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Chen G, Dang D, Zhang C, Qin L, Yan T, Wang W, Liang W. Recent advances in neurotechnology-based biohybrid robots. SOFT MATTER 2024; 20:7993-8011. [PMID: 39328163 DOI: 10.1039/d4sm00768a] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/28/2024]
Abstract
Biohybrid robots retain the innate biological characteristics and behavioral traits of animals, making them valuable in applications such as disaster relief, exploration of unknown terrains, and medical care. This review aims to comprehensively discuss the evolution of biohybrid robots, their key technologies and applications, and the challenges they face. By analyzing studies conducted on terrestrial, aquatic, and aerial biohybrid robots, we gain a deeper understanding of how these technologies have made significant progress in simulating natural organisms, improving mechanical performance, and intelligent control. Additionally, we address challenges associated with the application of electrical stimulation technology, the precision of neural signal monitoring, and the ethical considerations for biohybrid robots. We highlight the importance of future research focusing on developing more sophisticated and biocompatible control methods while prioritizing animal welfare. We believe that exploring multimodal monitoring and stimulation technologies holds the potential to enhance the performance of biohybrid robots. These efforts are expected to pave the way for biohybrid robotics technology to introduce greater innovation and well-being to human society in the future.
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Affiliation(s)
- Guiyong Chen
- School of Mechanical Engineering, Shenyang Jianzhu University, Shenyang 110168, People's Republic of China.
- State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, People's Republic of China.
- Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110169, People's Republic of China
| | - Dan Dang
- School of Sciences, Shenyang Jianzhu University, Shenyang 110168, People's Republic of China.
| | - Chuang Zhang
- State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, People's Republic of China.
- Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110169, People's Republic of China
| | - Ling Qin
- School of Life Sciences, China Medical University, Shenyang 110122, People's Republic of China
| | - Tao Yan
- Department of Anesthesiology, National Cancer Center, National Clinical Research Center for Cancer, Cancer Hospital, Beijing 100021, People's Republic of China
- Chinese Academy of Medical Sciences, Beijing 100021, People's Republic of China
- Peking Union Medical College, Beijing 100021, People's Republic of China
| | - Wenxue Wang
- State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, People's Republic of China.
- Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110169, People's Republic of China
| | - Wenfeng Liang
- School of Mechanical Engineering, Shenyang Jianzhu University, Shenyang 110168, People's Republic of China.
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2
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Bjånes DA, Kellis S, Nickl R, Baker B, Aflalo T, Bashford L, Chivukula S, Fifer MS, Osborn LE, Christie B, Wester BA, Celnik PA, Kramer D, Pejsa K, Crone NE, Anderson WS, Pouratian N, Lee B, Liu CY, Tenore F, Rieth L, Andersen RA. Quantifying physical degradation alongside recording and stimulation performance of 980 intracortical microelectrodes chronically implanted in three humans for 956-2246 days. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.09.09.24313281. [PMID: 39314938 PMCID: PMC11419230 DOI: 10.1101/2024.09.09.24313281] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 09/25/2024]
Abstract
Motivation The clinical success of brain-machine interfaces depends on overcoming both biological and material challenges to ensure a long-term stable connection for neural recording and stimulation. Therefore, there is a need to quantify any damage that microelectrodes sustain when they are chronically implanted in the human cortex. Methods Using scanning electron microscopy (SEM), we imaged 980 microelectrodes from Neuroport arrays chronically implanted in the cortex of three people with tetraplegia for 956-2246 days. We analyzed eleven multi-electrode arrays in total: eight arrays with platinum (Pt) electrode tips and three with sputtered iridium oxide tips (SIROF); one Pt array was left in sterile packaging, serving as a control. The arrays were implanted/explanted across three different clinical sites surgeries (Caltech/UCLA, Caltech/USC and APL/Johns Hopkins) in the anterior intraparietal area, Brodmann's area 5, motor cortex, and somatosensory cortex.Human experts rated the electron micrographs of electrodes with respect to five damage metrics: the loss of metal at the electrode tip, the amount of separation between the silicon shank and tip metal, tissue adherence or bio-material to the electrode, damage to the shank insulation and silicone shaft. These metrics were compared to functional outcomes (recording quality, noise, impedance and stimulation ability). Results Despite higher levels of physical degradation, SIROF electrodes were twice as likely to record neural activity than Pt electrodes (measured by SNR), at the time of explant. Additionally, 1 kHz impedance (measured in vivo prior to explant) significantly correlated with all physical damage metrics, recording, and stimulation performance for SIROF electrodes (but not Pt), suggesting a reliable measurement of in vivo degradation.We observed a new degradation type, primarily occurring on stimulated electrodes ("pockmarked" vs "cracked") electrodes; however, tip metalization damage was not significantly higher due to stimulation or amount of charge. Physical damage was centralized to specific regions of an array often with differences between outer and inner electrodes. This is consistent with degradation due to contact with the biologic milieu, influenced by variations in initial manufactured state. From our data, we hypothesize that erosion of the silicon shank often precedes damage to the tip metal, accelerating damage to the electrode / tissue interface. Conclusions These findings link quantitative measurements, such as impedance, to the physical condition of the microelectrodes and their capacity to record and stimulate. These data could lead to improved manufacturing or novel electrode designs to improve long-term performance of BMIs making them are vitally important as multi-year clinical trials of BMIs are becoming more common.
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Affiliation(s)
- D. A. Bjånes
- Department of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, USA
| | - S. Kellis
- Department of Neurological Surgery, Keck School of Medicine of USC; Los Angeles, CA 90033, USA
| | - R. Nickl
- Department of Physical Medicine and Rehabilitation, Johns Hopkins University Applied Physics Laboratory, Laurel, MD, USA 20723
| | - B. Baker
- Electrical and Computer Engineering Univ. of Utah, Salt Lake City, UT
| | - T. Aflalo
- Department of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, USA
| | - L. Bashford
- Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, UK
| | - S. Chivukula
- Department of Neurosurgery, Kaiser Permanente Los Angeles Medical Center, Los Angeles, CA 90027
| | - M. S. Fifer
- Research and Exploratory Development Department, Johns Hopkins University Applied Physics Laboratory, Laurel, MD, USA 20723
| | - L. E. Osborn
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA 44106
| | - B. Christie
- Research and Exploratory Development Department, Johns Hopkins University Applied Physics Laboratory, Laurel, MD, USA 20723
| | - B. A. Wester
- Research and Exploratory Development Department, Johns Hopkins University Applied Physics Laboratory, Laurel, MD, USA 20723
| | | | - D. Kramer
- Department of Neurological Surgery, University of Colorado Hospital, CO, 80045, USA
| | - K. Pejsa
- Department of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, USA
| | - N. E. Crone
- Department of Neurology, Johns Hopkins University Applied Physics Laboratory, Laurel, MD, USA 20723
| | - W. S. Anderson
- Department of Neurosurgery, Johns Hopkins University School of Medicine, Laurel, MD, USA 20723
| | - N. Pouratian
- Department of Neurological Surgery, UT Southwestern Medical Center, Dallas, TX 75390, USA
| | - B. Lee
- Department of Neurological Surgery, Keck School of Medicine of USC; Los Angeles, CA 90033, USA
| | - C. Y. Liu
- USC Neurorestoration Center, Department of Neurological Surgery, Keck School of Medicine of USC; Los Angeles, CA 90033, USA
| | - F. Tenore
- Research and Exploratory Development Department, Johns Hopkins University Applied Physics Laboratory, Laurel, MD, USA 20723
| | - L. Rieth
- Mechanical and Aerospace Engineering, West Virginia University, Morgantown, WV
| | - R. A. Andersen
- Department of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, USA
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Feng J, Gao S, Hu Y, Sun G, Sheng W. Brain-Computer Interface for Patients with Spinal Cord Injury: A Bibliometric Study. World Neurosurg 2024:S1878-8750(24)01532-8. [PMID: 39245135 DOI: 10.1016/j.wneu.2024.08.163] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2024] [Revised: 08/29/2024] [Accepted: 08/30/2024] [Indexed: 09/10/2024]
Abstract
BACKGROUND Spinal cord injury (SCI) is a debilitating condition with profound implications on patients' quality of life. Recent advancements in brain-computer interface (BCI) technology have provided novel opportunities for individuals with paralysis due to SCI. Consequently, research on the application of BCI for treating SCI has received increasing attention from scholars worldwide. However, there is a lack of rigorous bibliometric studies on the evolution and trends in this field. Hence, the present study aimed to use bibliometric methods to investigate the current status and emerging trends in the field of applying BCI for treating SCI and thus identify novel therapeutic options for SCI. METHODS We conducted a comprehensive review of the relevant literature on BCI applications for treating SCI published between 2005 and 2024 by using the Web of Science Core Collection database. To facilitate visualization and quantitative analysis of the published literature, we used VOSviewer and CiteSpace software tools. These tools enabled the assessment of co-authorships, co-occurrences, citations, and co-citations in the selected literature, thereby providing an overview of the current trends and predictive insights into the field. RESULTS The literature search yielded 714 publications from the Web of Science Core Collection database. The findings indicated a significant upward trend in the number of publications, yielding a total of 24,804 citations, with an average citation rate of 34.74 per publication and an H-index of 75. Research contributions were identified from 54 countries/regions, and the United States, China, and Germany emerged as the predominant contributors. A total of 1114 research institutions contributed to the retrieved literature, with Harvard Medical School, Brown University, and Northwestern University producing the highest number of publications. The published literature was predominantly distributed across 258 academic journals, and the Journal of Neural Engineering was the most frequently utilized publication source. Hochberg, Leigh, Henderson, Jaimie, and Collinger were the prominent authors in this field. CONCLUSIONS In recent years, there has been a steep increase in research on the use of BCI for treating SCI. Existing research focuses on the application of BCI for improving rehabilitation and quality of life of patients with SCI. Interdisciplinary collaboration is the current trend in this field.
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Affiliation(s)
- Jingsheng Feng
- Department of Spinal Surgery, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, China
| | - Shutao Gao
- Department of Spinal Surgery, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, China
| | - Yukun Hu
- Department of Spinal Surgery, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, China
| | - Guangxu Sun
- Department of Spinal Surgery, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, China
| | - Weibin Sheng
- Department of Spinal Surgery, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, China.
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Li L, Zhang B, Zhao W, Sheng D, Yin L, Sheng X, Yao D. Multimodal Technologies for Closed-Loop Neural Modulation and Sensing. Adv Healthc Mater 2024; 13:e2303289. [PMID: 38640468 DOI: 10.1002/adhm.202303289] [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: 09/27/2023] [Revised: 03/11/2024] [Indexed: 04/21/2024]
Abstract
Existing methods for studying neural circuits and treating neurological disorders are typically based on physical and chemical cues to manipulate and record neural activities. These approaches often involve predefined, rigid, and unchangeable signal patterns, which cannot be adjusted in real time according to the patient's condition or neural activities. With the continuous development of neural interfaces, conducting in vivo research on adaptive and modifiable treatments for neurological diseases and neural circuits is now possible. In this review, current and potential integration of various modalities to achieve precise, closed-loop modulation, and sensing in neural systems are summarized. Advanced materials, devices, or systems that generate or detect electrical, magnetic, optical, acoustic, or chemical signals are highlighted and utilized to interact with neural cells, tissues, and networks for closed-loop interrogation. Further, the significance of developing closed-loop techniques for diagnostics and treatment of neurological disorders such as epilepsy, depression, rehabilitation of spinal cord injury patients, and exploration of brain neural circuit functionality is elaborated.
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Affiliation(s)
- Lizhu Li
- Sichuan Provincial Key Laboratory for Human Disease Gene Study and the Center for Medical Genetics, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 611731, China
| | - Bozhen Zhang
- School of Materials Science and Engineering, The Key Laboratory of Advanced Materials of Ministry of Education, State Key Laboratory of New Ceramics and Fine Processing, Laboratory of Flexible Electronics Technology, Tsinghua University, Beijing, 100084, China
| | - Wenxin Zhao
- Department of Electronic Engineering, Beijing National Research Center for Information Science and Technology, Institute for Precision Medicine, Laboratory of Flexible Electronics Technology, IDG/McGovern Institute for Brain Research, Tsinghua University, Beijing, 100084, China
| | - David Sheng
- Department of Electronic Engineering, Beijing National Research Center for Information Science and Technology, Institute for Precision Medicine, Laboratory of Flexible Electronics Technology, IDG/McGovern Institute for Brain Research, Tsinghua University, Beijing, 100084, China
| | - Lan Yin
- School of Materials Science and Engineering, The Key Laboratory of Advanced Materials of Ministry of Education, State Key Laboratory of New Ceramics and Fine Processing, Laboratory of Flexible Electronics Technology, Tsinghua University, Beijing, 100084, China
| | - Xing Sheng
- Department of Electronic Engineering, Beijing National Research Center for Information Science and Technology, Institute for Precision Medicine, Laboratory of Flexible Electronics Technology, IDG/McGovern Institute for Brain Research, Tsinghua University, Beijing, 100084, China
| | - Dezhong Yao
- Sichuan Provincial Key Laboratory for Human Disease Gene Study and the Center for Medical Genetics, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 611731, China
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Kunigk NG, Schone HR, Gontier C, Hockeimer W, Tortolani AF, Hatsopoulos NG, Downey JE, Chase SM, Boninger ML, Dekleva BD, Collinger JL. Motor somatotopy impacts imagery strategy success in human intracortical brain-computer interfaces. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.08.01.24311180. [PMID: 39132484 PMCID: PMC11312650 DOI: 10.1101/2024.08.01.24311180] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 08/13/2024]
Abstract
The notion of a somatotopically organized motor cortex, with movements of different body parts being controlled by spatially distinct areas of cortex, is well known. However, recent studies have challenged this notion and suggested a more distributed representation of movement control. This shift in perspective has significant implications, particularly when considering the implantation location of electrode arrays for intracortical brain-computer interfaces (iBCIs). We sought to evaluate whether the location of neural recordings from the precentral gyrus, and thus the underlying somatotopy, has any impact on the imagery strategies that can enable successful iBCI control. Three individuals with a spinal cord injury were enrolled in an ongoing clinical trial of an iBCI. Participants had two intracortical microelectrode arrays implanted in the arm and/or hand areas of the precentral gyrus based on presurgical functional imaging. Neural data were recorded while participants attempted to perform movements of the hand, wrist, elbow, and shoulder. We found that electrode arrays that were located more medially recorded significantly more activity during attempted proximal arm movements (elbow, shoulder) than did lateral arrays, which captured more activity related to attempted distal arm movements (hand, wrist). We also evaluated the relative contribution from the two arrays implanted in each participant to decoding accuracy during calibration of an iBCI decoder for translation and grasping tasks. For both task types, imagery strategy (e.g., reaching vs. wrist movements) had a significant impact on the relative contributions of each array to decoding. Overall, we found some evidence of broad tuning to arm and hand movements; however, there was a clear bias in the amount of information accessible about each movement type in spatially distinct areas of cortex. These results demonstrate that classical concepts of somatotopy can have real consequences for iBCI use, and highlight the importance of considering somatotopy when planning iBCI implantation.
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Affiliation(s)
- N G Kunigk
- Rehab Neural Engineering Labs, University of Pittsburgh, Pittsburgh, PA, USA
- Dept. of Bioengineering, University of Pittsburgh, Pittsburgh, PA, USA
- Center for the Neural Basis of Cognition, Pittsburgh, PA
| | - H R Schone
- Rehab Neural Engineering Labs, University of Pittsburgh, Pittsburgh, PA, USA
- Dept. of Physical Medicine and Rehabilitation, University of Pittsburgh, Pittsburgh, PA, USA
| | - C Gontier
- Rehab Neural Engineering Labs, University of Pittsburgh, Pittsburgh, PA, USA
- Dept. of Physical Medicine and Rehabilitation, University of Pittsburgh, Pittsburgh, PA, USA
| | - W Hockeimer
- Rehab Neural Engineering Labs, University of Pittsburgh, Pittsburgh, PA, USA
- Dept. of Physical Medicine and Rehabilitation, University of Pittsburgh, Pittsburgh, PA, USA
| | - A F Tortolani
- Committee on Computational Neuroscience, University of Chicago, Chicago, IL, USA
| | - N G Hatsopoulos
- Committee on Computational Neuroscience, University of Chicago, Chicago, IL, USA
- Dept. of Organismal Biology and Anatomy, University of Chicago, Chicago, IL, USA
- Neuroscience Institute, University of Chicago, Chicago, IL, USA
| | - J E Downey
- Dept. of Organismal Biology and Anatomy, University of Chicago, Chicago, IL, USA
| | - S M Chase
- Center for the Neural Basis of Cognition, Pittsburgh, PA
- Dept. of Biomedical Engineering, Carnegie Mellon University, Pittsburgh, PA, USA
- Neuroscience Institute, Carnegie Mellon University, Pittsburgh, PA, USA
| | - M L Boninger
- Rehab Neural Engineering Labs, University of Pittsburgh, Pittsburgh, PA, USA
- Dept. of Bioengineering, University of Pittsburgh, Pittsburgh, PA, USA
- Dept. of Physical Medicine and Rehabilitation, University of Pittsburgh, Pittsburgh, PA, USA
| | - B D Dekleva
- Rehab Neural Engineering Labs, University of Pittsburgh, Pittsburgh, PA, USA
- Dept. of Physical Medicine and Rehabilitation, University of Pittsburgh, Pittsburgh, PA, USA
- Center for the Neural Basis of Cognition, Pittsburgh, PA
| | - J L Collinger
- Rehab Neural Engineering Labs, University of Pittsburgh, Pittsburgh, PA, USA
- Dept. of Bioengineering, University of Pittsburgh, Pittsburgh, PA, USA
- Dept. of Physical Medicine and Rehabilitation, University of Pittsburgh, Pittsburgh, PA, USA
- Center for the Neural Basis of Cognition, Pittsburgh, PA
- Dept. of Biomedical Engineering, Carnegie Mellon University, Pittsburgh, PA, USA
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Abbott JR, Jeakle EN, Haghighi P, Usoro JO, Sturgill BS, Wu Y, Geramifard N, Radhakrishna R, Patnaik S, Nakajima S, Hess J, Mehmood Y, Devata V, Vijayakumar G, Sood A, Doan Thai TT, Dogra K, Hernandez-Reynoso AG, Pancrazio JJ, Cogan SF. Planar amorphous silicon carbide microelectrode arrays for chronic recording in rat motor cortex. Biomaterials 2024; 308:122543. [PMID: 38547834 PMCID: PMC11065583 DOI: 10.1016/j.biomaterials.2024.122543] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2023] [Revised: 03/05/2024] [Accepted: 03/19/2024] [Indexed: 04/21/2024]
Abstract
Chronic implantation of intracortical microelectrode arrays (MEAs) capable of recording from individual neurons can be used for the development of brain-machine interfaces. However, these devices show reduced recording capabilities under chronic conditions due, at least in part, to the brain's foreign body response (FBR). This creates a need for MEAs that can minimize the FBR to possibly enable long-term recording. A potential approach to reduce the FBR is the use of MEAs with reduced cross-sectional geometries. Here, we fabricated 4-shank amorphous silicon carbide (a-SiC) MEAs and implanted them into the motor cortex of seven female Sprague-Dawley rats. Each a-SiC MEA shank was 8 μm thick by 20 μm wide and had sixteen sputtered iridium oxide film (SIROF) electrodes (4 per shank). A-SiC was chosen as the fabrication base for its high chemical stability, good electrical insulation properties, and amenability to thin film fabrication. Electrochemical analysis and neural recordings were performed weekly for 4 months. MEAs were characterized pre-implantation in buffered saline and in vivo using electrochemical impedance spectroscopy and cyclic voltammetry at 50 mV/s and 50,000 mV/s. Neural recordings were analyzed for single unit activity. At the end of the study, animals were sacrificed for immunohistochemical analysis. We observed statistically significant, but small, increases in 1 and 30 kHz impedance values and 50,000 mV/s charge storage capacity over the 16-week implantation period. Slow sweep 50 mV/s CV and 1 Hz impedance did not significantly change over time. Impedance values increased from 11.6 MΩ to 13.5 MΩ at 1 Hz, 1.2 MΩ-2.9 MΩ at 1 kHz, and 0.11 MΩ-0.13 MΩ at 30 kHz over 16 weeks. The median charge storage capacity of the implanted electrodes at 50 mV/s was 58.1 mC/cm2 on week 1 and 55.9 mC/cm2 on week 16, and at 50,000 mV/s, 4.27 mC/cm2 on week 1 and 5.93 mC/cm2 on week 16. Devices were able to record neural activity from 92% of all active channels at the beginning of the study, At the study endpoint, a-SiC devices were still recording single-unit activity on 51% of electrochemically active electrode channels. In addition, we observed that the signal-to-noise ratio experienced a small decline of -0.19 per week. We also classified observed units as fast and slow repolarizing based on the trough-to-peak time. Although the overall presence of single units declined, fast and slow repolarizing units declined at a similar rate. At recording electrode depth, immunohistochemistry showed minimal tissue response to the a-SiC devices, as indicated by statistically insignificant differences in activated glial cell response between implanted brains slices and contralateral sham slices at 150 μm away from the implant location, as evidenced by GFAP staining. NeuN staining revealed the presence of neuronal cell bodies close to the implantation site, again statistically not different from a contralateral sham slice. These results warrant further investigation of a-SiC MEAs for future long-term implantation neural recording studies.
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Affiliation(s)
- Justin R Abbott
- Department of Bioengineering, The University of Texas at Dallas, Richardson, TX, United States
| | - Eleanor N Jeakle
- Department of Bioengineering, The University of Texas at Dallas, Richardson, TX, United States
| | - Pegah Haghighi
- Department of Bioengineering, The University of Texas at Dallas, Richardson, TX, United States
| | - Joshua O Usoro
- Department of Bioengineering, The University of Texas at Dallas, Richardson, TX, United States
| | - Brandon S Sturgill
- Department of Bioengineering, The University of Texas at Dallas, Richardson, TX, United States
| | - Yupeng Wu
- Department of Materials Science and Engineering, The University of Texas at Dallas, Richardson, TX, United States
| | - Negar Geramifard
- Department of Bioengineering, The University of Texas at Dallas, Richardson, TX, United States
| | - Rahul Radhakrishna
- Department of Bioengineering, The University of Texas at Dallas, Richardson, TX, United States
| | - Sourav Patnaik
- Department of Bioengineering, The University of Texas at Dallas, Richardson, TX, United States
| | - Shido Nakajima
- Department of Bioengineering, The University of Texas at Dallas, Richardson, TX, United States
| | - Jordan Hess
- School of Behavioral and Brain Sciences, The University of Texas at Dallas, Richardson, TX, United States
| | - Yusef Mehmood
- Department of Bioengineering, The University of Texas at Dallas, Richardson, TX, United States
| | - Veda Devata
- Department of Chemistry and Biochemistry, The University of Texas at Dallas, Richardson, TX, United States
| | - Gayathri Vijayakumar
- School of Behavioral and Brain Sciences, The University of Texas at Dallas, Richardson, TX, United States
| | - Armaan Sood
- School of Behavioral and Brain Sciences, The University of Texas at Dallas, Richardson, TX, United States
| | - Teresa Thuc Doan Thai
- Department of Bioengineering, The University of Texas at Dallas, Richardson, TX, United States
| | - Komal Dogra
- Department of Bioengineering, The University of Texas at Dallas, Richardson, TX, United States
| | - Ana G Hernandez-Reynoso
- Department of Bioengineering, The University of Texas at Dallas, Richardson, TX, United States
| | - Joseph J Pancrazio
- Department of Bioengineering, The University of Texas at Dallas, Richardson, TX, United States
| | - Stuart F Cogan
- Department of Bioengineering, The University of Texas at Dallas, Richardson, TX, United States.
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7
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Dawit H, Zhao Y, Wang J, Pei R. Advances in conductive hydrogels for neural recording and stimulation. Biomater Sci 2024; 12:2786-2800. [PMID: 38682423 DOI: 10.1039/d4bm00048j] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/01/2024]
Abstract
The brain-computer interface (BCI) allows the human or animal brain to directly interact with the external environment through the neural interfaces, thus playing the role of monitoring, protecting, improving/restoring, enhancing, and replacing. Recording electrophysiological information such as brain neural signals is of great importance in health monitoring and disease diagnosis. According to the electrode position, it can be divided into non-implantable, semi-implantable, and implantable. Among them, implantable neural electrodes can obtain the highest-quality electrophysiological information, so they have the most promising application. However, due to the chemo-mechanical mismatch between devices and tissues, the adverse foreign body response and performance loss over time seriously restrict the development and application of implantable neural electrodes. Given the challenges, conductive hydrogel-based neural electrodes have recently attracted much attention, owing to many advantages such as good mechanical match with the native tissues, negligible foreign body response, and minimal signal attenuation. This review mainly focuses on the current development of conductive hydrogels as a biocompatible framework for neural tissue and conductivity-supporting substrates for the transmission of electrical signals of neural tissue to speed up electrical regeneration and their applications in neural sensing and recording as well as stimulation.
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Affiliation(s)
- Hewan Dawit
- School of Nano-Tech and Nano-Bionics, University of Science and Technology of China (USTC), Hefei 230026, PR China
- CAS Key Laboratory of Nano-Bio Interface, Suzhou Institute of Nano-Tech and Nano-Bionics, Chinese Academy of Sciences, Suzhou, 215123, China.
| | - Yuewu Zhao
- CAS Key Laboratory of Nano-Bio Interface, Suzhou Institute of Nano-Tech and Nano-Bionics, Chinese Academy of Sciences, Suzhou, 215123, China.
| | - Jine Wang
- College of Medicine and Nursing, Shandong Provincial Engineering Laboratory of Novel Pharmaceutical Excipients, Sustained and Controlled Release Preparations, Dezhou University, China.
- Jiangxi Institute of Nanotechnology, Nanchang, 330200, China
| | - Renjun Pei
- School of Nano-Tech and Nano-Bionics, University of Science and Technology of China (USTC), Hefei 230026, PR China
- CAS Key Laboratory of Nano-Bio Interface, Suzhou Institute of Nano-Tech and Nano-Bionics, Chinese Academy of Sciences, Suzhou, 215123, China.
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8
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Huang D, Wang Y, Fan L, Yu Y, Zhao Z, Zeng P, Wang K, Li N, Shen H. Decoding Subject-Driven Cognitive States from EEG Signals for Cognitive Brain-Computer Interface. Brain Sci 2024; 14:498. [PMID: 38790476 PMCID: PMC11120245 DOI: 10.3390/brainsci14050498] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2024] [Revised: 05/06/2024] [Accepted: 05/14/2024] [Indexed: 05/26/2024] Open
Abstract
In this study, we investigated the feasibility of using electroencephalogram (EEG) signals to differentiate between four distinct subject-driven cognitive states: resting state, narrative memory, music, and subtraction tasks. EEG data were collected from seven healthy male participants while performing these cognitive tasks, and the raw EEG signals were transformed into time-frequency maps using continuous wavelet transform. Based on these time-frequency maps, we developed a convolutional neural network model (TF-CNN-CFA) with a channel and frequency attention mechanism to automatically distinguish between these cognitive states. The experimental results demonstrated that the model achieved an average classification accuracy of 76.14% in identifying these four cognitive states, significantly outperforming traditional EEG signal processing methods and other classical image classification algorithms. Furthermore, we investigated the impact of varying lengths of EEG signals on classification performance and found that TF-CNN-CFA demonstrates consistent performance across different window lengths, indicating its strong generalization capability. This study validates the ability of EEG to differentiate higher cognitive states, which could potentially offer a novel BCI paradigm.
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Affiliation(s)
- Dingyong Huang
- College of Intelligence Science and Technology, National University of Defense Technology, Changsha 410073, China; (D.H.); (L.F.); (Y.Y.); (Z.Z.); (P.Z.); (K.W.)
| | - Yingjie Wang
- College of Physical Education and Health, Hebei Normal University of Science & Technology, Qinhuangdao 066004, China;
| | - Liangwei Fan
- College of Intelligence Science and Technology, National University of Defense Technology, Changsha 410073, China; (D.H.); (L.F.); (Y.Y.); (Z.Z.); (P.Z.); (K.W.)
| | - Yang Yu
- College of Intelligence Science and Technology, National University of Defense Technology, Changsha 410073, China; (D.H.); (L.F.); (Y.Y.); (Z.Z.); (P.Z.); (K.W.)
| | - Ziyu Zhao
- College of Intelligence Science and Technology, National University of Defense Technology, Changsha 410073, China; (D.H.); (L.F.); (Y.Y.); (Z.Z.); (P.Z.); (K.W.)
| | - Pu Zeng
- College of Intelligence Science and Technology, National University of Defense Technology, Changsha 410073, China; (D.H.); (L.F.); (Y.Y.); (Z.Z.); (P.Z.); (K.W.)
| | - Kunqing Wang
- College of Intelligence Science and Technology, National University of Defense Technology, Changsha 410073, China; (D.H.); (L.F.); (Y.Y.); (Z.Z.); (P.Z.); (K.W.)
| | - Na Li
- Radiology Department, Xiangya 3rd Hospital, Central South University, Changsha 410013, China;
| | - Hui Shen
- College of Intelligence Science and Technology, National University of Defense Technology, Changsha 410073, China; (D.H.); (L.F.); (Y.Y.); (Z.Z.); (P.Z.); (K.W.)
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9
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Tankus A, Rosenberg N, Ben-Hamo O, Stern E, Strauss I. Machine learning decoding of single neurons in the thalamus for speech brain-machine interfaces. J Neural Eng 2024; 21:036009. [PMID: 38648783 DOI: 10.1088/1741-2552/ad4179] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2023] [Accepted: 04/22/2024] [Indexed: 04/25/2024]
Abstract
Objective. Our goal is to decode firing patterns of single neurons in the left ventralis intermediate nucleus (Vim) of the thalamus, related to speech production, perception, and imagery. For realistic speech brain-machine interfaces (BMIs), we aim to characterize the amount of thalamic neurons necessary for high accuracy decoding.Approach. We intraoperatively recorded single neuron activity in the left Vim of eight neurosurgical patients undergoing implantation of deep brain stimulator or RF lesioning during production, perception and imagery of the five monophthongal vowel sounds. We utilized the Spade decoder, a machine learning algorithm that dynamically learns specific features of firing patterns and is based on sparse decomposition of the high dimensional feature space.Main results. Spade outperformed all algorithms compared with, for all three aspects of speech: production, perception and imagery, and obtained accuracies of 100%, 96%, and 92%, respectively (chance level: 20%) based on pooling together neurons across all patients. The accuracy was logarithmic in the amount of neurons for all three aspects of speech. Regardless of the amount of units employed, production gained highest accuracies, whereas perception and imagery equated with each other.Significance. Our research renders single neuron activity in the left Vim a promising source of inputs to BMIs for restoration of speech faculties for locked-in patients or patients with anarthria or dysarthria to allow them to communicate again. Our characterization of how many neurons are necessary to achieve a certain decoding accuracy is of utmost importance for planning BMI implantation.
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Affiliation(s)
- Ariel Tankus
- Functional Neurosurgery Unit, Tel Aviv Sourasky Medical Center, Tel Aviv 6423906, Israel
- Department of Neurology and Neurosurgery, School of Medicine, Tel Aviv University, Tel Aviv 6997801, Israel
- Sagol School of Neuroscience, Tel Aviv University, Tel Aviv 6997801, Israel
| | - Noam Rosenberg
- School of Electrical Engineering, Iby and Aladar Fleischman Faculty of Engineering, Tel Aviv University, Tel Aviv 6997801, Israel
| | - Oz Ben-Hamo
- School of Electrical Engineering, Iby and Aladar Fleischman Faculty of Engineering, Tel Aviv University, Tel Aviv 6997801, Israel
| | - Einat Stern
- Department of Neurology and Neurosurgery, School of Medicine, Tel Aviv University, Tel Aviv 6997801, Israel
| | - Ido Strauss
- Functional Neurosurgery Unit, Tel Aviv Sourasky Medical Center, Tel Aviv 6423906, Israel
- Department of Neurology and Neurosurgery, School of Medicine, Tel Aviv University, Tel Aviv 6997801, Israel
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10
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Menéndez JA, Hennig JA, Golub MD, Oby ER, Sadtler PT, Batista AP, Chase SM, Yu BM, Latham PE. A theory of brain-computer interface learning via low-dimensional control. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.04.18.589952. [PMID: 38712193 PMCID: PMC11071278 DOI: 10.1101/2024.04.18.589952] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/08/2024]
Abstract
A remarkable demonstration of the flexibility of mammalian motor systems is primates' ability to learn to control brain-computer interfaces (BCIs). This constitutes a completely novel motor behavior, yet primates are capable of learning to control BCIs under a wide range of conditions. BCIs with carefully calibrated decoders, for example, can be learned with only minutes to hours of practice. With a few weeks of practice, even BCIs with randomly constructed decoders can be learned. What are the biological substrates of this learning process? Here, we develop a theory based on a re-aiming strategy, whereby learning operates within a low-dimensional subspace of task-relevant inputs driving the local population of recorded neurons. Through comprehensive numerical and formal analysis, we demonstrate that this theory can provide a unifying explanation for disparate phenomena previously reported in three different BCI learning tasks, and we derive a novel experimental prediction that we verify with previously published data. By explicitly modeling the underlying neural circuitry, the theory reveals an interpretation of these phenomena in terms of biological constraints on neural activity.
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11
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Deo DR, Willett FR, Avansino DT, Hochberg LR, Henderson JM, Shenoy KV. Brain control of bimanual movement enabled by recurrent neural networks. Sci Rep 2024; 14:1598. [PMID: 38238386 PMCID: PMC10796685 DOI: 10.1038/s41598-024-51617-3] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2023] [Accepted: 01/07/2024] [Indexed: 01/22/2024] Open
Abstract
Brain-computer interfaces have so far focused largely on enabling the control of a single effector, for example a single computer cursor or robotic arm. Restoring multi-effector motion could unlock greater functionality for people with paralysis (e.g., bimanual movement). However, it may prove challenging to decode the simultaneous motion of multiple effectors, as we recently found that a compositional neural code links movements across all limbs and that neural tuning changes nonlinearly during dual-effector motion. Here, we demonstrate the feasibility of high-quality bimanual control of two cursors via neural network (NN) decoders. Through simulations, we show that NNs leverage a neural 'laterality' dimension to distinguish between left and right-hand movements as neural tuning to both hands become increasingly correlated. In training recurrent neural networks (RNNs) for two-cursor control, we developed a method that alters the temporal structure of the training data by dilating/compressing it in time and re-ordering it, which we show helps RNNs successfully generalize to the online setting. With this method, we demonstrate that a person with paralysis can control two computer cursors simultaneously. Our results suggest that neural network decoders may be advantageous for multi-effector decoding, provided they are designed to transfer to the online setting.
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Affiliation(s)
- Darrel R Deo
- Department of Neurosurgery, Stanford University, Stanford, CA, USA.
- Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA, USA.
| | - Francis R Willett
- Howard Hughes Medical Institute at Stanford University, Stanford, CA, USA
| | - Donald T Avansino
- Howard Hughes Medical Institute at Stanford University, Stanford, CA, USA
| | - Leigh R Hochberg
- School of Engineering, Brown University, Providence, RI, USA
- Carney Institute for Brain Science, Brown University, Providence, RI, USA
- VA RR&D Center for Neurorestoration and Neurotechnology, Rehabilitation R&D Service, Providence VA Medical Center, Providence, RI, USA
- Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Jaimie M Henderson
- Department of Neurosurgery, Stanford University, Stanford, CA, USA
- Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA, USA
- Bio-X Institute, Stanford University, Stanford, CA, USA
| | - Krishna V Shenoy
- Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA, USA
- Howard Hughes Medical Institute at Stanford University, Stanford, CA, USA
- Bio-X Institute, Stanford University, Stanford, CA, USA
- 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
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12
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Zhang C, Wang H, Tang S, Li Z. Rhesus monkeys learn to control a directional-key inspired brain machine interface via bio-feedback. PLoS One 2024; 19:e0286742. [PMID: 38232123 DOI: 10.1371/journal.pone.0286742] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2022] [Accepted: 05/23/2023] [Indexed: 01/19/2024] Open
Abstract
Brain machine interfaces (BMI) connect brains directly to the outside world, bypassing natural neural systems and actuators. Neuronal-activity-to-motion transformation algorithms allow applications such as control of prosthetics or computer cursors. These algorithms lie within a spectrum between bio-mimetic control and bio-feedback control. The bio-mimetic approach relies on increasingly complex algorithms to decode neural activity by mimicking the natural neural system and actuator relationship while focusing on machine learning: the supervised fitting of decoder parameters. On the other hand, the bio-feedback approach uses simple algorithms and relies primarily on user learning, which may take some time, but can facilitate control of novel, non-biological appendages. An increasing amount of work has focused on the arguably more successful bio-mimetic approach. However, as chronic recordings have become more accessible and utilization of novel appendages such as computer cursors have become more universal, users can more easily spend time learning in a bio-feedback control paradigm. We believe a simple approach which leverages user learning and few assumptions will provide users with good control ability. To test the feasibility of this idea, we implemented a simple firing-rate-to-motion correspondence rule, assigned groups of neurons to virtual "directional keys" for control of a 2D cursor. Though not strictly required, to facilitate initial control, we selected neurons with similar preferred directions for each group. The groups of neurons were kept the same across multiple recording sessions to allow learning. Two Rhesus monkeys used this BMI to perform a center-out cursor movement task. After about a week of training, monkeys performed the task better and neuronal signal patterns changed on a group basis, indicating learning. While our experiments did not compare this bio-feedback BMI to bio-mimetic BMIs, the results demonstrate the feasibility of our control paradigm and paves the way for further research in multi-dimensional bio-feedback BMIs.
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Affiliation(s)
- Chenguang Zhang
- Center for Cognition and Neuroergonomics, State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University at Zhuhai, Zhuhai, People's Republic of China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, People's Republic of China
| | - Hao Wang
- Institute of Big Data and Artificial Intelligence, China Telecom Corporation Limited Beijing Research Institute, Beijing, China
| | - Shaohua Tang
- Center for Cognition and Neuroergonomics, State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University at Zhuhai, Zhuhai, People's Republic of China
- School of Systems Science, Beijing Normal University, Beijing, China
- International Academic Center of Complex Systems, Beijing Normal University, Zhuhai, China
| | - Zheng Li
- Center for Cognition and Neuroergonomics, State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University at Zhuhai, Zhuhai, People's Republic of China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, People's Republic of China
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13
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Canny E, Vansteensel MJ, van der Salm SMA, Müller-Putz GR, Berezutskaya J. Boosting brain-computer interfaces with functional electrical stimulation: potential applications in people with locked-in syndrome. J Neuroeng Rehabil 2023; 20:157. [PMID: 37980536 PMCID: PMC10656959 DOI: 10.1186/s12984-023-01272-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2023] [Accepted: 10/23/2023] [Indexed: 11/20/2023] Open
Abstract
Individuals with a locked-in state live with severe whole-body paralysis that limits their ability to communicate with family and loved ones. Recent advances in brain-computer interface (BCI) technology have presented a potential alternative for these people to communicate by detecting neural activity associated with attempted hand or speech movements and translating the decoded intended movements to a control signal for a computer. A technique that could potentially enrich the communication capacity of BCIs is functional electrical stimulation (FES) of paralyzed limbs and face to restore body and facial movements of paralyzed individuals, allowing to add body language and facial expression to communication BCI utterances. Here, we review the current state of the art of existing BCI and FES work in people with paralysis of body and face and propose that a combined BCI-FES approach, which has already proved successful in several applications in stroke and spinal cord injury, can provide a novel promising mode of communication for locked-in individuals.
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Affiliation(s)
- Evan Canny
- Department of Neurology and Neurosurgery, Brain Center, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Mariska J Vansteensel
- Department of Neurology and Neurosurgery, Brain Center, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Sandra M A van der Salm
- Department of Neurology and Neurosurgery, Brain Center, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Gernot R Müller-Putz
- Institute of Neural Engineering, Laboratory of Brain-Computer Interfaces, Graz University of Technology, Graz, Austria
| | - Julia Berezutskaya
- Department of Neurology and Neurosurgery, Brain Center, University Medical Center Utrecht, Utrecht, The Netherlands.
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14
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Okatan M, Kocatürk M. Decoding the Spike-Band Subthreshold Motor Cortical Activity. J Mot Behav 2023; 56:161-183. [PMID: 37964432 DOI: 10.1080/00222895.2023.2280263] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2023] [Accepted: 10/25/2023] [Indexed: 11/16/2023]
Abstract
Intracortical Brain-Computer Interfaces (iBCI) use single-unit activity (SUA), multiunit activity (MUA) and local field potentials (LFP) to control neuroprosthetic devices. SUA and MUA are usually extracted from the bandpassed recording through amplitude thresholding, while subthreshold data are ignored. Here, we show that subthreshold data can actually be decoded to determine behavioral variables with test set accuracy of up to 100%. Although the utility of SUA, MUA and LFP for decoding behavioral variables has been explored previously, this study investigates the utility of spike-band subthreshold activity exclusively. We provide evidence suggesting that this activity can be used to keep decoding performance at acceptable levels even when SUA quality is reduced over time. To the best of our knowledge, the signals that we derive from the subthreshold activity may be the weakest neural signals that have ever been extracted from extracellular neural recordings, while still being decodable with test set accuracy of up to 100%. These results are relevant for the development of fully data-driven and automated methods for amplitude thresholding spike-band extracellular neural recordings in iBCIs containing thousands of electrodes.
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Affiliation(s)
- Murat Okatan
- Informatics Institute, Istanbul Technical University, Istanbul, Türkiye
- Artificial Intelligence and Data Engineering Department, Istanbul Technical University, Istanbul, Türkiye
| | - Mehmet Kocatürk
- Biomedical Engineering Department, Istanbul Medipol University, Istanbul, Türkiye
- Research Institute for Health Sciences and Technologies (SABITA), Istanbul Medipol University, Istanbul, Türkiye
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15
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Lai C, Tanaka S, Harris TD, Lee AK. Volitional activation of remote place representations with a hippocampal brain-machine interface. Science 2023; 382:566-573. [PMID: 37917713 PMCID: PMC10683874 DOI: 10.1126/science.adh5206] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2023] [Accepted: 09/22/2023] [Indexed: 11/04/2023]
Abstract
The hippocampus is critical for recollecting and imagining experiences. This is believed to involve voluntarily drawing from hippocampal memory representations of people, events, and places, including maplike representations of familiar environments. However, whether representations in such "cognitive maps" can be volitionally accessed is unknown. We developed a brain-machine interface to test whether rats can do so by controlling their hippocampal activity in a flexible, goal-directed, and model-based manner. We found that rats can efficiently navigate or direct objects to arbitrary goal locations within a virtual reality arena solely by activating and sustaining appropriate hippocampal representations of remote places. This provides insight into the mechanisms underlying episodic memory recall, mental simulation and planning, and imagination and opens up possibilities for high-level neural prosthetics that use hippocampal representations.
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Affiliation(s)
- Chongxi Lai
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, Virginia, USA
| | - Shinsuke Tanaka
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, Virginia, USA
| | - Timothy D. Harris
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, Virginia, USA
| | - Albert K. Lee
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, Virginia, USA
- Howard Hughes Medical Institute and Department of Neurology, Beth Israel Deaconess Medical Center, Boston, MA, USA
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16
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Tian Y, Yin J, Wang C, He Z, Xie J, Feng X, Zhou Y, Ma T, Xie Y, Li X, Yang T, Ren C, Li C, Zhao Z. An Ultraflexible Electrode Array for Large-Scale Chronic Recording in the Nonhuman Primate Brain. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2023; 10:e2302333. [PMID: 37870175 PMCID: PMC10667845 DOI: 10.1002/advs.202302333] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/12/2023] [Revised: 09/08/2023] [Indexed: 10/24/2023]
Abstract
Single-unit (SU) recording in nonhuman primates (NHPs) is indispensible in the quest of how the brain works, yet electrodes currently used for the NHP brain are limited in signal longevity, stability, and spatial coverage. Using new structural materials, microfabrication, and penetration techniques, we develop a mechanically robust ultraflexible, 1 µm thin electrode array (MERF) that enables pial penetration and high-density, large-scale, and chronic recording of neurons along both vertical and horizontal cortical axes in the nonhuman primate brain. Recording from three monkeys yields 2,913 SUs from 1,065 functional recording channels (up to 240 days), with some SUs tracked for up to 2 months. Recording from the primary visual cortex (V1) reveals that neurons with similar orientation preferences for visual stimuli exhibited higher spike correlation. Furthermore, simultaneously recorded neurons in different cortical layers of the primary motor cortex (M1) show preferential firing for hand movements of different directions. Finally, it is shown that a linear decoder trained with neuronal spiking activity across M1 layers during monkey's hand movements can be used to achieve on-line control of cursor movement. Thus, the MERF electrode array offers a new tool for basic neuroscience studies and brain-machine interface (BMI) applications in the primate brain.
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Affiliation(s)
- Yixin Tian
- Institute of NeuroscienceCenter for Excellence in Brain Science and Intelligence TechnologyChinese Academy of SciencesShanghai200031China
| | - Jiapeng Yin
- Shanghai Center for Brain Science and Brain‐Inspired TechnologyShanghai201602China
- Lingang LaboratoryShanghai200031China
| | - Chengyao Wang
- Institute of NeuroscienceCenter for Excellence in Brain Science and Intelligence TechnologyChinese Academy of SciencesShanghai200031China
| | - Zhenliang He
- Lingang LaboratoryShanghai200031China
- Institute of NeuroscienceState Key Laboratory of Neuroscience, Center for Excellence in Brain Science and Intelligence TechnologyChinese Academy of SciencesShanghai200031China
| | - Jingyi Xie
- Institute of NeuroscienceCenter for Excellence in Brain Science and Intelligence TechnologyChinese Academy of SciencesShanghai200031China
- University of Chinese Academy of SciencesBeijing100049China
| | - Xiaoshan Feng
- Institute of NeuroscienceCenter for Excellence in Brain Science and Intelligence TechnologyChinese Academy of SciencesShanghai200031China
| | - Yang Zhou
- Institute of NeuroscienceCenter for Excellence in Brain Science and Intelligence TechnologyChinese Academy of SciencesShanghai200031China
| | - Tianyu Ma
- Institute of NeuroscienceCenter for Excellence in Brain Science and Intelligence TechnologyChinese Academy of SciencesShanghai200031China
- University of Chinese Academy of SciencesBeijing100049China
| | - Yang Xie
- Lingang LaboratoryShanghai200031China
- Institute of NeuroscienceKey Laboratory of Primate Neurobiology, Center for Excellence in Brain Science and Intelligence TechnologyChinese Academy of SciencesShanghai200031China
| | - Xue Li
- Institute of NeuroscienceCenter for Excellence in Brain Science and Intelligence TechnologyChinese Academy of SciencesShanghai200031China
| | - Tianming Yang
- Institute of NeuroscienceCenter for Excellence in Brain Science and Intelligence TechnologyChinese Academy of SciencesShanghai200031China
- University of Chinese Academy of SciencesBeijing100049China
| | - Chi Ren
- Institute of NeuroscienceCenter for Excellence in Brain Science and Intelligence TechnologyChinese Academy of SciencesShanghai200031China
| | - Chengyu Li
- Lingang LaboratoryShanghai200031China
- Institute of NeuroscienceState Key Laboratory of Neuroscience, Center for Excellence in Brain Science and Intelligence TechnologyChinese Academy of SciencesShanghai200031China
| | - Zhengtuo Zhao
- Institute of NeuroscienceCenter for Excellence in Brain Science and Intelligence TechnologyChinese Academy of SciencesShanghai200031China
- University of Chinese Academy of SciencesBeijing100049China
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17
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Perna A, Angotzi GN, Berdondini L, Ribeiro JF. Advancing the interfacing performances of chronically implantable neural probes in the era of CMOS neuroelectronics. Front Neurosci 2023; 17:1275908. [PMID: 38027514 PMCID: PMC10644322 DOI: 10.3389/fnins.2023.1275908] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2023] [Accepted: 10/10/2023] [Indexed: 12/01/2023] Open
Abstract
Tissue penetrating microelectrode neural probes can record electrophysiological brain signals at resolutions down to single neurons, making them invaluable tools for neuroscience research and Brain-Computer-Interfaces (BCIs). The known gradual decrease of their electrical interfacing performances in chronic settings, however, remains a major challenge. A key factor leading to such decay is Foreign Body Reaction (FBR), which is the cascade of biological responses that occurs in the brain in the presence of a tissue damaging artificial device. Interestingly, the recent adoption of Complementary Metal Oxide Semiconductor (CMOS) technology to realize implantable neural probes capable of monitoring hundreds to thousands of neurons simultaneously, may open new opportunities to face the FBR challenge. Indeed, this shift from passive Micro Electro-Mechanical Systems (MEMS) to active CMOS neural probe technologies creates important, yet unexplored, opportunities to tune probe features such as the mechanical properties of the probe, its layout, size, and surface physicochemical properties, to minimize tissue damage and consequently FBR. Here, we will first review relevant literature on FBR to provide a better understanding of the processes and sources underlying this tissue response. Methods to assess FBR will be described, including conventional approaches based on the imaging of biomarkers, and more recent transcriptomics technologies. Then, we will consider emerging opportunities offered by the features of CMOS probes. Finally, we will describe a prototypical neural probe that may meet the needs for advancing clinical BCIs, and we propose axial insertion force as a potential metric to assess the influence of probe features on acute tissue damage and to control the implantation procedure to minimize iatrogenic injury and subsequent FBR.
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Affiliation(s)
- Alberto Perna
- Microtechnology for Neuroelectronics Lab, Fondazione Istituto Italiano di Tecnologia, Neuroscience and Brain Technologies, Genova, Italy
- The Open University Affiliated Research Centre at Istituto Italiano di Tecnologia (ARC@IIT), Istituto Italiano di Tecnologia, Genova, Italy
| | - Gian Nicola Angotzi
- Microtechnology for Neuroelectronics Lab, Fondazione Istituto Italiano di Tecnologia, Neuroscience and Brain Technologies, Genova, Italy
| | - Luca Berdondini
- Microtechnology for Neuroelectronics Lab, Fondazione Istituto Italiano di Tecnologia, Neuroscience and Brain Technologies, Genova, Italy
| | - João Filipe Ribeiro
- Microtechnology for Neuroelectronics Lab, Fondazione Istituto Italiano di Tecnologia, Neuroscience and Brain Technologies, Genova, Italy
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18
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Wang HL, Kuo YT, Lo YC, Kuo CH, Chen BW, Wang CF, Wu ZY, Lee CE, Yang SH, Lin SH, Chen PC, Chen YY. Enhancing Prediction of Forelimb Movement Trajectory through a Calibrating-Feedback Paradigm Incorporating RAT Primary Motor and Agranular Cortical Ensemble Activity in the Goal-Directed Reaching Task. Int J Neural Syst 2023; 33:2350051. [PMID: 37632142 DOI: 10.1142/s012906572350051x] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/27/2023]
Abstract
Complete reaching movements involve target sensing, motor planning, and arm movement execution, and this process requires the integration and communication of various brain regions. Previously, reaching movements have been decoded successfully from the motor cortex (M1) and applied to prosthetic control. However, most studies attempted to decode neural activities from a single brain region, resulting in reduced decoding accuracy during visually guided reaching motions. To enhance the decoding accuracy of visually guided forelimb reaching movements, we propose a parallel computing neural network using both M1 and medial agranular cortex (AGm) neural activities of rats to predict forelimb-reaching movements. The proposed network decodes M1 neural activities into the primary components of the forelimb movement and decodes AGm neural activities into internal feedforward information to calibrate the forelimb movement in a goal-reaching movement. We demonstrate that using AGm neural activity to calibrate M1 predicted forelimb movement can improve decoding performance significantly compared to neural decoders without calibration. We also show that the M1 and AGm neural activities contribute to controlling forelimb movement during goal-reaching movements, and we report an increase in the power of the local field potential (LFP) in beta and gamma bands over AGm in response to a change in the target distance, which may involve sensorimotor transformation and communication between the visual cortex and AGm when preparing for an upcoming reaching movement. The proposed parallel computing neural network with the internal feedback model improves prediction accuracy for goal-reaching movements.
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Affiliation(s)
- Han-Lin Wang
- Department of Biomedical Engineering, National Yang Ming Chiao Tung University, No. 155, Sec. 2 Linong St., Taipei 112304, Taiwan
| | - Yun-Ting Kuo
- Department of Biomedical Engineering, National Yang Ming Chiao Tung University, No. 155, Sec. 2 Linong St., Taipei 112304, Taiwan
| | - Yu-Chun Lo
- The Ph.D. Program in Medical Neuroscience, College of Medical Science and Technology, Taipei Medical University, 12F., Education & Research Building, Shuang-Ho Campus, No. 301, Yuantong Rd., New Taipei City 235235, Taiwan
| | - Chao-Hung Kuo
- Department of Neurosurgery, Neurological Institute Taipei Veterans General Hospital, No. 201, Sec. 2 Shipai Rd., Taipei 11217, Taiwan
| | - Bo-Wei Chen
- Department of Biomedical Engineering, National Yang Ming Chiao Tung University, No. 155, Sec. 2 Linong St., Taipei 112304, Taiwan
| | - Ching-Fu Wang
- Department of Biomedical Engineering, National Yang Ming Chiao Tung University, No. 155, Sec. 2 Linong St., Taipei 112304, Taiwan
- Biomedical Engineering Research and Development Center, National Yang Ming Chiao Tung University, No. 155, Sec. 2 Linong St., Taipei 112304, Taiwan
| | - Zu-Yu Wu
- Department of Biomedical Engineering, National Yang Ming Chiao Tung University, No. 155, Sec. 2 Linong St., Taipei 112304, Taiwan
| | - Chi-En Lee
- Department of Biomedical Engineering, National Yang Ming Chiao Tung University, No. 155, Sec. 2 Linong St., Taipei 112304, Taiwan
| | - Shih-Hung Yang
- Department of Mechanical Engineering, National Cheng Kung University, No. 1, University Rd., Tainan 70101, Taiwan
| | - Sheng-Huang Lin
- Department of Neurology, Hualien Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, No. 707, Sec. 3 Zhongyang Rd., Hualien 97002, Taiwan
- Department of Neurology, School of Medicine, Tzu Chi University, No. 701, Sec. 3, Zhongyang Rd., Hualien 97004, Taiwan
| | - Po-Chuan Chen
- School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA
| | - You-Yin Chen
- Department of Biomedical Engineering, National Yang Ming Chiao Tung University, No. 155, Sec. 2 Linong St., Taipei 112304, Taiwan
- The Ph.D. Program in Medical Neuroscience, College of Medical Science and Technology, Taipei Medical University, 12F., Education & Research Building, Shuang-Ho Campus, No. 301, Yuantong Rd., New Taipei City 235235, Taiwan
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19
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Bhat A, Jaipurkar SS, Low LT, Yeow RCH. Reconfigurable Soft Pneumatic Actuators Using Extensible Fabric-Based Skins. Soft Robot 2023; 10:923-936. [PMID: 37042707 DOI: 10.1089/soro.2022.0089] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/13/2023] Open
Abstract
The development of the field of soft robotics has led to the exploration of novel techniques to manufacture soft actuators, which provide distinct advantages for wearable assistive robotics. One subset of these soft pneumatic actuators is conventionally developed from silicone, fabrics, and thermoplastic polyurethane (TPU). Each of these materials in isolation possesses limitations of low-stress capacity, low-design complexity, and high-input pressure requirements, respectively. Combining these materials can overcome some limitations and maintain their desirable properties. In this article, we explore one such composite design scheme using a combination of silicone polymer-based bladder and reconfigurable fabric skin made from an anisotropic extensible fabric. The silicone polymer bladder acts as the hermetic seal, while this skin acts as the constraint. Bending and torsional actuators were designed utilizing the anisotropy of these fabrics. The torsional actuator designs can achieve over 540° of twist, significantly larger than previously reported in the literature, owing to the lower mechanical impedance of the extensible fabrics. Actuators with 360° of bending were also fabricated using this method. In addition, the lack of TPU-backed or inextensible fabrics reduces the actuator's stiffness, leading to lower actuation pressures. Skin-based designs also confer the advantage of modularity, reconfigurability, and the ability to achieve complex motions by tuning the properties of the bladder and the skin. For applications with high-force requirements, such as wearable exoskeletons, we demonstrate the utility of multilayer design schemes. A multilayer bending actuator generated 190 N of force at 100 kPa and was shown to be a candidate for wearable assistive devices. In addition, torsional designs were shown to have utility in practical scenarios such as screwing on a bottle cap and turning knobs. Thus, we present a novel fabric-skin-based design concept that is highly versatile and customizable for various application requirements.
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Affiliation(s)
- Ajinkya Bhat
- Evolution Innovation Laboratory, National University of Singapore, Singapore, Singapore
- Integrated Science and Engineering Program (ISEP), National University of Singapore, Singapore, Singapore
| | - Shobhit Sandeep Jaipurkar
- Evolution Innovation Laboratory, National University of Singapore, Singapore, Singapore
- Department of Biomedical Engineering, National University of Singapore, Singapore, Singapore
| | - Li Ting Low
- Evolution Innovation Laboratory, National University of Singapore, Singapore, Singapore
- Department of Biomedical Engineering, National University of Singapore, Singapore, Singapore
| | - Raye Chen-Hua Yeow
- Evolution Innovation Laboratory, National University of Singapore, Singapore, Singapore
- Department of Biomedical Engineering, National University of Singapore, Singapore, Singapore
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Zhou Y, Yu T, Gao W, Huang W, Lu Z, Huang Q, Li Y. Shared Three-Dimensional Robotic Arm Control Based on Asynchronous BCI and Computer Vision. IEEE Trans Neural Syst Rehabil Eng 2023; 31:3163-3175. [PMID: 37498753 DOI: 10.1109/tnsre.2023.3299350] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/29/2023]
Abstract
OBJECTIVE A brain-computer interface (BCI) can be used to translate neuronal activity into commands to control external devices. However, using noninvasive BCI to control a robotic arm for movements in three-dimensional (3D) environments and accomplish complicated daily tasks, such as grasping and drinking, remains a challenge. APPROACH In this study, a shared robotic arm control system based on hybrid asynchronous BCI and computer vision was presented. The BCI model, which combines steady-state visual evoked potentials (SSVEPs) and blink-related electrooculography (EOG) signals, allows users to freely choose from fifteen commands in an asynchronous mode corresponding to robot actions in a 3D workspace and reach targets with a wide movement range, while computer vision can identify objects and assist a robotic arm in completing more precise tasks, such as grasping a target automatically. RESULTS Ten subjects participated in the experiments and achieved an average accuracy of more than 92% and a high trajectory efficiency for robot movement. All subjects were able to perform the reach-grasp-drink tasks successfully using the proposed shared control method, with fewer error commands and shorter completion time than with direct BCI control. SIGNIFICANCE Our results demonstrated the feasibility and efficiency of generating practical multidimensional control of an intuitive robotic arm by merging hybrid asynchronous BCI and computer vision-based recognition.
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Wang Y, Wang Q, Zheng R, Xu X, Yang X, Gui Q, Yang X, Wang Y, Cui H, Pei W. Flexible multichannel electrodes for acute recording in nonhuman primates. MICROSYSTEMS & NANOENGINEERING 2023; 9:93. [PMID: 37484502 PMCID: PMC10359297 DOI: 10.1038/s41378-023-00550-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/15/2022] [Revised: 04/07/2023] [Accepted: 04/29/2023] [Indexed: 07/25/2023]
Abstract
Flexible electrodes have demonstrated better biocompatibility than rigid electrodes in relieving tissue encapsulation and long-term recording. Nonhuman primates are closer to humans in their brains' structural and functional properties, thus making them more suitable than rodents as animal models for potential clinical usage. However, the application of flexible electrodes on nonhuman primates has rarely been reported. In the present study, a flexible multichannel electrode array for nonhuman primates was developed and implemented for extracellular recording in behaving monkeys. To minimize the window of durotomy for reducing possible risks, a guide-tube-compatible implantation solution was designed to deliver the flexible electrodes through the dura into the cortex. The proposed structure for inserting flexible electrodes was characterized ex vivo and validated in vivo. Furthermore, acute recording of multichannel flexible electrodes for the primates was performed. The results showed that the flexible electrodes and implantation method used in this study meet the needs of extracellular recording in nonhuman primates. Task-related neuronal activities with a high signal-to-noise ratio of spikes demonstrated that our whole device is currently a minimally invasive and clinically viable approach for extracellular recording.
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Affiliation(s)
- Yang Wang
- State Key Laboratory of Integrated Optoelectronics, Institute of Semiconductors, Chinese Academy of Sciences, Beijing, 100083 China
- University of Chinese Academy of Sciences, Beijing, 101408 China
| | - Qifan Wang
- University of Chinese Academy of Sciences, Beijing, 101408 China
- Institute of Neuroscience, Key Laboratory of Primate Neurobiology, CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, 200031 China
- Chinese Institute for Brain Research, Beijing, 102206 China
| | - Ruichen Zheng
- University of Chinese Academy of Sciences, Beijing, 101408 China
- Institute of Neuroscience, Key Laboratory of Primate Neurobiology, CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, 200031 China
| | - Xinxiu Xu
- Institute of Neuroscience, Key Laboratory of Primate Neurobiology, CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, 200031 China
- Chinese Institute for Brain Research, Beijing, 102206 China
| | - Xinze Yang
- State Key Laboratory of Integrated Optoelectronics, Institute of Semiconductors, Chinese Academy of Sciences, Beijing, 100083 China
- University of Chinese Academy of Sciences, Beijing, 101408 China
| | - Qiang Gui
- State Key Laboratory of Integrated Optoelectronics, Institute of Semiconductors, Chinese Academy of Sciences, Beijing, 100083 China
| | - Xiaowei Yang
- State Key Laboratory of Integrated Optoelectronics, Institute of Semiconductors, Chinese Academy of Sciences, Beijing, 100083 China
| | - Yijun Wang
- State Key Laboratory of Integrated Optoelectronics, Institute of Semiconductors, Chinese Academy of Sciences, Beijing, 100083 China
- University of Chinese Academy of Sciences, Beijing, 101408 China
- Chinese Institute for Brain Research, Beijing, 102206 China
| | - He Cui
- University of Chinese Academy of Sciences, Beijing, 101408 China
- Institute of Neuroscience, Key Laboratory of Primate Neurobiology, CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, 200031 China
- Chinese Institute for Brain Research, Beijing, 102206 China
| | - Weihua Pei
- State Key Laboratory of Integrated Optoelectronics, Institute of Semiconductors, Chinese Academy of Sciences, Beijing, 100083 China
- University of Chinese Academy of Sciences, Beijing, 101408 China
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Autonomous grasping of 3-D objects by a vision-actuated robot arm using Brain–Computer Interface. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2023.104765] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/09/2023]
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23
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Wan Z, Liu T, Ran X, Liu P, Chen W, Zhang S. The influence of non-stationarity of spike signals on decoding performance in intracortical brain-computer interface: a simulation study. Front Comput Neurosci 2023; 17:1135783. [PMID: 37251598 PMCID: PMC10213332 DOI: 10.3389/fncom.2023.1135783] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/01/2023] [Accepted: 04/28/2023] [Indexed: 05/31/2023] Open
Abstract
Introduction Intracortical Brain-Computer Interfaces (iBCI) establish a new pathway to restore motor functions in individuals with paralysis by interfacing directly with the brain to translate movement intention into action. However, the development of iBCI applications is hindered by the non-stationarity of neural signals induced by the recording degradation and neuronal property variance. Many iBCI decoders were developed to overcome this non-stationarity, but its effect on decoding performance remains largely unknown, posing a critical challenge for the practical application of iBCI. Methods To improve our understanding on the effect of non-stationarity, we conducted a 2D-cursor simulation study to examine the influence of various types of non-stationarities. Concentrating on spike signal changes in chronic intracortical recording, we used the following three metrics to simulate the non-stationarity: mean firing rate (MFR), number of isolated units (NIU), and neural preferred directions (PDs). MFR and NIU were decreased to simulate the recording degradation while PDs were changed to simulate the neuronal property variance. Performance evaluation based on simulation data was then conducted on three decoders and two different training schemes. Optimal Linear Estimation (OLE), Kalman Filter (KF), and Recurrent Neural Network (RNN) were implemented as decoders and trained using static and retrained schemes. Results In our evaluation, RNN decoder and retrained scheme showed consistent better performance under small recording degradation. However, the serious signal degradation would cause significant performance to drop eventually. On the other hand, RNN performs significantly better than the other two decoders in decoding simulated non-stationary spike signals, and the retrained scheme maintains the decoders' high performance when changes are limited to PDs. Discussion Our simulation work demonstrates the effects of neural signal non-stationarity on decoding performance and serves as a reference for selecting decoders and training schemes in chronic iBCI. Our result suggests that comparing to KF and OLE, RNN has better or equivalent performance using both training schemes. Performance of decoders under static scheme is influenced by recording degradation and neuronal property variation while decoders under retrained scheme are only influenced by the former one.
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Affiliation(s)
- Zijun Wan
- Key Laboratory of Biomedical Engineering of Education Ministry, Zhejiang Provincial Key Laboratory of Cardio-Cerebral Vascular Detection Technology and Medicinal Effectiveness Appraisal, Department of Biomedical Engineering, School of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, China
- Qiushi Academy for Advanced Studies, Zhejiang University, Hangzhou, China
| | - Tengjun Liu
- Key Laboratory of Biomedical Engineering of Education Ministry, Zhejiang Provincial Key Laboratory of Cardio-Cerebral Vascular Detection Technology and Medicinal Effectiveness Appraisal, Department of Biomedical Engineering, School of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, China
- Qiushi Academy for Advanced Studies, Zhejiang University, Hangzhou, China
| | - Xingchen Ran
- Key Laboratory of Biomedical Engineering of Education Ministry, Zhejiang Provincial Key Laboratory of Cardio-Cerebral Vascular Detection Technology and Medicinal Effectiveness Appraisal, Department of Biomedical Engineering, School of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, China
- Qiushi Academy for Advanced Studies, Zhejiang University, Hangzhou, China
| | - Pengfu Liu
- Key Laboratory of Biomedical Engineering of Education Ministry, Zhejiang Provincial Key Laboratory of Cardio-Cerebral Vascular Detection Technology and Medicinal Effectiveness Appraisal, Department of Biomedical Engineering, School of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, China
- Qiushi Academy for Advanced Studies, Zhejiang University, Hangzhou, China
| | - Weidong Chen
- Key Laboratory of Biomedical Engineering of Education Ministry, Zhejiang Provincial Key Laboratory of Cardio-Cerebral Vascular Detection Technology and Medicinal Effectiveness Appraisal, Department of Biomedical Engineering, School of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, China
- Qiushi Academy for Advanced Studies, Zhejiang University, Hangzhou, China
| | - Shaomin Zhang
- Key Laboratory of Biomedical Engineering of Education Ministry, Zhejiang Provincial Key Laboratory of Cardio-Cerebral Vascular Detection Technology and Medicinal Effectiveness Appraisal, Department of Biomedical Engineering, School of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, China
- Qiushi Academy for Advanced Studies, Zhejiang University, Hangzhou, China
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Deo DR, Willett FR, Avansino DT, Hochberg LR, Henderson JM, Shenoy KV. Translating deep learning to neuroprosthetic control. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.04.21.537581. [PMID: 37131830 PMCID: PMC10153231 DOI: 10.1101/2023.04.21.537581] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
Advances in deep learning have given rise to neural network models of the relationship between movement and brain activity that appear to far outperform prior approaches. Brain-computer interfaces (BCIs) that enable people with paralysis to control external devices, such as robotic arms or computer cursors, might stand to benefit greatly from these advances. We tested recurrent neural networks (RNNs) on a challenging nonlinear BCI problem: decoding continuous bimanual movement of two computer cursors. Surprisingly, we found that although RNNs appeared to perform well in offline settings, they did so by overfitting to the temporal structure of the training data and failed to generalize to real-time neuroprosthetic control. In response, we developed a method that alters the temporal structure of the training data by dilating/compressing it in time and re-ordering it, which we show helps RNNs successfully generalize to the online setting. With this method, we demonstrate that a person with paralysis can control two computer cursors simultaneously, far outperforming standard linear methods. Our results provide evidence that preventing models from overfitting to temporal structure in training data may, in principle, aid in translating deep learning advances to the BCI setting, unlocking improved performance for challenging applications.
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Wen S, Yin A, Furlanello T, Perich MG, Miller LE, Itti L. Rapid adaptation of brain-computer interfaces to new neuronal ensembles or participants via generative modelling. Nat Biomed Eng 2023; 7:546-558. [PMID: 34795394 PMCID: PMC9114171 DOI: 10.1038/s41551-021-00811-z] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2020] [Accepted: 09/17/2021] [Indexed: 11/09/2022]
Abstract
For brain-computer interfaces (BCIs), obtaining sufficient training data for algorithms that map neural signals onto actions can be difficult, expensive or even impossible. Here we report the development and use of a generative model-a model that synthesizes a virtually unlimited number of new data distributions from a learned data distribution-that learns mappings between hand kinematics and the associated neural spike trains. The generative spike-train synthesizer is trained on data from one recording session with a monkey performing a reaching task and can be rapidly adapted to new sessions or monkeys by using limited additional neural data. We show that the model can be adapted to synthesize new spike trains, accelerating the training and improving the generalization of BCI decoders. The approach is fully data-driven, and hence, applicable to applications of BCIs beyond motor control.
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Affiliation(s)
- Shixian Wen
- University of Southern California, Los Angeles, CA, USA.
| | | | | | - M G Perich
- University of Geneva, Geneva, Switzerland
| | - L E Miller
- Northwestern University, Chicago, IL, USA
| | - Laurent Itti
- University of Southern California, Los Angeles, CA, USA.
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26
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Zhao ZP, Nie C, Jiang CT, Cao SH, Tian KX, Yu S, Gu JW. Modulating Brain Activity with Invasive Brain-Computer Interface: A Narrative Review. Brain Sci 2023; 13:brainsci13010134. [PMID: 36672115 PMCID: PMC9856340 DOI: 10.3390/brainsci13010134] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2022] [Revised: 12/17/2022] [Accepted: 01/05/2023] [Indexed: 01/15/2023] Open
Abstract
Brain-computer interface (BCI) can be used as a real-time bidirectional information gateway between the brain and machines. In particular, rapid progress in invasive BCI, propelled by recent developments in electrode materials, miniature and power-efficient electronics, and neural signal decoding technologies has attracted wide attention. In this review, we first introduce the concepts of neuronal signal decoding and encoding that are fundamental for information exchanges in BCI. Then, we review the history and recent advances in invasive BCI, particularly through studies using neural signals for controlling external devices on one hand, and modulating brain activity on the other hand. Specifically, regarding modulating brain activity, we focus on two types of techniques, applying electrical stimulation to cortical and deep brain tissues, respectively. Finally, we discuss the related ethical issues concerning the clinical application of this emerging technology.
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Affiliation(s)
- Zhi-Ping Zhao
- School of Biological Science and Medical Engineering, Beihang University, Beijing 100191, China
| | - Chuang Nie
- Strategic Support Force Medical Center, Beijing 100101, China
| | - Cheng-Teng Jiang
- Savaid Medical School, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Sheng-Hao Cao
- Brainnetome Center and National Laboratory of Pattern Recognition, Chinese Academy of Sciences, Beijing 100190, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Kai-Xi Tian
- Brainnetome Center and National Laboratory of Pattern Recognition, Chinese Academy of Sciences, Beijing 100190, China
- School of Future Technology, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Shan Yu
- Brainnetome Center and National Laboratory of Pattern Recognition, Chinese Academy of Sciences, Beijing 100190, China
- School of Future Technology, University of Chinese Academy of Sciences, Beijing 100049, China
- Correspondence: (S.Y.); (J.-W.G.); Tel.: +86-010-8254-4786 (S.Y.); +86-010-6635-6729 (J.-W.G.)
| | - Jian-Wen Gu
- School of Biological Science and Medical Engineering, Beihang University, Beijing 100191, China
- Strategic Support Force Medical Center, Beijing 100101, China
- Correspondence: (S.Y.); (J.-W.G.); Tel.: +86-010-8254-4786 (S.Y.); +86-010-6635-6729 (J.-W.G.)
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Gupta A, Vardalakis N, Wagner FB. Neuroprosthetics: from sensorimotor to cognitive disorders. Commun Biol 2023; 6:14. [PMID: 36609559 PMCID: PMC9823108 DOI: 10.1038/s42003-022-04390-w] [Citation(s) in RCA: 15] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2022] [Accepted: 12/19/2022] [Indexed: 01/07/2023] Open
Abstract
Neuroprosthetics is a multidisciplinary field at the interface between neurosciences and biomedical engineering, which aims at replacing or modulating parts of the nervous system that get disrupted in neurological disorders or after injury. Although neuroprostheses have steadily evolved over the past 60 years in the field of sensory and motor disorders, their application to higher-order cognitive functions is still at a relatively preliminary stage. Nevertheless, a recent series of proof-of-concept studies suggest that electrical neuromodulation strategies might also be useful in alleviating some cognitive and memory deficits, in particular in the context of dementia. Here, we review the evolution of neuroprosthetics from sensorimotor to cognitive disorders, highlighting important common principles such as the need for neuroprosthetic systems that enable multisite bidirectional interactions with the nervous system.
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Affiliation(s)
- Ankur Gupta
- grid.462010.1Univ. Bordeaux, CNRS, IMN, UMR 5293, F-33000 Bordeaux, France
| | | | - Fabien B. Wagner
- grid.462010.1Univ. Bordeaux, CNRS, IMN, UMR 5293, F-33000 Bordeaux, France
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28
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Priorelli M, Stoianov IP. Flexible intentions: An Active Inference theory. Front Comput Neurosci 2023; 17:1128694. [PMID: 37021085 PMCID: PMC10067605 DOI: 10.3389/fncom.2023.1128694] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2022] [Accepted: 03/03/2023] [Indexed: 04/07/2023] Open
Abstract
We present a normative computational theory of how the brain may support visually-guided goal-directed actions in dynamically changing environments. It extends the Active Inference theory of cortical processing according to which the brain maintains beliefs over the environmental state, and motor control signals try to fulfill the corresponding sensory predictions. We propose that the neural circuitry in the Posterior Parietal Cortex (PPC) compute flexible intentions-or motor plans from a belief over targets-to dynamically generate goal-directed actions, and we develop a computational formalization of this process. A proof-of-concept agent embodying visual and proprioceptive sensors and an actuated upper limb was tested on target-reaching tasks. The agent behaved correctly under various conditions, including static and dynamic targets, different sensory feedbacks, sensory precisions, intention gains, and movement policies; limit conditions were individuated, too. Active Inference driven by dynamic and flexible intentions can thus support goal-directed behavior in constantly changing environments, and the PPC might putatively host its core intention mechanism. More broadly, the study provides a normative computational basis for research on goal-directed behavior in end-to-end settings and further advances mechanistic theories of active biological systems.
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29
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Bibliometric analysis on Brain-computer interfaces in a 30-year period. APPL INTELL 2022. [DOI: 10.1007/s10489-022-04226-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/03/2022]
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30
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Cometa A, Falasconi A, Biasizzo M, Carpaneto J, Horn A, Mazzoni A, Micera S. Clinical neuroscience and neurotechnology: An amazing symbiosis. iScience 2022; 25:105124. [PMID: 36193050 PMCID: PMC9526189 DOI: 10.1016/j.isci.2022.105124] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
In the last decades, clinical neuroscience found a novel ally in neurotechnologies, devices able to record and stimulate electrical activity in the nervous system. These technologies improved the ability to diagnose and treat neural disorders. Neurotechnologies are concurrently enabling a deeper understanding of healthy and pathological dynamics of the nervous system through stimulation and recordings during brain implants. On the other hand, clinical neurosciences are not only driving neuroengineering toward the most relevant clinical issues, but are also shaping the neurotechnologies thanks to clinical advancements. For instance, understanding the etiology of a disease informs the location of a therapeutic stimulation, but also the way stimulation patterns should be designed to be more effective/naturalistic. Here, we describe cases of fruitful integration such as Deep Brain Stimulation and cortical interfaces to highlight how this symbiosis between clinical neuroscience and neurotechnology is closer to a novel integrated framework than to a simple interdisciplinary interaction.
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Affiliation(s)
- Andrea Cometa
- The Biorobotics Institute, Scuola Superiore Sant’Anna, 56127 Pisa, Italy
- Department of Excellence in Robotics and AI, Scuola Superiore Sant'Anna, 56127 Pisa, Italy
| | - Antonio Falasconi
- Friedrich Miescher Institute for Biomedical Research, 4058 Basel, Switzerland
- Biozentrum, University of Basel, 4056 Basel, Switzerland
| | - Marco Biasizzo
- The Biorobotics Institute, Scuola Superiore Sant’Anna, 56127 Pisa, Italy
- Department of Excellence in Robotics and AI, Scuola Superiore Sant'Anna, 56127 Pisa, Italy
| | - Jacopo Carpaneto
- The Biorobotics Institute, Scuola Superiore Sant’Anna, 56127 Pisa, Italy
- Department of Excellence in Robotics and AI, Scuola Superiore Sant'Anna, 56127 Pisa, Italy
| | - Andreas Horn
- Center for Brain Circuit Therapeutics Department of Neurology Brigham & Women’s Hospital, Harvard Medical School, Boston, MA 02115, USA
- MGH Neurosurgery & Center for Neurotechnology and Neurorecovery (CNTR) at MGH Neurology Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA
- Movement Disorder and Neuromodulation Unit, Department of Neurology, Charité – Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt- Universität zu Berlin, Department of Neurology, 10117 Berlin, Germany
| | - Alberto Mazzoni
- The Biorobotics Institute, Scuola Superiore Sant’Anna, 56127 Pisa, Italy
- Department of Excellence in Robotics and AI, Scuola Superiore Sant'Anna, 56127 Pisa, Italy
| | - Silvestro Micera
- The Biorobotics Institute, Scuola Superiore Sant’Anna, 56127 Pisa, Italy
- Department of Excellence in Robotics and AI, Scuola Superiore Sant'Anna, 56127 Pisa, Italy
- Translational Neural Engineering Lab, School of Engineering, École Polytechnique Fèdèrale de Lausanne, 1015 Lausanne, Switzerland
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Li L, Zhao S, Ran W, Li Z, Yan Y, Zhong B, Lou Z, Wang L, Shen G. Dual sensing signal decoupling based on tellurium anisotropy for VR interaction and neuro-reflex system application. Nat Commun 2022; 13:5975. [PMID: 36216925 PMCID: PMC9550802 DOI: 10.1038/s41467-022-33716-9] [Citation(s) in RCA: 23] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2022] [Accepted: 09/29/2022] [Indexed: 11/13/2022] Open
Abstract
Anisotropy control of the electronic structure in inorganic semiconductors is an important step in developing devices endowed with multi-function. Here, we demonstrate that the intrinsic anisotropy of tellurium nanowires can be used to modulate the electronic structure and piezoelectric polarization and decouple pressure and temperature difference signals, and realize VR interaction and neuro-reflex applications. The architecture design of the device combined with self-locking effect can eliminate dependence on displacement, enabling a single device to determine the hardness and thermal conductivity of materials through a simple touch. We used a bimodal Te-based sensor to develop a wearable glove for endowing real objects to the virtual world, which greatly improves VR somatosensory feedback. In addition, we successfully achieved stimulus recognition and neural-reflex in a rabbit sciatic nerve model by integrating the sensor signals using a deep learning technique. In view of in-/ex-vivo feasibility, the bimodal Te-based sensor would be considered a novel sensing platform for a wide range application of metaverse, AI robot, and electronic medicine.
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Affiliation(s)
- Linlin Li
- State Key Laboratory for Superlattices and Microstructures, Institute of Semiconductors, Chinese Academy of Sciences, Beijing, 100083, China
- Center of Materials Science and Optoelectronic Engineering, University of Chinese Academy of Sciences, Beijing, 100083, China
| | - Shufang Zhao
- State Key Laboratory for Superlattices and Microstructures, Institute of Semiconductors, Chinese Academy of Sciences, Beijing, 100083, China
- Center of Materials Science and Optoelectronic Engineering, University of Chinese Academy of Sciences, Beijing, 100083, China
| | - Wenhao Ran
- State Key Laboratory for Superlattices and Microstructures, Institute of Semiconductors, Chinese Academy of Sciences, Beijing, 100083, China
- Center of Materials Science and Optoelectronic Engineering, University of Chinese Academy of Sciences, Beijing, 100083, China
| | - Zhexin Li
- State Key Laboratory for Superlattices and Microstructures, Institute of Semiconductors, Chinese Academy of Sciences, Beijing, 100083, China
- Center of Materials Science and Optoelectronic Engineering, University of Chinese Academy of Sciences, Beijing, 100083, China
| | - Yongxu Yan
- State Key Laboratory for Superlattices and Microstructures, Institute of Semiconductors, Chinese Academy of Sciences, Beijing, 100083, China
- Center of Materials Science and Optoelectronic Engineering, University of Chinese Academy of Sciences, Beijing, 100083, China
| | - Bowen Zhong
- State Key Laboratory for Superlattices and Microstructures, Institute of Semiconductors, Chinese Academy of Sciences, Beijing, 100083, China
- Center of Materials Science and Optoelectronic Engineering, University of Chinese Academy of Sciences, Beijing, 100083, China
| | - Zheng Lou
- State Key Laboratory for Superlattices and Microstructures, Institute of Semiconductors, Chinese Academy of Sciences, Beijing, 100083, China
- Center of Materials Science and Optoelectronic Engineering, University of Chinese Academy of Sciences, Beijing, 100083, China
| | - Lili Wang
- State Key Laboratory for Superlattices and Microstructures, Institute of Semiconductors, Chinese Academy of Sciences, Beijing, 100083, China.
- Center of Materials Science and Optoelectronic Engineering, University of Chinese Academy of Sciences, Beijing, 100083, China.
| | - Guozhen Shen
- School of Integrated Circuits and Electronics, Beijing Institute of Technology, Beijing, 100081, China.
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Loriette C, Amengual JL, Ben Hamed S. Beyond the brain-computer interface: Decoding brain activity as a tool to understand neuronal mechanisms subtending cognition and behavior. Front Neurosci 2022; 16:811736. [PMID: 36161174 PMCID: PMC9492914 DOI: 10.3389/fnins.2022.811736] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2021] [Accepted: 08/23/2022] [Indexed: 11/13/2022] Open
Abstract
One of the major challenges in system neurosciences consists in developing techniques for estimating the cognitive information content in brain activity. This has an enormous potential in different domains spanning from clinical applications, cognitive enhancement to a better understanding of the neural bases of cognition. In this context, the inclusion of machine learning techniques to decode different aspects of human cognition and behavior and its use to develop brain-computer interfaces for applications in neuroprosthetics has supported a genuine revolution in the field. However, while these approaches have been shown quite successful for the study of the motor and sensory functions, success is still far from being reached when it comes to covert cognitive functions such as attention, motivation and decision making. While improvement in this field of BCIs is growing fast, a new research focus has emerged from the development of strategies for decoding neural activity. In this review, we aim at exploring how the advanced in decoding of brain activity is becoming a major neuroscience tool moving forward our understanding of brain functions, providing a robust theoretical framework to test predictions on the relationship between brain activity and cognition and behavior.
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Affiliation(s)
- Célia Loriette
- Institut des Sciences Cognitives Marc Jeannerod, CNRS UMR 5229, Université Claude Bernard Lyon 1, Bron, France
| | | | - Suliann Ben Hamed
- Institut des Sciences Cognitives Marc Jeannerod, CNRS UMR 5229, Université Claude Bernard Lyon 1, Bron, France
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Instant disembodiment of virtual body parts. Atten Percept Psychophys 2022; 84:2725-2740. [PMID: 36045312 PMCID: PMC9630226 DOI: 10.3758/s13414-022-02544-w] [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] [Accepted: 07/21/2022] [Indexed: 11/16/2022]
Abstract
Evidence from multisensory body illusions suggests that body representations may be malleable, for instance, by embodying external objects. However, adjusting body representations to current task demands also implies that external objects become disembodied from the body representation if they are no longer required. In the current web-based study, we induced the embodiment of a two-dimensional (2D) virtual hand that could be controlled by active movements of a computer mouse or on a touchpad. Following initial embodiment, we probed for disembodiment by comparing two conditions: Participants either continued moving the virtual hand or they stopped moving and kept the hand still. Based on theoretical accounts that conceptualize body representations as a set of multisensory bindings, we expected gradual disembodiment of the virtual hand if the body representations are no longer updated through correlated visuomotor signals. In contrast to our prediction, the virtual hand was instantly disembodied as soon as participants stopped moving it. This result was replicated in two follow-up experiments. The observed instantaneous disembodiment might suggest that humans are sensitive to the rapid changes that characterize action and body in virtual environments, and hence adjust corresponding body representations particularly swiftly.
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Grani F, Soto Sanchez C, Farfan FD, Alfaro A, Grima MD, Rodil Doblado A, Fernandez E. Time stability and connectivity analysis with an intracortical 96-channel microelectrode array inserted in human visual cortex. J Neural Eng 2022; 19. [PMID: 35817011 DOI: 10.1088/1741-2552/ac801d] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2022] [Accepted: 07/11/2022] [Indexed: 11/12/2022]
Abstract
OBJECTIVE Microstimulation via electrodes that penetrate the visual cortex creates visual perceptions called phosphenes. Besides providing electrical stimulation to induce perceptions, each electrode can be used to record the brain signals from the cortex region under the electrode which contains brain state information. Since the future visual prosthesis interfaces will be implanted chronically in the visual cortex of blind people, it is important to study the long-term stability of the signals acquired from the electrodes. Here, we studied the changes over time and the repercussions of electrical stimulation on the brain signals acquired with an intracortical 96-channel microelectrode array implanted in the visual cortex of a blind volunteer for 6 months. APPROACH We used variance, power spectral density, correlation, coherence, and phase coherence to study the brain signals acquired in resting condition before and after the administration of electrical stimulation during a period of 6 months. MAIN RESULTS Variance and power spectral density up to 750 Hz do not show any significant trend in the 6 months, but correlation coherence and phase coherence significantly decrease over the implantation time and increase after electrical stimulation. SIGNIFICANCE The stability of variance and power spectral density in time is important for long-term clinical applications based on the intracortical signals collected by the electrodes. The decreasing trends of correlation, coherence, and phase coherence might be related to plasticity changes in the visual cortex due to electrical microstimulation.
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Affiliation(s)
- Fabrizio Grani
- Universidad Miguel Hernandez de Elche, Avinguda de la Universitat d'Elx, Elche, 03206, SPAIN
| | - Cristina Soto Sanchez
- Universidad Miguel Hernandez de Elche, Avinguda de la Universitat d'Elx, Elche, 03206, SPAIN
| | - Fernando Daniel Farfan
- Departmento de Bioingenieria Fac de Ciencias Exactas y Technologia, Universidad Nacional de Tucuman, Av. Independencia 1800, San Miguel de Tucumán, Tucumán, 4000, ARGENTINA
| | - Arantxa Alfaro
- Institute of Bioengineering, Universidad Miguel Hernandez de Elche, Fac. Medicina, San Juan, Alicante , 03550, SPAIN
| | - Maria Dolores Grima
- Universidad Miguel Hernandez de Elche, Avinguda de la Universitat d'Elx, ELCHE, Elche, 03206, SPAIN
| | - Alfonso Rodil Doblado
- Universidad Miguel Hernandez de Elche, Avinguda de la Universitat d'Elx, Elche, 03206, SPAIN
| | - Eduardo Fernandez
- Institute of Bioengineering, Universidad Miguel Hernandez de Elche, Unidad de Neuroingeniería Biomédica, Avda de la Universidad s/n, Elche, ALicante, 03202, SPAIN
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Song Z, Zhang X, Wang Y. Cluster Kernel Reinforcement Learning-based Kalman Filter for Three-Lever Discrimination Task in Brain-Machine Interface. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2022; 2022:690-693. [PMID: 36086404 DOI: 10.1109/embc48229.2022.9871669] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Brain-Machine Interface (BMI) translates paralyzed people's neural activity into control commands of the prosthesis so that their lost motor functions could be restored. The neural activities represent brain states that change continuously over time which brings the challenge to the online decoder. Reinforcement Learning (RL) has the advantage to construct the dynamic neural-kinematic mapping during the interaction. However, existing RL decoders output discrete actions as a classification problem and cannot provide continuous estimation. Previous work has combined Kalman Filter (KF) with RL for BMI, which achieves a continuous motor state estimation. However, this method adopts a neural network structure, which might get stuck in local optimum and cannot provide an efficient online update for the neural-kinematic mapping. In this paper, we propose a Cluster Kernel Reinforcement Learning-based Kalman Filter (CKRL-based KF) to avoid the local optimum problem for online neural-kinematic updating. The neural patterns are projected into Reproducing Kernel Hilbert Space (RKHS), which builds a universal approximation to guarantee the global optimum. We compare our proposed algorithm with the existing method on rat data collected during a brain control three-lever discrimination task. Our preliminary results show that the proposed method has a higher trial accuracy with lower variance across data segments, which shows its potential to improve the performance for online BMI control. Clinical Relevance- This paper provides a more stable decoding method for adaptive and continuous neural decoding. It is promising for clinical applications in BMI.
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A hybrid autoencoder framework of dimensionality reduction for brain-computer interface decoding. Comput Biol Med 2022; 148:105871. [DOI: 10.1016/j.compbiomed.2022.105871] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2022] [Revised: 06/20/2022] [Accepted: 07/09/2022] [Indexed: 11/19/2022]
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Premchand B, Toe KK, Wang C, Libedinsky C, Ang KK, So RQ. Information sparseness in cortical microelectrode channels while decoding movement direction using an artificial neural network. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2022; 2022:3534-3537. [PMID: 36085749 DOI: 10.1109/embc48229.2022.9870896] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Implanted microelectrode arrays can directly pick up electrode signals from the primary motor cortex (M1) during movement, and brain-machine interfaces (BMIs) can decode these signals to predict the directions of contemporaneous movements. However, it is not well known how much each individual input is responsible for the overall performance of a BMI decoder. In this paper, we seek to quantify how much each channel contributes to an artificial neural network (ANN)-based decoder, by measuring how much the removal of each individual channel degrades the accuracy of the output. If information on movement direction was equally distributed among channels, then the removal of one would have a minimal effect on decoder accuracy. On the other hand, if that information was distributed sparsely, then the removal of specific information-rich channels would significantly lower decoder accuracy. We found that for most channels, their removal did not significantly affect decoder performance. However, for a subset of channels (16 out of 61), removing them significantly reduced the decoder accuracy. This suggests that information is not uniformly distributed among the recording channels. We propose examining these channels further to optimize BMIs more effectively, as well as understand how M1 functions at the neuronal level.
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Hasse BA, Sheets DEG, Holly NL, Gothard KM, Fuglevand AJ. Restoration of complex movement in the paralyzed upper limb. J Neural Eng 2022; 19. [PMID: 35728568 DOI: 10.1088/1741-2552/ac7ad7] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2022] [Accepted: 06/21/2022] [Indexed: 11/11/2022]
Abstract
OBJECTIVE Functional electrical stimulation (FES) involves artificial activation of skeletal muscles to reinstate motor function in paralyzed individuals. While FES applied to the upper limb has improved the ability of tetraplegics to perform activities of daily living, there are key shortcomings impeding its widespread use. One major limitation is that the range of motor behaviors that can be generated is restricted to a small set of simple, preprogrammed movements. This limitation stems from the substantial difficulty in determining the patterns of stimulation across many muscles required to produce more complex movements. Therefore, the objective of this study was to use machine learning to flexibly identify patterns of muscle stimulation needed to evoke a wide array of multi-joint arm movements. APPROACH Arm kinematics and electromyographic activity from 29 muscles were recorded while a 'trainer' monkey made an extensive range of arm movements. Those data were used to train an artificial neural network that predicted patterns of muscle activity associated with a new set of movements. Those patterns were converted into trains of stimulus pulses that were delivered to upper limb muscles in two other temporarily paralyzed monkeys. RESULTS Machine-learning based prediction of EMG was good for within-subject predictions but appreciably poorer for across-subject predictions. Evoked responses matched the desired movements with good fidelity only in some cases. Means to mitigate errors associated with FES-evoked movements are discussed. SIGNIFICANCE Because the range of movements that can be produced with our approach is virtually unlimited, this system could greatly expand the repertoire of movements available to individuals with high level paralysis.
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Affiliation(s)
- Brady A Hasse
- Department of Physiology, The University of Arizona College of Medicine Tucson, 1501 N Campbell Avenue, Tucson, Arizona, 85724-5051, UNITED STATES
| | - Drew E G Sheets
- Department of Organismal Biology & Anatomy, University of Chicago Biological Sciences Division, Anatomy, 1027 E 57th Street Chicago, IL 60637, Chicago, Illinois, 60637-5416, UNITED STATES
| | - Nicole L Holly
- Physiology, The University of Arizona College of Medicine Tucson, 1501 N Campbell Avenue, Tucson, Arizona, 85724-5051, UNITED STATES
| | - Katalin M Gothard
- Physiology, The University of Arizona College of Medicine Tucson, 1501 N Campbell Ave, Tucson, Arizona, 85724-5051, UNITED STATES
| | - Andrew J Fuglevand
- Department of Physiology, University of Arizona, Arizona Health Sciences Center, 1501 N. Campbell Ave, Tucson, Arizona, 85724-5051, UNITED STATES
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Liu D, Xu X, Li D, Li J, Yu X, Ling Z, Hong B. Intracranial brain-computer interface spelling using localized visual motion response. Neuroimage 2022; 258:119363. [PMID: 35688315 DOI: 10.1016/j.neuroimage.2022.119363] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2022] [Revised: 06/03/2022] [Accepted: 06/06/2022] [Indexed: 11/30/2022] Open
Abstract
Intracranial brain-computer interfaces (BCIs) can assist severely disabled persons in text communication and environmental control with high precision and speed. Nevertheless, sustainable BCI implants require minimal invasiveness. One of the implantation strategies is to adopt localized and robust cortical activities to drive BCI communication and to make a precise presurgical planning. The visual motion response is a good candidate for inclusion in this strategy because of its focal activity over the middle temporal visual area (MT). Here, we developed an intracranial BCI for spelling, utilizing only three electrodes over the MT area. The best recording electrodes were decided by preoperative functional magnetic resonance imaging (MRI) localization of the MT, and local neural activities were further enhanced by differential rereferencing of these electrodes. The BCI spelling system was validated both offline and online by five epilepsy patients, achieving the fastest speed of 62 bits/min, i.e., 12 characters/min. Moreover, the response patterns of dual-directional visual motion stimuli provided an additional dimension of BCI target encoding and paved the way for a higher information transfer rate of intracranial BCI spelling.
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Affiliation(s)
- Dingkun Liu
- Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, Beijing, 100084, China
| | - Xin Xu
- Department of Neurosurgery, Chinese PLA General Hospital, Beijing, Beijing, 100853, China
| | - Dongyang Li
- Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, Beijing, 100084, China
| | - Jie Li
- Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, Beijing, 100084, China
| | - Xinguang Yu
- Department of Neurosurgery, Chinese PLA General Hospital, Beijing, Beijing, 100853, China
| | - Zhipei Ling
- Department of Neurosurgery, Chinese PLA General Hospital, Beijing, Beijing, 100853, China
| | - Bo Hong
- Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, Beijing, 100084, China; McGovern Institute for Brain Research, Tsinghua University, Beijing, Beijing, 100084, China.
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Lim T, Kim M, Akbarian A, Kim J, Tresco PA, Zhang H. Conductive Polymer Enabled Biostable Liquid Metal Electrodes for Bioelectronic Applications. Adv Healthc Mater 2022; 11:e2102382. [PMID: 35112800 DOI: 10.1002/adhm.202102382] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2021] [Revised: 01/14/2022] [Indexed: 12/11/2022]
Abstract
Gallium (Ga)-based liquid metal materials have emerged as a promising material platform for soft bioelectronics. Unfortunately, Ga has limited biostability and electrochemical performance under physiological conditions, which can hinder the implementation of its use in bioelectronic devices. Here, an effective conductive polymer deposition strategy on the liquid metal surface to improve the biostability and electrochemical performance of Ga-based liquid metals for use under physiological conditions is demonstrated. The conductive polymer [poly(3,4-ethylene dioxythiophene):tetrafluoroborate]-modified liquid metal surface significantly outperforms the liquid metal.based electrode in mechanical, biological, and electrochemical studies. In vivo action potential recordings in behaving nonhuman primate and invertebrate models demonstrate the feasibility of using liquid metal electrodes for high-performance neural recording applications. This is the first demonstration of single-unit neural recording using Ga-based liquid metal bioelectronic devices to date. The results determine that the electrochemical deposition of conductive polymer over liquid metal can improve the material properties of liquid metal electrodes for use under physiological conditions and open numerous design opportunities for next-generation liquid metal-based bioelectronics.
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Affiliation(s)
- Taehwan Lim
- Department of Chemical Engineering University of Utah Salt Lake City Utah 84112 USA
| | - Minju Kim
- Department of Mechanical Engineering University of Utah Salt Lake City Utah 84112 USA
| | - Amir Akbarian
- Department of Ophthalmology and Visual Science University of Utah Salt Lake City Utah 84112 USA
| | - Jungkyu Kim
- Department of Mechanical Engineering University of Utah Salt Lake City Utah 84112 USA
| | - Patrick A. Tresco
- Department of Biomedical Engineering University of Utah Salt Lake City Utah 84112 USA
| | - Huanan Zhang
- Department of Chemical Engineering University of Utah Salt Lake City Utah 84112 USA
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41
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An H, Nason-Tomaszewski SR, Lim J, Kwon K, Willsey MS, Patil PG, Kim HS, Sylvester D, Chestek CA, Blaauw D. A Power-Efficient Brain-Machine Interface System With a Sub-mw Feature Extraction and Decoding ASIC Demonstrated in Nonhuman Primates. IEEE TRANSACTIONS ON BIOMEDICAL CIRCUITS AND SYSTEMS 2022; 16:395-408. [PMID: 35594208 PMCID: PMC9375520 DOI: 10.1109/tbcas.2022.3175926] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Intracortical brain-machine interfaces have shown promise for restoring function to people with paralysis, but their translation to portable and implantable devices is hindered by their high power consumption. Recent devices have drastically reduced power consumption compared to standard experimental brain-machine interfaces, but still require wired or wireless connections to computing hardware for feature extraction and inference. Here, we introduce a Neural Recording And Decoding (NeuRAD) application specific integrated circuit (ASIC) in 180 nm CMOS that can extract neural spiking features and predict two-dimensional behaviors in real-time. To reduce amplifier and feature extraction power consumption, the NeuRAD has a hardware accelerator for extracting spiking band power (SBP) from intracortical spiking signals and includes an M0 processor with a fixed-point Matrix Acceleration Unit (MAU) for efficient and flexible decoding. We validated device functionality by recording SBP from a nonhuman primate implanted with a Utah microelectrode array and predicting the one- and two-dimensional finger movements the monkey was attempting to execute in closed-loop using a steady-state Kalman filter (SSKF). Using the NeuRAD's real-time predictions, the monkey achieved 100% success rate and 0.82 s mean target acquisition time to control one-dimensional finger movements using just 581 μW. To predict two-dimensional finger movements, the NeuRAD consumed 588 μW to enable the monkey to achieve a 96% success rate and 2.4 s mean acquisition time. By employing SBP, ASIC brain-machine interfaces can close the gap to enable fully implantable therapies for people with paralysis.
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Pulferer HS, Ásgeirsdóttir B, Mondini V, Sburlea AI, Müller-Putz GR. Continuous 2D trajectory decoding from attempted movement: across-session performance in able-bodied and feasibility in a spinal cord injured participant. J Neural Eng 2022; 19. [PMID: 35443233 DOI: 10.1088/1741-2552/ac689f] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2021] [Accepted: 04/19/2022] [Indexed: 11/11/2022]
Abstract
OBJECTIVE In people with a cervical spinal cord injury (SCI) or degenerative diseases leading to limited motor function, restoration of upper limb movement has been a goal of the brain-computer interface (BCI) field for decades. Recently, research from our group investigated non-invasive and real-time decoding of continuous movement in able-bodied participants from low-frequency brain signals during a target-tracking task. To advance our setup towards motor-impaired end users, we consequently chose a new paradigm based on attempted movement. APPROACH Here, we present the results of two studies. During the first study, data of ten able-bodied participants completing a target-tracking/shape-tracing task on-screen were investigated in terms of improvements in decoding performance due to user training. In a second study, a spinal cord injured participant underwent the same tasks. To investigate the merit of employing attempted movement in end users with SCI, data of the spinal cord injured participant were recorded twice; once within an observation only condition, and once while simultaneously attempting movement. MAIN RESULTS We observed mean correlation well above chance level for continuous motor decoding based on attempted movement in able-bodied participants. No global improvement over three sessions, both in sensor and source space, could be observed across all participants and movement parameters. In the participant with SCI, decoding performance well above chance was found. SIGNIFICANCE No presence of a learning effect in continuous attempted movement decoding in able-bodied participants could be observed. In contrast, non-significantly varying decoding patterns may promote the use of source space decoding in terms of generalized decoders utilizing transfer learning. Furthermore, above-chance correlations for attempted movement decoding ranging between those of observation only and executed movement were seen in one spinal cord injured participant, suggesting attempted movement decoding as a possible link between feasibility studies in able-bodied and actual applications in motor impaired end users.
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Affiliation(s)
| | | | - Valeria Mondini
- Institute of Neural Engineering, Graz University of Technology, Stremayrgasse 16/IV, Graz, 8010, AUSTRIA
| | - Andreea Ioana Sburlea
- Institute of Neural Engineering, Technische Universitat Graz, Stremayrgasse 16/IV, 8010 Graz, Austria, Graz, 8010, AUSTRIA
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Zhao Z, Spyropoulos GD, Cea C, Gelinas JN, Khodagholy D. Ionic communication for implantable bioelectronics. SCIENCE ADVANCES 2022; 8:eabm7851. [PMID: 35385298 PMCID: PMC8985921 DOI: 10.1126/sciadv.abm7851] [Citation(s) in RCA: 22] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/11/2021] [Accepted: 02/14/2022] [Indexed: 05/22/2023]
Abstract
Implanted bioelectronic devices require data transmission through tissue, but ionic conductivity and inhomogeneity of this medium complicate conventional communication approaches. Here, we introduce ionic communication (IC) that uses ions to effectively propagate megahertz-range signals. We demonstrate that IC operates by generating and sensing electrical potential energy within polarizable media. IC was tuned to transmit across a range of biologically relevant tissue depths. The radius of propagation was controlled to enable multiline parallel communication, and it did not interfere with concurrent use of other bioelectronics. We created a fully implantable IC-based neural interface device that acquired and noninvasively transmitted neurophysiologic data from freely moving rodents over a period of weeks with stability sufficient for isolation of action potentials from individual neurons. IC is a biologically based data communication that establishes long-term, high-fidelity interactions across intact tissue.
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Affiliation(s)
- Zifang Zhao
- Department of Electrical Engineering, Columbia University, New York, NY 10027, USA
| | | | - Claudia Cea
- Department of Electrical Engineering, Columbia University, New York, NY 10027, USA
| | - Jennifer N. Gelinas
- Department of Neurology, Columbia University Medical Center, New York, NY 10032, USA
- Institute for Genomic Medicine, Columbia University Medical Center, New York, NY 10032, USA
| | - Dion Khodagholy
- Department of Electrical Engineering, Columbia University, New York, NY 10027, USA
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Pandarinath C, Bensmaia SJ. The science and engineering behind sensitized brain-controlled bionic hands. Physiol Rev 2022; 102:551-604. [PMID: 34541898 PMCID: PMC8742729 DOI: 10.1152/physrev.00034.2020] [Citation(s) in RCA: 31] [Impact Index Per Article: 15.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2020] [Revised: 09/07/2021] [Accepted: 09/13/2021] [Indexed: 12/13/2022] Open
Abstract
Advances in our understanding of brain function, along with the development of neural interfaces that allow for the monitoring and activation of neurons, have paved the way for brain-machine interfaces (BMIs), which harness neural signals to reanimate the limbs via electrical activation of the muscles or to control extracorporeal devices, thereby bypassing the muscles and senses altogether. BMIs consist of reading out motor intent from the neuronal responses monitored in motor regions of the brain and executing intended movements with bionic limbs, reanimated limbs, or exoskeletons. BMIs also allow for the restoration of the sense of touch by electrically activating neurons in somatosensory regions of the brain, thereby evoking vivid tactile sensations and conveying feedback about object interactions. In this review, we discuss the neural mechanisms of motor control and somatosensation in able-bodied individuals and describe approaches to use neuronal responses as control signals for movement restoration and to activate residual sensory pathways to restore touch. Although the focus of the review is on intracortical approaches, we also describe alternative signal sources for control and noninvasive strategies for sensory restoration.
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Affiliation(s)
- Chethan Pandarinath
- Department of Biomedical Engineering, Emory University and Georgia Institute of Technology, Atlanta, Georgia
- Department of Neurosurgery, Emory University, Atlanta, Georgia
| | - Sliman J Bensmaia
- Department of Organismal Biology and Anatomy, University of Chicago, Chicago, Illinois
- Committee on Computational Neuroscience, University of Chicago, Chicago, Illinois
- Grossman Institute for Neuroscience, Quantitative Biology, and Human Behavior, University of Chicago, Chicago, Illinois
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Mendez Guerra I, Barsakcioglu DY, Vujaklija I, Wetmore DZ, Farina D. Far-field electric potentials provide access to the output from the spinal cord from wrist-mounted sensors. J Neural Eng 2022; 19. [PMID: 35303732 DOI: 10.1088/1741-2552/ac5f1a] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2021] [Accepted: 03/18/2022] [Indexed: 11/11/2022]
Abstract
OBJECTIVE Neural interfaces need to become more unobtrusive and socially acceptable to appeal to general consumers outside rehabilitation settings. APPROACH We developed a non-invasive neural interface that provides access to spinal motor neuron activities from the wrist, which is the preferred location for a wearable. The interface decodes far-field potentials present at the tendon endings of the forearm muscles using blind source separation. First, we evaluated the reliability of the interface to detect motor neuron firings based on far-field potentials, and thereafter we used the decoded motor neuron activity for the prediction of finger contractions in offline and real-time conditions. MAIN RESULTS The results showed that motor neuron activity decoded from the far-field potentials at the wrist accurately predicted individual and combined finger commands and therefore allowed for highly accurate real-time task classification. SIGNIFICANCE These findings demonstrate the feasibility of a non-invasive, neural interface at the wrist for precise real-time control based on the output of the spinal cord.
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Affiliation(s)
- Irene Mendez Guerra
- Department of Bioengineering, Imperial College London, 80 Wood Lane, London, W12 7TA, UNITED KINGDOM OF GREAT BRITAIN AND NORTHERN IRELAND
| | - Deren Yusuf Barsakcioglu
- Department of Bioengineering, Imperial College London, 80 Wood Lane, London, W12 7TA, UNITED KINGDOM OF GREAT BRITAIN AND NORTHERN IRELAND
| | - Ivan Vujaklija
- Department of Electrical Engineering and Automation, Aalto-yliopisto, Otakaari 3 (F306), Espoo, 00076, FINLAND
| | - Daniel Z Wetmore
- Meta Inc, 770 Broadway, New York City, New York, 10003, UNITED STATES
| | - Dario Farina
- Department of Bioengineering, Imperial College London, 80 Wood Lane, London, W12 7TA, UNITED KINGDOM OF GREAT BRITAIN AND NORTHERN IRELAND
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Wang T, Chen Y, Cui H. From Parametric Representation to Dynamical System: Shifting Views of the Motor Cortex in Motor Control. Neurosci Bull 2022; 38:796-808. [PMID: 35298779 PMCID: PMC9276910 DOI: 10.1007/s12264-022-00832-x] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2021] [Accepted: 11/29/2021] [Indexed: 11/01/2022] Open
Abstract
In contrast to traditional representational perspectives in which the motor cortex is involved in motor control via neuronal preference for kinetics and kinematics, a dynamical system perspective emerging in the last decade views the motor cortex as a dynamical machine that generates motor commands by autonomous temporal evolution. In this review, we first look back at the history of the representational and dynamical perspectives and discuss their explanatory power and controversy from both empirical and computational points of view. Here, we aim to reconcile the above perspectives, and evaluate their theoretical impact, future direction, and potential applications in brain-machine interfaces.
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Affiliation(s)
- Tianwei Wang
- Center for Excellence in Brain Science and Intelligent Technology, Institute of Neuroscience, Chinese Academy of Sciences, Shanghai, 200031, China.,Shanghai Center for Brain and Brain-inspired Intelligence Technology, Shanghai, 200031, China.,University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Yun Chen
- Center for Excellence in Brain Science and Intelligent Technology, Institute of Neuroscience, Chinese Academy of Sciences, Shanghai, 200031, China.,Shanghai Center for Brain and Brain-inspired Intelligence Technology, Shanghai, 200031, China.,University of Chinese Academy of Sciences, Beijing, 100049, China
| | - He Cui
- Center for Excellence in Brain Science and Intelligent Technology, Institute of Neuroscience, Chinese Academy of Sciences, Shanghai, 200031, China. .,Shanghai Center for Brain and Brain-inspired Intelligence Technology, Shanghai, 200031, China. .,University of Chinese Academy of Sciences, Beijing, 100049, China.
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47
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Zaidi SMT, Kocatürk S, Baykaş T, Kocatürk M. A behavioral paradigm for cortical control of a robotic actuator by freely moving rats in a one-dimensional two-target reaching task. J Neurosci Methods 2022; 373:109555. [PMID: 35271875 DOI: 10.1016/j.jneumeth.2022.109555] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2021] [Revised: 02/01/2022] [Accepted: 03/04/2022] [Indexed: 10/18/2022]
Abstract
BACKGROUND Controlling the trajectory of a neuroprosthesis to reach distant targets is a commonly used brain-machine interface (BMI) task in primates and has not been available for rodents yet. NEW METHOD Here, we describe a novel, fine-tuned behavioral paradigm and setup which enables this task for rats in one-dimensional space for reaching two distant targets depending on their limited cognitive and visual capabilities compared to those of primates. An online transform was used to convert the activity of a pair of primary motor cortex (M1) units into two robotic actions. The rats were shaped to adapt to the transform and direct the robotic actuator toward the selected target by modulating the activity of the M1 neurons. RESULTS All three rats involved in the study were capable of achieving randomly selected targets with at least 78% accuracy. A total of 9 out of 16 pairs of units examined were eligible for exceeding this success criterion. Two out of three rats were capable of reversal learning, where the mapping between the activity of the M1 units and the robotic actions were reversed. COMPARISON WITH EXISTING METHODS The present work is the first demonstration of trajectory-based control of a neuroprosthetic device by rodents to reach two distant targets using visual feedback. CONCLUSION The behavioral paradigm and setup introduced here can be used as a cost-effective platform for elucidating the information processing principles in the neural circuits related to neuroprosthetic control and for studying the performance of novel BMI technologies using freely moving rats.
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Affiliation(s)
| | - Samet Kocatürk
- Department of Biomedical Engineering, Istanbul Medipol University, Istanbul, Turkey
| | - Tunçer Baykaş
- Department of Computer Engineering, Istanbul Medipol University, Istanbul, Turkey
| | - Mehmet Kocatürk
- Department of Biomedical Engineering, Istanbul Medipol University, Istanbul, Turkey; Research Institute for Health Sciences and Technology, Istanbul Medipol University, Istanbul, Turkey.
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48
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Wang Y, Fang P, Tang X, Jiang N, Tian L, Li X, Zheng Y, Huang J, Samuel OW, Wang H, Wu K, Li G. Effective Evaluation of Finger Sensation Evoking by Non-invasive Stimulation for Sensory Function Recovery in Transradial Amputees. IEEE Trans Neural Syst Rehabil Eng 2022; 30:519-528. [PMID: 35235514 DOI: 10.1109/tnsre.2022.3155756] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Synergetic recovery of both somatosensory and motor functions is highly desired by limb amputees to fully regain their lost limb abilities. The commercially available prostheses can restore the lost motor function in amputees but lack intuitive sensory feedback. The previous studies showed that electrical stimulation on the arm stump would be a promising approach to induce sensory information into the nervous system, enabling the possibility of realizing sensory feedback in limb prostheses. However, there are currently limited studies on the effective evaluation of the sensations evoked by transcutaneous electrical nerve stimulation (TENS). In this paper, a multichannel TENS platform was developed and the different stimulus patterns were designed to evoke stable finger sensations for a transradial amputee. Electroencephalogram (EEG) was recorded simultaneously during TENS on the arm stump, which was utilized to evaluate the evoked sensations. The experimental results revealed that different types of sensations on three phantom fingers could be stably evoked for the amputee by properly selecting TENS patterns. The analysis of the event-related potential (ERP) of EEG recordings further confirmed the evoked sensations, and ERP latencies and curve characteristics for different phantom fingers showed significant differences. This work may provide insight for an in-depth understanding of how somatosensation could be restored in limb amputees and offer technical support for the applications of non-invasive sensory feedback systems.
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49
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Towards in vivo neural decoding. Biomed Eng Lett 2022; 12:185-195. [PMID: 35529345 PMCID: PMC9046500 DOI: 10.1007/s13534-022-00217-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2021] [Revised: 01/17/2022] [Accepted: 01/23/2022] [Indexed: 10/19/2022] Open
Abstract
Conventional spike sorting and motor intention decoding algorithms are mostly implemented on an external computing device, such as a personal computer. The innovation of high-resolution and high-density electrodes to record the brain's activity at the single neuron level may eliminate the need for spike sorting altogether while potentially enabling in vivo neural decoding. This article explores the feasibility and efficient realization of in vivo decoding, with and without spike sorting. The efficiency of neural network-based models for reliable motor decoding is presented and the performance of candidate neural decoding schemes on sorted single-unit activity and unsorted multi-unit activity are evaluated. A programmable processor with a custom instruction set architecture, for the first time to the best of our knowledge, is designed and implemented for executing neural network operations in a standard 180-nm CMOS process. The processor's layout is estimated to occupy 49 mm 2 of silicon area and to dissipate 12 mW of power from a 1.8 V supply, which is within the tissue-safe operation of the brain.
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50
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Zeinolabedin SMA, Schuffny FM, George R, Kelber F, Bauer H, Scholze S, Hanzsche S, Stolba M, Dixius A, Ellguth G, Walter D, Hoppner S, Mayr C. A 16-Channel Fully Configurable Neural SoC With 1.52 μW/Ch Signal Acquisition, 2.79 μW/Ch Real-Time Spike Classifier, and 1.79 TOPS/W Deep Neural Network Accelerator in 22 nm FDSOI. IEEE TRANSACTIONS ON BIOMEDICAL CIRCUITS AND SYSTEMS 2022; 16:94-107. [PMID: 35025750 DOI: 10.1109/tbcas.2022.3142987] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
With the advent of high-density micro-electrodes arrays, developing neural probes satisfying the real-time and stringent power-efficiency requirements becomes more challenging. A smart neural probe is an essential device in future neuroscientific research and medical applications. To realize such devices, we present a 22 nm FDSOI SoC with complex on-chip real-time data processing and training for neural signal analysis. It consists of a digitally-assisted 16-channel analog front-end with 1.52 μW/Ch, dedicated bio-processing accelerators for spike detection and classification with 2.79 μW/Ch, and a 125 MHz RISC-V CPU, utilizing adaptive body biasing at 0.5 V with a supporting 1.79 TOPS/W MAC array. The proposed SoC shows a proof-of-concept of how to realize a high-level integration of various on-chip accelerators to satisfy the neural probe requirements for modern applications.
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