Xilinx Spartan 6 FPGA assembles the digital signals into serial data frames for transmission at 20 kHz though an 802.11n wireless data link on a frequency-shift key-modulated signal at 5.7-5.8 GHz to a receiver up to 10 m away. The system is powered by two CR123A, 3 V batteries for 2 h of operation. MAIN RESULTS We implanted a multi-electrode array in visual area V4 of one anesthetized monkey (Macaca fascicularis) and in the dorsolateral prefrontal cortex (dlPFC) of a freely moving monkey (Macaca mulatta). The implanted recording arrays were electrically stable and delivered broadband neural data over a year of testing. For the first time, we compared dlPFC neuronal responses to the same set of stimuli (food reward) in restrained and freely moving conditions. Although we did not find differences in neuronal responses as a function of reward type in the restrained and unrestrained conditions, there were significant differences in correlated activity. This demonstrates that measuring neural responses in freely moving animals can capture phenomena that are absent in the traditional head-fixed paradigm. SIGNIFICANCE We implemented a wireless neural interface for multi-electrode recordings in freely moving non-human primates, which can potentially move systems neuroscience to a new direction by allowing one to record neural signals while animals interact with their environment.
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Affiliation(s)
- Jose A Fernandez-Leon
- Department of Neurobiology and Anatomy, University of Texas-Houston Medical School, 6431 Fannin St., Houston, TX 77030, USA. Centre for Computational Neuroscience and Robotics, University of Sussex, Brighton BN1 9QG, UK
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15
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Kiourti A, Lee CWL, Chae J, Volakis JL. A Wireless Fully Passive Neural Recording Device for Unobtrusive Neuropotential Monitoring. IEEE Trans Biomed Eng 2015. [PMID: 26208260 DOI: 10.1109/tbme.2015.2458583] [Citation(s) in RCA: 49] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
GOAL We propose a novel wireless fully passive neural recording device for unobtrusive neuropotential monitoring. Previous work demonstrated the feasibility of monitoring emulated brain signals in a wireless fully passive manner. In this paper, we propose a novel realistic recorder that is significantly smaller and much more sensitive. METHODS The proposed recorder utilizes a highly efficient microwave backscattering method and operates without any formal power supply or regulating elements. Also, no intracranial wires or cables are required. In-vitro testing is performed inside a four-layer head phantom (skin, bone, gray matter, and white matter). RESULTS Compared to our former implementation, the neural recorder proposed in this study has the following improved features: 1) 59% smaller footprint, 2) up to 20-dB improvement in neuropotential detection sensitivity, and 3) encapsulation in biocompatible polymer. CONCLUSION For the first time, temporal emulated neuropotentials as low as 63 μVpp can be detected in a wireless fully passive manner. Remarkably, the high-sensitivity achieved in this study implies reading of most neural signals generated by the human brain. SIGNIFICANCE The proposed recorder brings forward transformational possibilities in wireless fully passive neural detection for a very wide range of applications (e.g., epilepsy, Alzheimer's, mental disorders, etc.).
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Yin M, Li H, Bull C, Borton DA, Aceros J, Larson L, Nurmikko AV. An externally head-mounted wireless neural recording device for laboratory animal research and possible human clinical use. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2015; 2013:3109-14. [PMID: 24110386 DOI: 10.1109/embc.2013.6610199] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
In this paper we present a new type of head-mounted wireless neural recording device in a highly compact package, dedicated for untethered laboratory animal research and designed for future mobile human clinical use. The device, which takes its input from an array of intracortical microelectrode arrays (MEA) has ninety-seven broadband parallel neural recording channels and was integrated on to two custom designed printed circuit boards. These house several low power, custom integrated circuits, including a preamplifier ASIC, a controller ASIC, plus two SAR ADCs, a 3-axis accelerometer, a 48MHz clock source, and a Manchester encoder. Another ultralow power RF chip supports an OOK transmitter with the center frequency tunable from 3GHz to 4GHz, mounted on a separate low loss dielectric board together with a 3V LDO, with output fed to a UWB chip antenna. The IC boards were interconnected and packaged in a polyether ether ketone (PEEK) enclosure which is compatible with both animal and human use (e.g. sterilizable). The entire system consumes 17mA from a 1.2Ahr 3.6V Li-SOCl2 1/2AA battery, which operates the device for more than 2 days. The overall system includes a custom RF receiver electronics which are designed to directly interface with any number of commercial (or custom) neural signal processors for multi-channel broadband neural recording. Bench-top measurements and in vivo testing of the device in rhesus macaques are presented to demonstrate the performance of the wireless neural interface.
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Shaeri MA, Sodagar AM. A Method for Compression of Intra-Cortically-Recorded Neural Signals Dedicated to Implantable Brain–Machine Interfaces. IEEE Trans Neural Syst Rehabil Eng 2015; 23:485-97. [DOI: 10.1109/tnsre.2014.2355139] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
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18
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Yin M, Borton DA, Komar J, Agha N, Lu Y, Li H, Laurens J, Lang Y, Li Q, Bull C, Larson L, Rosler D, Bezard E, Courtine G, Nurmikko AV. Wireless neurosensor for full-spectrum electrophysiology recordings during free behavior. Neuron 2014; 84:1170-82. [PMID: 25482026 DOI: 10.1016/j.neuron.2014.11.010] [Citation(s) in RCA: 93] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/03/2014] [Indexed: 10/24/2022]
Abstract
Brain recordings in large animal models and humans typically rely on a tethered connection, which has restricted the spectrum of accessible experimental and clinical applications. To overcome this limitation, we have engineered a compact, lightweight, high data rate wireless neurosensor capable of recording the full spectrum of electrophysiological signals from the cortex of mobile subjects. The wireless communication system exploits a spatially distributed network of synchronized receivers that is scalable to hundreds of channels and vast environments. To demonstrate the versatility of our wireless neurosensor, we monitored cortical neuron populations in freely behaving nonhuman primates during natural locomotion and sleep-wake transitions in ecologically equivalent settings. The interface is electrically safe and compatible with the majority of existing neural probes, which may support previously inaccessible experimental and clinical research.
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Affiliation(s)
- Ming Yin
- School of Engineering, Brown University, 184 Hope Street, Providence, RI 02912, USA
| | - David A Borton
- School of Engineering, Brown University, 184 Hope Street, Providence, RI 02912, USA; Center for Neuroprosthetics, Swiss Federal Institute of Technology (EPFL), Lausanne, CH-1015 Vaud, Switzerland
| | - Jacob Komar
- School of Engineering, Brown University, 184 Hope Street, Providence, RI 02912, USA
| | - Naubahar Agha
- School of Engineering, Brown University, 184 Hope Street, Providence, RI 02912, USA
| | - Yao Lu
- School of Engineering, Brown University, 184 Hope Street, Providence, RI 02912, USA
| | - Hao Li
- Marvell Semiconductor, 5488 Marvell Lane, Santa Clara, CA 95054, USA
| | - Jean Laurens
- Center for Neuroprosthetics, Swiss Federal Institute of Technology (EPFL), Lausanne, CH-1015 Vaud, Switzerland
| | - Yiran Lang
- Institute of Neurodegenerative diseases, Bordeaux Institut of Neuroscience, 146 Rue Léo Saignat, UMR, 33076 Bordeaux, France
| | - Qin Li
- Motac Neuroscience, Lloyd Street N., Manchester, M15 6SE, UK; Institute of Laboratory Animal Sciences, China Academy of Medical Sciences, NO. 9, Dongdan san tiao, Dongcheng District, 100730 Beijing, China
| | - Christopher Bull
- School of Engineering, Brown University, 184 Hope Street, Providence, RI 02912, USA
| | - Lawrence Larson
- School of Engineering, Brown University, 184 Hope Street, Providence, RI 02912, USA
| | - David Rosler
- School of Engineering, Brown University, 184 Hope Street, Providence, RI 02912, USA; Center for Neurorestoration and Neurotechnology, Rehabilitation R&D Service, Department of Veterans Affairs Medical Center, 830 Chalkstone Avenue, Providence, RI 02908, USA
| | - Erwan Bezard
- Institute of Neurodegenerative diseases, Bordeaux Institut of Neuroscience, 146 Rue Léo Saignat, UMR, 33076 Bordeaux, France; Institute of Laboratory Animal Sciences, China Academy of Medical Sciences, NO. 9, Dongdan san tiao, Dongcheng District, 100730 Beijing, China
| | - Grégoire Courtine
- Center for Neuroprosthetics, Swiss Federal Institute of Technology (EPFL), Lausanne, CH-1015 Vaud, Switzerland
| | - Arto V Nurmikko
- School of Engineering, Brown University, 184 Hope Street, Providence, RI 02912, USA.
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Abstract
With the growing interdependence between medicine and technology, the prospect of connecting machines to the human brain is rapidly being realized. The field of neuroprosthetics is transitioning from the proof of concept stage to the development of advanced clinical treatments. In one area of brain-machine interfaces (BMIs) related to the motor system, also termed ‘motor neuroprosthetics’, research successes with implanted microelectrodes in animals have demonstrated immense potential for restoring motor deficits. Early human trials have also begun, with some success but also highlighting several technical challenges. Here we review the concepts and anatomy underlying motor BMI designs, review their early use in clinical applications, and offer a framework to evaluate these technologies in order to predict their eventual clinical utility. Ultimately, we hope to help neuroscience clinicians understand and participate in this burgeoning field.
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20
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Paraskevopoulou SE, Wu D, Eftekhar A, Constandinou TG. Hierarchical Adaptive Means (HAM) clustering for hardware-efficient, unsupervised and real-time spike sorting. J Neurosci Methods 2014; 235:145-56. [DOI: 10.1016/j.jneumeth.2014.07.004] [Citation(s) in RCA: 24] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2014] [Revised: 07/01/2014] [Accepted: 07/03/2014] [Indexed: 11/25/2022]
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Angotzi GN, Boi F, Zordan S, Bonfanti A, Vato A. A programmable closed-loop recording and stimulating wireless system for behaving small laboratory animals. Sci Rep 2014; 4:5963. [PMID: 25096831 PMCID: PMC4123143 DOI: 10.1038/srep05963] [Citation(s) in RCA: 27] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2014] [Accepted: 07/10/2014] [Indexed: 11/08/2022] Open
Abstract
A portable 16-channels microcontroller-based wireless system for a bi-directional interaction with the central nervous system is presented in this work. The device is designed to be used with freely behaving small laboratory animals and allows recording of spontaneous and evoked neural activity wirelessly transmitted and stored on a personal computer. Biphasic current stimuli with programmable duration, frequency and amplitude may be triggered in real-time on the basis of the recorded neural activity as well as by the animal behavior within a specifically designed experimental setup. An intuitive graphical user interface was developed to configure and to monitor the whole system. The system was successfully tested through bench tests and in vivo measurements on behaving rats chronically implanted with multi-channels microwire arrays.
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Affiliation(s)
- Gian Nicola Angotzi
- Department of Neuroscience and Brain Technologies, Istituto Italiano di Tecnologia, Genova, Italy
- Department of Robotics, Brain and Cognitive Sciences, Istituto Italiano di Tecnologia, Genova, Italy
| | - Fabio Boi
- Center for Neuroscience and Cognitive Systems @UniTn, Istituto Italiano di Tecnologia, Rovereto, Italy
- Department of Robotics, Brain and Cognitive Sciences, Istituto Italiano di Tecnologia, Genova, Italy
| | - Stefano Zordan
- Department of Robotics, Brain and Cognitive Sciences, Istituto Italiano di Tecnologia, Genova, Italy
| | - Andrea Bonfanti
- Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Italy
| | - Alessandro Vato
- Center for Neuroscience and Cognitive Systems @UniTn, Istituto Italiano di Tecnologia, Rovereto, Italy
- Department of Robotics, Brain and Cognitive Sciences, Istituto Italiano di Tecnologia, Genova, Italy
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22
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Mestais CS, Charvet G, Sauter-Starace F, Foerster M, Ratel D, Benabid AL. WIMAGINE: wireless 64-channel ECoG recording implant for long term clinical applications. IEEE Trans Neural Syst Rehabil Eng 2014; 23:10-21. [PMID: 25014960 DOI: 10.1109/tnsre.2014.2333541] [Citation(s) in RCA: 78] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
A wireless 64-channel ElectroCorticoGram (ECoG) recording implant named WIMAGINE has been designed for various clinical applications. The device is aimed at interfacing a cortical electrode array to an external computer for neural recording and control applications. This active implantable medical device is able to record neural activity on 64 electrodes with selectable gain and sampling frequency, with less than 1 μV(RMS) input referred noise in the [0.5 Hz - 300 Hz] band. It is powered remotely through an inductive link at 13.56 MHz which provides up to 100 mW. The digitized data is transmitted wirelessly to a custom designed base station connected to a PC. The hermetic housing and the antennae have been designed and optimized to ease the surgery. The design of this implant takes into account all the requirements of a clinical trial, in particular safety, reliability, and compliance with the regulations applicable to class III AIMD. The main features of this WIMAGINE implantable device and its architecture are presented, as well as its functional performances and long-term biocompatibility results.
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23
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Navajas J, Barsakcioglu DY, Eftekhar A, Jackson A, Constandinou TG, Quian Quiroga R. Minimum requirements for accurate and efficient real-time on-chip spike sorting. J Neurosci Methods 2014; 230:51-64. [PMID: 24769170 PMCID: PMC4151286 DOI: 10.1016/j.jneumeth.2014.04.018] [Citation(s) in RCA: 40] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2014] [Revised: 04/11/2014] [Accepted: 04/14/2014] [Indexed: 11/30/2022]
Abstract
BACKGROUND Extracellular recordings are performed by inserting electrodes in the brain, relaying the signals to external power-demanding devices, where spikes are detected and sorted in order to identify the firing activity of different putative neurons. A main caveat of these recordings is the necessity of wires passing through the scalp and skin in order to connect intracortical electrodes to external amplifiers. The aim of this paper is to evaluate the feasibility of an implantable platform (i.e., a chip) with the capability to wirelessly transmit the neural signals and perform real-time on-site spike sorting. NEW METHOD We computationally modelled a two-stage implementation for online, robust, and efficient spike sorting. In the first stage, spikes are detected on-chip and streamed to an external computer where mean templates are created and sent back to the chip. In the second stage, spikes are sorted in real-time through template matching. RESULTS We evaluated this procedure using realistic simulations of extracellular recordings and describe a set of specifications that optimise performance while keeping to a minimum the signal requirements and the complexity of the calculations. COMPARISON WITH EXISTING METHODS A key bottleneck for the development of long-term BMIs is to find an inexpensive method for real-time spike sorting. Here, we simulated a solution to this problem that uses both offline and online processing of the data. CONCLUSIONS Hardware implementations of this method therefore enable low-power long-term wireless transmission of multiple site extracellular recordings, with application to wireless BMIs or closed-loop stimulation designs.
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Affiliation(s)
- Joaquin Navajas
- Centre for Systems Neuroscience, University of Leicester, 9 Salisbury Road, LE1 7QR, United Kingdom.
| | - Deren Y Barsakcioglu
- Centre for Bio-Inspired Technology, Department of Electrical and Electronic Engineering, Imperial College London, SW7 2AZ, United Kingdom
| | - Amir Eftekhar
- Centre for Bio-Inspired Technology, Department of Electrical and Electronic Engineering, Imperial College London, SW7 2AZ, United Kingdom
| | - Andrew Jackson
- Institute of Neuroscience, Newcastle University, Newcastle-upon-Tyne NE2 4HH, United Kingdom
| | - Timothy G Constandinou
- Centre for Bio-Inspired Technology, Department of Electrical and Electronic Engineering, Imperial College London, SW7 2AZ, United Kingdom
| | - Rodrigo Quian Quiroga
- Centre for Systems Neuroscience, University of Leicester, 9 Salisbury Road, LE1 7QR, United Kingdom
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Bensmaia SJ, Miller LE. Restoring sensorimotor function through intracortical interfaces: progress and looming challenges. Nat Rev Neurosci 2014; 15:313-25. [PMID: 24739786 DOI: 10.1038/nrn3724] [Citation(s) in RCA: 260] [Impact Index Per Article: 26.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
The loss of a limb or paralysis resulting from spinal cord injury has devastating consequences on quality of life. One approach to restoring lost sensory and motor abilities in amputees and patients with tetraplegia is to supply them with implants that provide a direct interface with the CNS. Such brain-machine interfaces might enable a patient to exert voluntary control over a prosthetic or robotic limb or over the electrically induced contractions of paralysed muscles. A parallel interface could convey sensory information about the consequences of these movements back to the patient. Recent developments in the algorithms that decode motor intention from neuronal activity and in approaches to convey sensory feedback by electrically stimulating neurons, using biomimetic and adaptation-based approaches, have shown the promise of invasive interfaces with sensorimotor cortices, although substantial challenges remain.
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Affiliation(s)
- Sliman J Bensmaia
- Department of Organismal Biology and Anatomy, and Committee on Computational Neuroscience, University of Chicago, Chicago, Illinois 60637, USA
| | - Lee E Miller
- 1] Department of Physical Medicine and Rehabilitation, and Department of Physiology, Feinberg School of Medicine, Northwestern University, Chicago, Illinois 60611, USA. [2] Department of Biomedical Engineering, Northwestern University, Evanston, Illinois 60208, USA
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25
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Judy M, Sodagar AM, Lotfi R, Sawan M. Nonlinear Signal-Specific ADC for Efficient Neural Recording in Brain-Machine Interfaces. IEEE TRANSACTIONS ON BIOMEDICAL CIRCUITS AND SYSTEMS 2014; 8:371-381. [PMID: 23925374 DOI: 10.1109/tbcas.2013.2270178] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/02/2023]
Abstract
A nonlinear ADC dedicated to the digitization of neural signals in implantable brain-machine interfaces is presented. Benefitting from an exponential quantization function, effective resolution of the proposed ADC in the digitization of action potentials is almost 2 bits more than its physical number of bits. Hence, it is shown in this paper that the choice of a proper nonlinear quantization function helps reduce the outgoing bit rate carrying the recorded neural data. Another major benefit of digitizing neural signals using the proposed signal-specific ADC is the considerable reduction in the background noise of the neural signal. The 8-b exponential ADC reported in this paper digitizes large action potentials with maximum resolution of 10.5 bits , while quantizing the small background noise is performed with a resolution of as low as 3 bits. Fully-integrated version of the circuit was designed and fabricated in a 0.18-μm CMOS process, occupying 0.036 mm(2) silicon area. Designed based on a two-step successive-approximation register ADC architecture, the proposed ADC employs a piecewise-linear approximation of the target exponential function for quantization. Operating at a sampling frequency of 25 kS/s (typical for intra-cortical neural recording) and with a supply voltage of 1.8 V, the entire chip, including the ADC and reference circuits, dissipates 87.2 μW. According to the experiments, Noise-Content-Reduction Ratio (NCRR) of the ADC is 41.1 dB.
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26
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Sankar V, Patrick E, Dieme R, Sanchez JC, Prasad A, Nishida T. Electrode impedance analysis of chronic tungsten microwire neural implants: understanding abiotic vs. biotic contributions. FRONTIERS IN NEUROENGINEERING 2014; 7:13. [PMID: 24847248 PMCID: PMC4021112 DOI: 10.3389/fneng.2014.00013] [Citation(s) in RCA: 49] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/31/2014] [Accepted: 04/17/2014] [Indexed: 11/13/2022]
Abstract
Changes in biotic and abiotic factors can be reflected in the complex impedance spectrum of the microelectrodes chronically implanted into the neural tissue. The recording surface of the tungsten electrode in vivo undergoes abiotic changes due to recording site corrosion and insulation delamination as well as biotic changes due to tissue encapsulation as a result of the foreign body immune response. We reported earlier that large changes in electrode impedance measured at 1 kHz were correlated with poor electrode functional performance, quantified through electrophysiological recordings during the chronic lifetime of the electrode. There is a need to identity the factors that contribute to the chronic impedance variation. In this work, we use numerical simulation and regression to equivalent circuit models to evaluate both the abiotic and biotic contributions to the impedance response over chronic implant duration. COMSOL® simulation of abiotic electrode morphology changes provide a possible explanation for the decrease in the electrode impedance at long implant duration while biotic changes play an important role in the large increase in impedance observed initially.
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Affiliation(s)
- Viswanath Sankar
- Electrical and Computer Engineering Department, University of Florida Gainesville, FL, USA
| | - Erin Patrick
- Electrical and Computer Engineering Department, University of Florida Gainesville, FL, USA
| | - Robert Dieme
- Electrical and Computer Engineering Department, University of Florida Gainesville, FL, USA
| | - Justin C Sanchez
- Biomedical Engineering Department, University of Miami Coral Gables, FL, USA
| | - Abhishek Prasad
- Biomedical Engineering Department, University of Miami Coral Gables, FL, USA
| | - Toshikazu Nishida
- Electrical and Computer Engineering Department, University of Florida Gainesville, FL, USA
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27
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Denoising and compression of intracortical signals with a modified MDL criterion. Med Biol Eng Comput 2014; 52:429-38. [DOI: 10.1007/s11517-014-1146-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2013] [Accepted: 03/03/2014] [Indexed: 10/25/2022]
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28
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A configurable realtime DWT-based neural data compression and communication VLSI system for wireless implants. J Neurosci Methods 2014; 227:140-50. [PMID: 24613794 DOI: 10.1016/j.jneumeth.2014.02.009] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2013] [Revised: 02/12/2014] [Accepted: 02/13/2014] [Indexed: 11/20/2022]
Abstract
This paper presents the design of a complete multi-channel neural recording compression and communication system for wireless implants that addresses the challenging simultaneous requirements for low power, high bandwidth and error-free communication. The compression engine implements discrete wavelet transform (DWT) and run length encoding schemes and offers a practical data compression solution that faithfully preserves neural information. The communication engine encodes data and commands separately into custom-designed packet structures utilizing a protocol capable of error handling. VLSI hardware implementation of these functions, within the design constraints of a 32-channel neural compression implant, is presented. Designed in 0.13μm CMOS, the core of the neural compression and communication chip occupies only 1.21mm(2) and consumes 800μW of power (25μW per channel at 26KS/s) demonstrating an effective solution for intra-cortical neural interfaces.
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Schwarz DA, Lebedev MA, Hanson TL, Dimitrov DF, Lehew G, Meloy J, Rajangam S, Subramanian V, Ifft PJ, Li Z, Ramakrishnan A, Tate A, Zhuang KZ, Nicolelis MAL. Chronic, wireless recordings of large-scale brain activity in freely moving rhesus monkeys. Nat Methods 2014; 11:670-6. [PMID: 24776634 PMCID: PMC4161037 DOI: 10.1038/nmeth.2936] [Citation(s) in RCA: 189] [Impact Index Per Article: 18.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2013] [Accepted: 03/12/2014] [Indexed: 11/23/2022]
Abstract
Advances in techniques for recording large-scale brain activity contribute to both the elucidation of neurophysiological principles and the development of brain-machine interfaces (BMIs). Here we describe a neurophysiological paradigm for performing tethered and wireless large-scale recordings based on movable volumetric three-dimensional (3D) multielectrode implants. This approach allowed us to isolate up to 1,800 units per animal and simultaneously record the extracellular activity of close to 500 cortical neurons, distributed across multiple cortical areas, in freely behaving rhesus monkeys. The method is expandable, in principle, to thousands of simultaneously recorded channels. It also allows increased recording longevity (5 consecutive years), and recording of a broad range of behaviors, e.g. social interactions, and BMI paradigms in freely moving primates. We propose that wireless large-scale recordings could have a profound impact on basic primate neurophysiology research, while providing a framework for the development and testing of clinically relevant neuroprostheses.
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Affiliation(s)
- David A Schwarz
- 1] Department of Neurobiology, Duke University, Durham, North Carolina, USA. [2] Center for Neuroengineering, Duke University, Durham, North Carolina, USA
| | - Mikhail A Lebedev
- 1] Department of Neurobiology, Duke University, Durham, North Carolina, USA. [2] Center for Neuroengineering, Duke University, Durham, North Carolina, USA
| | - Timothy L Hanson
- 1] Department of Neurobiology, Duke University, Durham, North Carolina, USA. [2] Center for Neuroengineering, Duke University, Durham, North Carolina, USA
| | | | - Gary Lehew
- 1] Department of Neurobiology, Duke University, Durham, North Carolina, USA. [2] Center for Neuroengineering, Duke University, Durham, North Carolina, USA
| | - Jim Meloy
- 1] Department of Neurobiology, Duke University, Durham, North Carolina, USA. [2] Center for Neuroengineering, Duke University, Durham, North Carolina, USA
| | - Sankaranarayani Rajangam
- 1] Department of Neurobiology, Duke University, Durham, North Carolina, USA. [2] Center for Neuroengineering, Duke University, Durham, North Carolina, USA
| | - Vivek Subramanian
- 1] Center for Neuroengineering, Duke University, Durham, North Carolina, USA. [2] Department of Biomedical Engineering, Duke University, Durham, North Carolina, USA
| | - Peter J Ifft
- 1] Center for Neuroengineering, Duke University, Durham, North Carolina, USA. [2] Department of Biomedical Engineering, Duke University, Durham, North Carolina, USA
| | - Zheng Li
- 1] Department of Neurobiology, Duke University, Durham, North Carolina, USA. [2] Center for Neuroengineering, Duke University, Durham, North Carolina, USA
| | - Arjun Ramakrishnan
- 1] Department of Neurobiology, Duke University, Durham, North Carolina, USA. [2] Center for Neuroengineering, Duke University, Durham, North Carolina, USA
| | - Andrew Tate
- 1] Department of Neurobiology, Duke University, Durham, North Carolina, USA. [2] Center for Neuroengineering, Duke University, Durham, North Carolina, USA
| | - Katie Z Zhuang
- 1] Center for Neuroengineering, Duke University, Durham, North Carolina, USA. [2] Department of Biomedical Engineering, Duke University, Durham, North Carolina, USA
| | - Miguel A L Nicolelis
- 1] Department of Neurobiology, Duke University, Durham, North Carolina, USA. [2] Center for Neuroengineering, Duke University, Durham, North Carolina, USA. [3] Department of Biomedical Engineering, Duke University, Durham, North Carolina, USA. [4] Department of Psychology and Neuroscience, Duke University, Durham, North Carolina, USA. [5] Edmond and Lily Safra International Institute of Neuroscience of Natal, Natal, Brazil
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Hosseini-Nejad H, Jannesari A, Sodagar AM. Data compression in brain-machine/computer interfaces based on the Walsh-Hadamard transform. IEEE TRANSACTIONS ON BIOMEDICAL CIRCUITS AND SYSTEMS 2014; 8:129-137. [PMID: 24681926 DOI: 10.1109/tbcas.2013.2258669] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/03/2023]
Abstract
This paper reports on the application of the Walsh-Hadamard transform (WHT) for data compression in brain-machine/brain-computer interfaces. Using the proposed technique, the amount of the neural data transmitted off the implant is compressed by a factor of at least 63 at the expense of as low as 4.66% RMS error between the signal reconstructed on the external host and the original neural signal on the implant side. Based on the proposed idea, a 128-channel WHT processor was designed in a 0.18- μm CMOS process occupying 1.64 mm(2) of silicon area. The circuit consumes 81 μW (0.63 μW per channel) from a 1.8-V power supply at 250 kHz. A prototype of the proposed processor was implemented and successfully tested using prerecorded neural signals.
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Di Pascoli S, Puntin D, Pinciaroli A, Balaban E, Pompeiano M. Design and implementation of a wireless in-ovo EEG/EMG recorder. IEEE TRANSACTIONS ON BIOMEDICAL CIRCUITS AND SYSTEMS 2013; 7:832-840. [PMID: 24473547 DOI: 10.1109/tbcas.2013.2251343] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/03/2023]
Abstract
The developmental origins of sleep and brain activity rhythms in higher vertebrate animals (birds and mammals) are currently unknown. In order to create an experimental system in which these could be better elucidated, we designed, built and tested a system for recording EEG and EMG signals in-ovo from chicken embryos incubated for 16-21 days. This system can remain attached to the individual subject through the process of hatching and continue to be worn post-natally. Electrode wires surgically implanted on the head of the embryo are connected to a battery-operated ultraportable transmitter which can either be attached to the eggshell or worn on the back. The transmitter processes up to 6 channels of data with a maximum sampling frequency of 500 Hz and a resolution of 12 bits. The radio link uses a carrier frequency of 4 MHz, and has a maximum transfer rate of 500 kbit/s; receiving antennas compatible with both in-egg recordings and post-natal recordings from freely-moving birds were produced. A receiver connected with one USB port of a PC transmits the data for digital storage. This system is based on discrete, off-the-shelf components, can provide a few days of continuous operation with a single lithium coin battery, and has a noise floor level of 0.35 μV. The transmitter dimensions are 16 × 13 × 1.5 mm and the weight without the battery is 0.7 g. The microprocessor allows flexible operation modes not usually made available in other small multichannel acquisition systems implemented by means of ad hoc mixed signal chips.
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Marblestone AH, Zamft BM, Maguire YG, Shapiro MG, Cybulski TR, Glaser JI, Amodei D, Stranges PB, Kalhor R, Dalrymple DA, Seo D, Alon E, Maharbiz MM, Carmena JM, Rabaey JM, Boyden ES, Church GM, Kording KP. Physical principles for scalable neural recording. Front Comput Neurosci 2013; 7:137. [PMID: 24187539 PMCID: PMC3807567 DOI: 10.3389/fncom.2013.00137] [Citation(s) in RCA: 134] [Impact Index Per Article: 12.2] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2013] [Accepted: 09/23/2013] [Indexed: 12/20/2022] Open
Abstract
Simultaneously measuring the activities of all neurons in a mammalian brain at millisecond resolution is a challenge beyond the limits of existing techniques in neuroscience. Entirely new approaches may be required, motivating an analysis of the fundamental physical constraints on the problem. We outline the physical principles governing brain activity mapping using optical, electrical, magnetic resonance, and molecular modalities of neural recording. Focusing on the mouse brain, we analyze the scalability of each method, concentrating on the limitations imposed by spatiotemporal resolution, energy dissipation, and volume displacement. Based on this analysis, all existing approaches require orders of magnitude improvement in key parameters. Electrical recording is limited by the low multiplexing capacity of electrodes and their lack of intrinsic spatial resolution, optical methods are constrained by the scattering of visible light in brain tissue, magnetic resonance is hindered by the diffusion and relaxation timescales of water protons, and the implementation of molecular recording is complicated by the stochastic kinetics of enzymes. Understanding the physical limits of brain activity mapping may provide insight into opportunities for novel solutions. For example, unconventional methods for delivering electrodes may enable unprecedented numbers of recording sites, embedded optical devices could allow optical detectors to be placed within a few scattering lengths of the measured neurons, and new classes of molecularly engineered sensors might obviate cumbersome hardware architectures. We also study the physics of powering and communicating with microscale devices embedded in brain tissue and find that, while radio-frequency electromagnetic data transmission suffers from a severe power-bandwidth tradeoff, communication via infrared light or ultrasound may allow high data rates due to the possibility of spatial multiplexing. The use of embedded local recording and wireless data transmission would only be viable, however, given major improvements to the power efficiency of microelectronic devices.
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Affiliation(s)
- Adam H. Marblestone
- Biophysics Program, Harvard UniversityBoston, MA, USA
- Wyss Institute for Biologically Inspired Engineering at Harvard UniversityBoston, MA, USA
| | | | - Yael G. Maguire
- Department of Genetics, Harvard Medical SchoolBoston, MA, USA
- Plum Labs LLCCambridge, MA, USA
| | - Mikhail G. Shapiro
- Division of Chemistry and Chemical Engineering, California Institute of TechnologyPasadena, CA, USA
| | | | - Joshua I. Glaser
- Interdepartmental Neuroscience Program, Northwestern UniversityChicago, IL, USA
| | - Dario Amodei
- Department of Radiology, Stanford UniversityPalo Alto, CA, USA
| | | | - Reza Kalhor
- Department of Genetics, Harvard Medical SchoolBoston, MA, USA
| | - David A. Dalrymple
- Biophysics Program, Harvard UniversityBoston, MA, USA
- NemaloadSan Francisco, CA, USA
- Media Laboratory, Massachusetts Institute of TechnologyCambridge, MA, USA
| | - Dongjin Seo
- Department of Electrical Engineering and Computer Sciences, University of California at BerkeleyBerkeley, CA, USA
| | - Elad Alon
- Department of Electrical Engineering and Computer Sciences, University of California at BerkeleyBerkeley, CA, USA
| | - Michel M. Maharbiz
- Department of Electrical Engineering and Computer Sciences, University of California at BerkeleyBerkeley, CA, USA
| | - Jose M. Carmena
- Department of Electrical Engineering and Computer Sciences, University of California at BerkeleyBerkeley, CA, USA
- Helen Wills Neuroscience Institute, University of California at BerkeleyBerkeley, CA, USA
| | - Jan M. Rabaey
- Department of Electrical Engineering and Computer Sciences, University of California at BerkeleyBerkeley, CA, USA
| | - Edward S. Boyden
- Media Laboratory, Massachusetts Institute of TechnologyCambridge, MA, USA
- Departments of Brain and Cognitive Sciences and Biological Engineering, Massachusetts Institute of TechnologyCambridge, MA, USA
| | - George M. Church
- Biophysics Program, Harvard UniversityBoston, MA, USA
- Wyss Institute for Biologically Inspired Engineering at Harvard UniversityBoston, MA, USA
- Department of Genetics, Harvard Medical SchoolBoston, MA, USA
| | - Konrad P. Kording
- Departments of Physical Medicine and Rehabilitation and of Physiology, Northwestern University Feinberg School of MedicineChicago, IL, USA
- Sensory Motor Performance Program, The Rehabilitation Institute of ChicagoChicago, IL, USA
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Borton D, Bonizzato M, Beauparlant J, DiGiovanna J, Moraud EM, Wenger N, Musienko P, Minev IR, Lacour SP, Millán JDR, Micera S, Courtine G. Corticospinal neuroprostheses to restore locomotion after spinal cord injury. Neurosci Res 2013; 78:21-9. [PMID: 24135130 DOI: 10.1016/j.neures.2013.10.001] [Citation(s) in RCA: 41] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2013] [Revised: 09/17/2013] [Accepted: 09/26/2013] [Indexed: 01/20/2023]
Abstract
In this conceptual review, we highlight our strategy for, and progress in the development of corticospinal neuroprostheses for restoring locomotor functions and promoting neural repair after thoracic spinal cord injury in experimental animal models. We specifically focus on recent developments in recording and stimulating neural interfaces, decoding algorithms, extraction of real-time feedback information, and closed-loop control systems. Each of these complex neurotechnologies plays a significant role for the design of corticospinal neuroprostheses. Even more challenging is the coordinated integration of such multifaceted technologies into effective and practical neuroprosthetic systems to improve movement execution, and augment neural plasticity after injury. In this review we address our progress in rodent animal models to explore the viability of a technology-intensive strategy for recovery and repair of the damaged nervous system. The technical, practical, and regulatory hurdles that lie ahead along the path toward clinical applications are enormous - and their resolution is uncertain at this stage. However, it is imperative that the discoveries and technological developments being made across the field of neuroprosthetics do not stay in the lab, but instead reach clinical fruition at the fastest pace possible.
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Affiliation(s)
- David Borton
- Center for Neuroprosthetics and Brain Mind Institute, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Marco Bonizzato
- Translational Neural Engineering Laboratory, Center for Neuroprosthetics and Institute of Bioengineering, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Janine Beauparlant
- Center for Neuroprosthetics and Brain Mind Institute, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Jack DiGiovanna
- Translational Neural Engineering Laboratory, Center for Neuroprosthetics and Institute of Bioengineering, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Eduardo M Moraud
- Translational Neural Engineering Laboratory, Center for Neuroprosthetics and Institute of Bioengineering, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland; Automatic Control Laboratory, Swiss Federal Institute of Technology (ETH), Zurich, Switzerland
| | - Nikolaus Wenger
- Center for Neuroprosthetics and Brain Mind Institute, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Pavel Musienko
- Center for Neuroprosthetics and Brain Mind Institute, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Ivan R Minev
- Laboratory for Soft Bioelectronic Interfaces, Center for Neuroprosthetics, IMT/IBI, EPFL, Switzerland
| | - Stéphanie P Lacour
- Laboratory for Soft Bioelectronic Interfaces, Center for Neuroprosthetics, IMT/IBI, EPFL, Switzerland
| | - José del R Millán
- Laboratory for Non-Invasive Brain-Machine Interface, Center for Neuroprosthetics and Institute of Bioengineering, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Silvestro Micera
- Translational Neural Engineering Laboratory, Center for Neuroprosthetics and Institute of Bioengineering, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland; The BioRobotics Institute, Scuola Superiore Sant'Anna, Pisa, Italy
| | - Grégoire Courtine
- Center for Neuroprosthetics and Brain Mind Institute, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland.
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Abstract
Development of neural prostheses over the past few decades has produced a number of clinically relevant brain-machine interfaces (BMIs), such as the cochlear prostheses and deep brain stimulators. Current research pursues the restoration of communication or motor function to individuals with neurological disorders. Efforts in the field, such as the BrainGate trials, have already demonstrated that such interfaces can enable humans to effectively control external devices with neural signals. However, a number of significant issues regarding BMI performance, device capabilities, and surgery must be resolved before clinical use of BMI technology can become widespread. This chapter reviews challenges to clinical translation and discusses potential solutions that have been reported in recent literature, with focuses on hardware reliability, state-of-the-art decoding algorithms, and surgical considerations during implantation.
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35
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Yin M, Borton DA, Aceros J, Patterson WR, Nurmikko AV. A 100-channel hermetically sealed implantable device for chronic wireless neurosensing applications. IEEE TRANSACTIONS ON BIOMEDICAL CIRCUITS AND SYSTEMS 2013; 7:115-28. [PMID: 23853294 PMCID: PMC3904295 DOI: 10.1109/tbcas.2013.2255874] [Citation(s) in RCA: 34] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/09/2023]
Abstract
A 100-channel fully implantable wireless broadband neural recording system was developed. It features 100 parallel broadband (0.1 Hz-7.8 kHz) neural recording channels, a medical grade 200 mAh Li-ion battery recharged inductively at 150 kHz , and data telemetry using 3.2 GHz to 3.8 GHz FSK modulated wireless link for 48 Mbps Manchester encoded data. All active electronics are hermetically sealed in a titanium enclosure with a sapphire window for electromagnetic transparency. A custom, high-density configuration of 100 individual hermetic feedthrough pins enable connection to an intracortical neural recording microelectrode array. A 100 MHz bandwidth custom receiver was built to remotely receive the FSK signal and achieved -77.7 dBm sensitivity with 10(-8) BER at 48 Mbps data rate. ESD testing on all the electronic inputs and outputs has proven that the implantable device satisfies the HBM Class-1B ESD Standard. In addition, the evaluation of the worst-case charge density delivered to the tissue from each I/O pin verifies the patient safety of the device in the event of failure. Finally, the functionality and reliability of the complete device has been tested on-bench and further validated chronically in ongoing freely moving swine and monkey animal trials for more than one year to date.
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Affiliation(s)
- Ming Yin
- School of Engineering, Brown University, Providence, RI 02912, USA.
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Borton DA, Yin M, Aceros J, Nurmikko A. An implantable wireless neural interface for recording cortical circuit dynamics in moving primates. J Neural Eng 2013; 10:026010. [PMID: 23428937 PMCID: PMC3638022 DOI: 10.1088/1741-2560/10/2/026010] [Citation(s) in RCA: 226] [Impact Index Per Article: 20.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
OBJECTIVE Neural interface technology suitable for clinical translation has the potential to significantly impact the lives of amputees, spinal cord injury victims and those living with severe neuromotor disease. Such systems must be chronically safe, durable and effective. APPROACH We have designed and implemented a neural interface microsystem, housed in a compact, subcutaneous and hermetically sealed titanium enclosure. The implanted device interfaces the brain with a 510k-approved, 100-element silicon-based microelectrode array via a custom hermetic feedthrough design. Full spectrum neural signals were amplified (0.1 Hz to 7.8 kHz, 200× gain) and multiplexed by a custom application specific integrated circuit, digitized and then packaged for transmission. The neural data (24 Mbps) were transmitted by a wireless data link carried on a frequency-shift-key-modulated signal at 3.2 and 3.8 GHz to a receiver 1 m away by design as a point-to-point communication link for human clinical use. The system was powered by an embedded medical grade rechargeable Li-ion battery for 7 h continuous operation between recharge via an inductive transcutaneous wireless power link at 2 MHz. MAIN RESULTS Device verification and early validation were performed in both swine and non-human primate freely-moving animal models and showed that the wireless implant was electrically stable, effective in capturing and delivering broadband neural data, and safe for over one year of testing. In addition, we have used the multichannel data from these mobile animal models to demonstrate the ability to decode neural population dynamics associated with motor activity. SIGNIFICANCE We have developed an implanted wireless broadband neural recording device evaluated in non-human primate and swine. The use of this new implantable neural interface technology can provide insight into how to advance human neuroprostheses beyond the present early clinical trials. Further, such tools enable mobile patient use, have the potential for wider diagnosis of neurological conditions and will advance brain research.
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Affiliation(s)
- David A Borton
- School of Engineering, Brown University, Providence, RI 02912, USA.
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Sawan M, Salam MT, Le Lan J, Kassab A, Gelinas S, Vannasing P, Lesage F, Lassonde M, Nguyen DK. Wireless recording systems: from noninvasive EEG-NIRS to invasive EEG devices. IEEE TRANSACTIONS ON BIOMEDICAL CIRCUITS AND SYSTEMS 2013; 7:186-95. [PMID: 23853301 DOI: 10.1109/tbcas.2013.2255595] [Citation(s) in RCA: 21] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/25/2023]
Abstract
In this paper, we present the design and implementation of a wireless wearable electronic system dedicated to remote data recording for brain monitoring. The reported wireless recording system is used for a) simultaneous near-infrared spectrometry (NIRS) and scalp electro-encephalography (EEG) for noninvasive monitoring and b) intracerebral EEG (icEEG) for invasive monitoring. Bluetooth and dual radio links were introduced for these recordings. The Bluetooth-based device was embedded in a noninvasive multichannel EEG-NIRS system for easy portability and long-term monitoring. On the other hand, the 32-channel implantable recording device offers 24-bit resolution, tunable features, and a sampling frequency up to 2 kHz per channel. The analog front-end preamplifier presents low input-referred noise of 5 μ VRMS and a signal-to-noise ratio of 112 dB. The communication link is implemented using a dual-band radio frequency transceiver offering a half-duplex 800 kb/s data rate, 16.5 mW power consumption and less than 10(-10) post-correction Bit-Error Rate (BER). The designed system can be accessed and controlled by a computer with a user-friendly graphical interface. The proposed wireless implantable recording device was tested in vitro using real icEEG signals from two patients with refractory epilepsy. The wirelessly recorded signals were compared to the original signals recorded using wired-connection, and measured normalized root-mean square deviation was under 2%.
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Affiliation(s)
- Mohamad Sawan
- Polystim Neurotechnologies Laboratory, Electrical Engineering Department, Polytechnique Montréal, QC H3T1J4, Canada.
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Müller J, Bakkum DJ, Hierlemann A. Sub-millisecond closed-loop feedback stimulation between arbitrary sets of individual neurons. Front Neural Circuits 2013; 6:121. [PMID: 23335887 PMCID: PMC3541546 DOI: 10.3389/fncir.2012.00121] [Citation(s) in RCA: 35] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2012] [Accepted: 12/22/2012] [Indexed: 11/20/2022] Open
Abstract
We present a system to artificially correlate the spike timing between sets of arbitrary neurons that were interfaced to a complementary metal–oxide–semiconductor (CMOS) high-density microelectrode array (MEA). The system features a novel reprogrammable and flexible event engine unit to detect arbitrary spatio-temporal patterns of recorded action potentials and is capable of delivering sub-millisecond closed-loop feedback of electrical stimulation upon trigger events in real-time. The relative timing between action potentials of individual neurons as well as the temporal pattern among multiple neurons, or neuronal assemblies, is considered an important factor governing memory and learning in the brain. Artificially changing timings between arbitrary sets of spiking neurons with our system could provide a “knob” to tune information processing in the network.
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Affiliation(s)
- Jan Müller
- Bio Engineering Laboratory, ETH Zürich Basel, Switzerland
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Schwerdt HN, Xu W, Shekhar S, Abbaspour-Tamijani A, Towe BC, Miranda FA, Chae J. A Fully-Passive Wireless Microsystem for Recording of Neuropotentials using RF Backscattering Methods. JOURNAL OF MICROELECTROMECHANICAL SYSTEMS : A JOINT IEEE AND ASME PUBLICATION ON MICROSTRUCTURES, MICROACTUATORS, MICROSENSORS, AND MICROSYSTEMS 2011; 20:1119-1130. [PMID: 22267898 PMCID: PMC3259707 DOI: 10.1109/jmems.2011.2162487] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/03/2023]
Abstract
The ability to safely monitor neuropotentials is essential in establishing methods to study the brain. Current research focuses on the wireless telemetry aspect of implantable sensors in order to make these devices ubiquitous and safe. Chronic implants necessitate superior reliability and durability of the integrated electronics. The power consumption of implanted electronics must also be limited to within several milliwatts to microwatts to minimize heat trauma in the human body. In order to address these severe requirements, we developed an entirely passive and wireless microsystem for recording neuropotentials. An external interrogator supplies a fundamental microwave carrier to the microsystem. The microsystem comprises varactors that perform nonlinear mixing of neuropotential and fundamental carrier signals. The varactors generate third-order mixing products that are wirelessly backscattered to the external interrogator where the original neuropotential signals are recovered. Performance of the neuro-recording microsystem was demonstrated by wireless recording of emulated and in vivo neuropotentials. The obtained results were wireless recovery of neuropotentials as low as approximately 500 microvolts peak-to-peak (μV(pp)) with a bandwidth of 10 Hz to 3 kHz (for emulated signals) and with 128 epoch signal averaging of repetitive signals (for in vivo signals).
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Affiliation(s)
- Helen N Schwerdt
- School of Electrical, Computer, and Energy Engineering at Arizona State University, Tempe, AZ 85287 USA
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40
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Kiani M, Jow UM, Ghovanloo M. Design and Optimization of a 3-Coil Inductive Link for Efficient Wireless Power Transmission. IEEE TRANSACTIONS ON BIOMEDICAL CIRCUITS AND SYSTEMS 2011; 99:1. [PMID: 21922034 PMCID: PMC3171453 DOI: 10.1109/tbcas.2011.2158431] [Citation(s) in RCA: 61] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/20/2023]
Abstract
Inductive power transmission is widely used to energize implantable microelectronic devices (IMDs), recharge batteries, and energy harvesters. Power transfer efficiency (PTE) and power delivered to the load (PDL) are two key parameters in wireless links, which affect the energy source specifications, heat dissipation, power transmission range, and interference with other devices. To improve the PTE, a 4-coil inductive link has been recently proposed. Through a comprehensive circuit based analysis that can guide a design and optimization scheme, we have shown that despite achieving high PTE at larger coil separations, the 4-coil inductive links fail to achieve a high PDL. Instead, we have proposed a 3-coil inductive power transfer link with comparable PTE over its 4-coil counterpart at large coupling distances, which can also achieve high PDL. We have also devised an iterative design methodology that provides the optimal coil geometries in a 3-coil inductive power transfer link. Design examples of 2-, 3-, and 4-coil inductive links have been presented, and optimized for 13.56 MHz carrier frequency and 12 cm coupling distance, showing PTEs of 15%, 37%, and 35%, respectively. At this distance, the PDL of the proposed 3-coil inductive link is 1.5 and 59 times higher than its equivalent 2- and 4-coil links, respectively. For short coupling distances, however, 2-coil links remain the optimal choice when a high PDL is required, while 4-coil links are preferred when the driver has large output resistance or small power is needed. These results have been verified through simulations and measurements.
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Affiliation(s)
| | | | - Maysam Ghovanloo
- Corresponding author: (phone: 404-385-7048; fax: 404-894-4701, )
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41
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Zanos S, Richardson AG, Shupe L, Miles FP, Fetz EE. The Neurochip-2: an autonomous head-fixed computer for recording and stimulating in freely behaving monkeys. IEEE Trans Neural Syst Rehabil Eng 2011; 19:427-35. [PMID: 21632309 DOI: 10.1109/tnsre.2011.2158007] [Citation(s) in RCA: 87] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
The Neurochip-2 is a second generation, battery-powered device for neural recording and stimulating that is small enough to be carried in a chamber on a monkey's head. It has three recording channels, with user-adjustable gains, filters, and sampling rates, that can be optimized for recording single unit activity, local field potentials, electrocorticography, electromyography, arm acceleration, etc. Recorded data are stored on a removable, flash memory card. The Neurochip-2 also has three separate stimulation channels. Two "programmable-system-on-chips" (PSoCs) control the data acquisition and stimulus output. The PSoCs permit flexible real-time processing of the recorded data, such as digital filtering and time-amplitude window discrimination. The PSoCs can be programmed to deliver stimulation contingent on neural events or deliver preprogrammed stimuli. Access pins to the microcontroller are also available to connect external devices, such as accelerometers. The Neurochip-2 can record and stimulate autonomously for up to several days in freely behaving monkeys, enabling a wide range of novel neurophysiological and neuroengineering experiments.
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Affiliation(s)
- Stavros Zanos
- Department of Physiology and Biophysics, University of Washington, Seattle, WA 98195, USA
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42
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Vidaurre C, Sannelli C, Müller KR, Blankertz B. Machine-Learning-Based Coadaptive Calibration for Brain-Computer Interfaces. Neural Comput 2011; 23:791-816. [PMID: 21162666 DOI: 10.1162/neco_a_00089] [Citation(s) in RCA: 153] [Impact Index Per Article: 11.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
Abstract
Brain-computer interfaces (BCIs) allow users to control a computer application by brain activity as acquired (e.g., by EEG). In our classic machine learning approach to BCIs, the participants undertake a calibration measurement without feedback to acquire data to train the BCI system. After the training, the user can control a BCI and improve the operation through some type of feedback. However, not all BCI users are able to perform sufficiently well during feedback operation. In fact, a nonnegligible portion of participants (estimated 15%–30%) cannot control the system (a BCI illiteracy problem, generic to all motor-imagery-based BCIs). We hypothesize that one main difficulty for a BCI user is the transition from offline calibration to online feedback. In this work, we investigate adaptive machine learning methods to eliminate offline calibration and analyze the performance of 11 volunteers in a BCI based on the modulation of sensorimotor rhythms. We present an adaptation scheme that individually guides the user. It starts with a subject-independent classifier that evolves to a subject-optimized state-of-the-art classifier within one session while the user interacts continuously. These initial runs use supervised techniques for robust coadaptive learning of user and machine. Subsequent runs use unsupervised adaptation to track the features’ drift during the session and provide an unbiased measure of BCI performance. Using this approach, without any offline calibration, six users, including one novice, obtained good performance after 3 to 6 minutes of adaptation. More important, this novel guided learning also allows participants with BCI illiteracy to gain significant control with the BCI in less than 60 minutes. In addition, one volunteer without sensorimotor idle rhythm peak at the beginning of the BCI experiment developed it during the course of the session and used voluntary modulation of its amplitude to control the feedback application.
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Affiliation(s)
- Carmen Vidaurre
- Machine Learning Department, Berlin Institute of Technology, Berlin 10587, Germany
| | - Claudia Sannelli
- Machine Learning Department, Berlin Institute of Technology, Berlin 10587, Germany
| | - Klaus-Robert Müller
- Machine Learning Department, Berlin Institute of Technology, Berlin 10587, Germany, and Bernstein Focus: Neurotechnology, Berlin 10115, Germany
| | - Benjamin Blankertz
- Machine Learning Department, Berlin Institute of Technology, Berlin 10587, Germany; Bernstein Focus: Neurotechnology, Berlin 10115, Germany; and Fraunhofer FIRST (IDA), Berlin 12489, Germany
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Low-power, high data rate transceiver system for implantable prostheses. Int J Telemed Appl 2011; 2010:563903. [PMID: 21317982 PMCID: PMC3026970 DOI: 10.1155/2010/563903] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2010] [Revised: 11/11/2010] [Accepted: 11/17/2010] [Indexed: 11/26/2022] Open
Abstract
Wireless telemetry is crucial for long-term implantable neural recording systems. RF-encoded neurological signals often require high data-rates to transmit information from multiple electrodes with a sufficient sampling frequency and resolution. In this work, we quantify the effects of interferers and tissue attenuation on a wireless link for optimal design of future systems. The wireless link consists of an external receiver capable of demodulating FSK/OOK transmission at speeds up to 8 Mbps, with <1e-5 bit-error rate (BER) without error correction, and a fully implanted transmitter consuming about 1.05 mW. The external receiver is tested with the transmitter in vivo to show demodulation efficacy of the transcutaneous link at high data-rates. Transmitter/Receiver link BER is quantified in typical and controlled RF environments for ex vivo and in vivo performance.
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Chah E, Hok V, Della-Chiesa A, Miller JJH, O'Mara SM, Reilly RB. Automated spike sorting algorithm based on Laplacian eigenmaps and k-means clustering. J Neural Eng 2011; 8:016006. [PMID: 21248378 DOI: 10.1088/1741-2560/8/1/016006] [Citation(s) in RCA: 34] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
This study presents a new automatic spike sorting method based on feature extraction by Laplacian eigenmaps combined with k-means clustering. The performance of the proposed method was compared against previously reported algorithms such as principal component analysis (PCA) and amplitude-based feature extraction. Two types of classifier (namely k-means and classification expectation-maximization) were incorporated within the spike sorting algorithms, in order to find a suitable classifier for the feature sets. Simulated data sets and in-vivo tetrode multichannel recordings were employed to assess the performance of the spike sorting algorithms. The results show that the proposed algorithm yields significantly improved performance with mean sorting accuracy of 73% and sorting error of 10% compared to PCA which combined with k-means had a sorting accuracy of 58% and sorting error of 10%.A correction was made to this article on 22 February 2011. The spacing of the title was amended on the abstract page. No changes were made to the article PDF and the print version was unaffected.
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Affiliation(s)
- E Chah
- Trinity Centre for Bioengineering, Trinity College Dublin, Ireland. Trinity College Institute for Neuroscience, Trinity College Dublin, Ireland.
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45
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Lee SB, Lee HM, Kiani M, Jow UM, Ghovanloo M. An Inductively Powered Scalable 32-Channel Wireless Neural Recording System-on-a-Chip for Neuroscience Applications. IEEE TRANSACTIONS ON BIOMEDICAL CIRCUITS AND SYSTEMS 2010; 4:360-71. [PMID: 23850753 PMCID: PMC4104168 DOI: 10.1109/tbcas.2010.2078814] [Citation(s) in RCA: 29] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/21/2023]
Abstract
We present an inductively powered 32-channel wireless integrated neural recording (WINeR) system-on-a-chip (SoC) to be ultimately used for one or more small freely behaving animals. The inductive powering is intended to relieve the animals from carrying bulky batteries used in other wireless systems, and enables long recording sessions. The WINeR system uses time-division multiplexing along with a novel power scheduling method that reduces the current in unused low-noise amplifiers (LNAs) to cut the total SoC power consumption. In addition, an on-chip high-efficiency active rectifier with optimized coils help improve the overall system power efficiency, which is controlled in a closed loop to supply stable power to the WINeR regardless of the coil displacements. The WINeR SoC has been implemented in a 0.5-μ m standard complementary metal-oxide semiconductor process, measuring 4.9×3.3 mm(2) and consuming 5.85 mW at ±1.5 V when 12 out of 32 LNAs are active at any time by power scheduling. Measured input-referred noise for the entire system, including the receiver located at 1.2 m, is 4.95 μVrms in the 1 Hz~10 kHz range when the system is inductively powered with 7-cm separation between aligned coils.
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46
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Rothschild RM. Neuroengineering tools/applications for bidirectional interfaces, brain-computer interfaces, and neuroprosthetic implants - a review of recent progress. FRONTIERS IN NEUROENGINEERING 2010; 3:112. [PMID: 21060801 PMCID: PMC2972680 DOI: 10.3389/fneng.2010.00112] [Citation(s) in RCA: 47] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/20/2010] [Accepted: 09/22/2010] [Indexed: 11/30/2022]
Abstract
The main focus of this review is to provide a holistic amalgamated overview of the most recent human in vivo techniques for implementing brain–computer interfaces (BCIs), bidirectional interfaces, and neuroprosthetics. Neuroengineering is providing new methods for tackling current difficulties; however neuroprosthetics have been studied for decades. Recent progresses are permitting the design of better systems with higher accuracies, repeatability, and system robustness. Bidirectional interfaces integrate recording and the relaying of information from and to the brain for the development of BCIs. The concepts of non-invasive and invasive recording of brain activity are introduced. This includes classical and innovative techniques like electroencephalography and near-infrared spectroscopy. Then the problem of gliosis and solutions for (semi-) permanent implant biocompatibility such as innovative implant coatings, materials, and shapes are discussed. Implant power and the transmission of their data through implanted pulse generators and wireless telemetry are taken into account. How sensation can be relayed back to the brain to increase integration of the neuroengineered systems with the body by methods such as micro-stimulation and transcranial magnetic stimulation are then addressed. The neuroprosthetic section discusses some of the various types and how they operate. Visual prosthetics are discussed and the three types, dependant on implant location, are examined. Auditory prosthetics, being cochlear or cortical, are then addressed. Replacement hand and limb prosthetics are then considered. These are followed by sections concentrating on the control of wheelchairs, computers and robotics directly from brain activity as recorded by non-invasive and invasive techniques.
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Royer S, Zemelman BV, Barbic M, Losonczy A, Buzsáki G, Magee JC. Multi-array silicon probes with integrated optical fibers: light-assisted perturbation and recording of local neural circuits in the behaving animal. Eur J Neurosci 2010; 31:2279-91. [PMID: 20529127 PMCID: PMC2954764 DOI: 10.1111/j.1460-9568.2010.07250.x] [Citation(s) in RCA: 203] [Impact Index Per Article: 14.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Recordings of large neuronal ensembles and neural stimulation of high spatial and temporal precision are important requisites for studying the real-time dynamics of neural networks. Multiple-shank silicon probes enable large-scale monitoring of individual neurons. Optical stimulation of genetically targeted neurons expressing light-sensitive channels or other fast (milliseconds) actuators offers the means for controlled perturbation of local circuits. Here we describe a method to equip the shanks of silicon probes with micron-scale light guides for allowing the simultaneous use of the two approaches. We then show illustrative examples of how these compact hybrid electrodes can be used in probing local circuits in behaving rats and mice. A key advantage of these devices is the enhanced spatial precision of stimulation that is achieved by delivering light close to the recording sites of the probe. When paired with the expression of light-sensitive actuators within genetically specified neuronal populations, these devices allow the relatively straightforward and interpretable manipulation of network activity.
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Affiliation(s)
- Sébastien Royer
- Howard Hughes Medical Institute, Janelia Farm Research Campus, Ashburn, VA 20147, USA
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48
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Royer S, Zemelman BV, Barbic M, Losonczy A, Buzsáki G, Magee JC. Multi-array silicon probes with integrated optical fibers: light-assisted perturbation and recording of local neural circuits in the behaving animal. Eur J Neurosci 2010. [PMID: 20529127 DOI: 10.1111/(issn)1460-9568] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/12/2023]
Abstract
Recordings of large neuronal ensembles and neural stimulation of high spatial and temporal precision are important requisites for studying the real-time dynamics of neural networks. Multiple-shank silicon probes enable large-scale monitoring of individual neurons. Optical stimulation of genetically targeted neurons expressing light-sensitive channels or other fast (milliseconds) actuators offers the means for controlled perturbation of local circuits. Here we describe a method to equip the shanks of silicon probes with micron-scale light guides for allowing the simultaneous use of the two approaches. We then show illustrative examples of how these compact hybrid electrodes can be used in probing local circuits in behaving rats and mice. A key advantage of these devices is the enhanced spatial precision of stimulation that is achieved by delivering light close to the recording sites of the probe. When paired with the expression of light-sensitive actuators within genetically specified neuronal populations, these devices allow the relatively straightforward and interpretable manipulation of network activity.
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Affiliation(s)
- Sébastien Royer
- Howard Hughes Medical Institute, Janelia Farm Research Campus, Ashburn, VA 20147, USA
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49
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Lee SB, Lee HM, Kiani M, Jow UM, Ghovanloo M. An Inductively Powered Scalable 32-Channel Wireless Neural Recording System-on-a-Chip for Neuroscience Applications. DIGEST OF TECHNICAL PAPERS. IEEE INTERNATIONAL SOLID-STATE CIRCUITS CONFERENCE 2010; 2010:120-121. [PMID: 21572569 DOI: 10.1109/isscc.2010.5434028] [Citation(s) in RCA: 109] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
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50
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Vaadia E, Birbaumer N. Grand challenges of brain computer interfaces in the years to come. Front Neurosci 2009; 3:151-4. [PMID: 20228862 PMCID: PMC2751651 DOI: 10.3389/neuro.01.015.2009] [Citation(s) in RCA: 30] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2009] [Indexed: 11/13/2022] Open
Affiliation(s)
- Eilon Vaadia
- Interdisciplinary Center for Neural Computation and the Edmond and Lily Safra Center for Brain Sciences, The Hebrew University Jerusalem, Israel
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