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Saway BF, Palmer C, Hughes C, Triano M, Suresh RE, Gilmore J, George M, Kautz SA, Rowland NC. The evolution of neuromodulation for chronic stroke: From neuroplasticity mechanisms to brain-computer interfaces. Neurotherapeutics 2024; 21:e00337. [PMID: 38377638 PMCID: PMC11103214 DOI: 10.1016/j.neurot.2024.e00337] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2023] [Revised: 02/05/2024] [Accepted: 02/13/2024] [Indexed: 02/22/2024] Open
Abstract
Stroke is one of the most common and debilitating neurological conditions worldwide. Those who survive experience motor, sensory, speech, vision, and/or cognitive deficits that severely limit remaining quality of life. While rehabilitation programs can help improve patients' symptoms, recovery is often limited, and patients frequently continue to experience impairments in functional status. In this review, invasive neuromodulation techniques to augment the effects of conventional rehabilitation methods are described, including vagus nerve stimulation (VNS), deep brain stimulation (DBS) and brain-computer interfaces (BCIs). In addition, the evidence base for each of these techniques, pivotal trials, and future directions are explored. Finally, emerging technologies such as functional near-infrared spectroscopy (fNIRS) and the shift to artificial intelligence-enabled implants and wearables are examined. While the field of implantable devices for chronic stroke recovery is still in a nascent stage, the data reviewed are suggestive of immense potential for reducing the impact and impairment from this globally prevalent disorder.
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Affiliation(s)
- Brian F Saway
- Department of Neurosurgery, Medical University of South Carolina, SC 29425, USA.
| | - Charles Palmer
- Department of Psychiatry, Medical University of South Carolina, SC 29425, USA
| | - Christopher Hughes
- Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA 15260, USA
| | - Matthew Triano
- Department of Neurosurgery, Medical University of South Carolina, SC 29425, USA
| | - Rishishankar E Suresh
- College of Medicine, Medical University of South Carolina, Charleston, SC 29425, USA
| | - Jordon Gilmore
- Department of Bioengineering, Clemson University, Clemson, SC 29634, USA
| | - Mark George
- Department of Psychiatry, Medical University of South Carolina, SC 29425, USA; Ralph H Johnson VA Health Care System, Charleston, SC 29425, USA
| | - Steven A Kautz
- Department of Health Science and Research, Medical University of South Carolina, SC 29425, USA; Ralph H Johnson VA Health Care System, Charleston, SC 29425, USA
| | - Nathan C Rowland
- Department of Neurosurgery, Medical University of South Carolina, SC 29425, USA; MUSC Institute for Neuroscience Discovery (MIND), Medical University of South Carolina, SC 29425, USA
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Zheng N, Jiang Y, Jiang S, Kim J, Chen G, Li Y, Cheng JX, Jia X, Yang C. Multifunctional Fiber-Based Optoacoustic Emitter as a Bidirectional Brain Interface. Adv Healthc Mater 2023; 12:e2300430. [PMID: 37451259 PMCID: PMC10592200 DOI: 10.1002/adhm.202300430] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2023] [Revised: 07/11/2023] [Accepted: 07/12/2023] [Indexed: 07/18/2023]
Abstract
A bidirectional brain interface with both "write" and "read" functions can be an important tool for fundamental studies and potential clinical treatments for neurological diseases. Herein, a miniaturized multifunctional fiber-based optoacoustic emitter (mFOE) is reported thatintegrates simultaneous optoacoustic stimulation for "write" and electrophysiology recording of neural circuits for "read". Because of the intrinsic ability of neurons to respond to acoustic wave, there is no requirement of the viral transfection. The orthogonality between optoacoustic waves and electrical field provides a solution to avoid the interference between electrical stimulation and recording. The stimulation function of the mFOE is first validated in cultured ratcortical neurons using calcium imaging. In vivo application of mFOE for successful simultaneous optoacoustic stimulation and electrical recording of brain activities is confirmed in mouse hippocampus in both acute and chronical applications up to 1 month. Minor brain tissue damage is confirmed after these applications. The capability of simultaneous neural stimulation and recording enabled by mFOE opens up new possibilities for the investigation of neural circuits and brings new insights into the study of ultrasound neurostimulation.
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Affiliation(s)
- Nan Zheng
- Division of Materials Science and Engineering, Boston University, Boston, MA, USA
| | - Ying Jiang
- Department of Biomedical Engineering, Boston University, Boston, MA, USA
| | - Shan Jiang
- Bradley Department of Electrical and Computer Engineering, Virginia Tech, Blacksburg, VA, USA
| | - Jongwoon Kim
- Bradley Department of Electrical and Computer Engineering, Virginia Tech, Blacksburg, VA, USA
| | - Guo Chen
- Department of Electrical and Computer Engineering, Boston University, Boston, MA, USA
| | - Yueming Li
- Department of Mechanical Engineering, Boston University, Boston, MA, USA
| | - Ji-Xin Cheng
- Department of Biomedical Engineering, Boston University, Boston, MA, USA
- Department of Electrical and Computer Engineering, Boston University, Boston, MA, USA
| | - Xiaoting Jia
- Bradley Department of Electrical and Computer Engineering, Virginia Tech, Blacksburg, VA, USA
| | - Chen Yang
- Department of Electrical and Computer Engineering, Boston University, Boston, MA, USA
- Department of Chemistry, Boston University, Boston, MA, USA
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Lim J, Wang PT, Bashford L, Kellis S, Shaw SJ, Gong H, Armacost M, Heydari P, Do AH, Andersen RA, Liu CY, Nenadic Z. Suppression of cortical electrostimulation artifacts using pre-whitening and null projection. J Neural Eng 2023; 20:056018. [PMID: 37666246 DOI: 10.1088/1741-2552/acf68b] [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: 04/25/2023] [Accepted: 09/04/2023] [Indexed: 09/06/2023]
Abstract
Objective.Invasive brain-computer interfaces (BCIs) have shown promise in restoring motor function to those paralyzed by neurological injuries. These systems also have the ability to restore sensation via cortical electrostimulation. Cortical stimulation produces strong artifacts that can obscure neural signals or saturate recording amplifiers. While front-end hardware techniques can alleviate this problem, residual artifacts generally persist and must be suppressed by back-end methods.Approach.We have developed a technique based on pre-whitening and null projection (PWNP) and tested its ability to suppress stimulation artifacts in electroencephalogram (EEG), electrocorticogram (ECoG) and microelectrode array (MEA) signals from five human subjects.Main results.In EEG signals contaminated by narrow-band stimulation artifacts, the PWNP method achieved average artifact suppression between 32 and 34 dB, as measured by an increase in signal-to-interference ratio. In ECoG and MEA signals contaminated by broadband stimulation artifacts, our method suppressed artifacts by 78%-80% and 85%, respectively, as measured by a reduction in interference index. When compared to independent component analysis, which is considered the state-of-the-art technique for artifact suppression, our method achieved superior results, while being significantly easier to implement.Significance.PWNP can potentially act as an efficient method of artifact suppression to enable simultaneous stimulation and recording in bi-directional BCIs to biomimetically restore motor function.
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Affiliation(s)
- Jeffrey Lim
- Department of Biomedical Engineering, University of California Irvine (UCI), Irvine, CA 92697, United States of America
| | - Po T Wang
- Department of Biomedical Engineering, University of California Irvine (UCI), Irvine, CA 92697, United States of America
| | - Luke Bashford
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA 91125, United States of America
| | - Spencer Kellis
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA 91125, United States of America
- Department of Neurological Surgery, Keck School of Medicine of University of Southern California (USC), Los Angeles, CA 90033, United States of America
- USC Neurorestoration Center, Keck School of Medicine of USC, Los Angeles, CA 90033, United States of America
| | - Susan J Shaw
- Department of Neurology, Rancho Los Amigos National Rehabilitation Center, Downey, CA 90242, United States of America
- Department of Neurology, Keck School of Medicine of USC, Los Angeles, CA 90033, United States of America
| | - Hui Gong
- Department of Neurology, Rancho Los Amigos National Rehabilitation Center, Downey, CA 90242, United States of America
- Department of Neurology, Keck School of Medicine of USC, Los Angeles, CA 90033, United States of America
| | - Michelle Armacost
- Department of Neurology, Rancho Los Amigos National Rehabilitation Center, Downey, CA 90242, United States of America
- Department of Neurology, Keck School of Medicine of USC, Los Angeles, CA 90033, United States of America
| | - Payam Heydari
- Department of Biomedical Engineering, University of California Irvine (UCI), Irvine, CA 92697, United States of America
- Department of Electrical Engineering and Computer Science, UCI, Irvine, CA 92697, United States of America
| | - An H Do
- Department of Neurology, UCI, Irvine, CA 92697, United States of America
| | - Richard A Andersen
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA 91125, United States of America
| | - Charles Y Liu
- Department of Neurological Surgery, Keck School of Medicine of University of Southern California (USC), Los Angeles, CA 90033, United States of America
- USC Neurorestoration Center, Keck School of Medicine of USC, Los Angeles, CA 90033, United States of America
- Department of Neurosurgery, Rancho Los Amigos National Rehabilitation Center, Downey, CA 90242, United States of America
| | - Zoran Nenadic
- Department of Biomedical Engineering, University of California Irvine (UCI), Irvine, CA 92697, United States of America
- Department of Electrical Engineering and Computer Science, UCI, Irvine, CA 92697, United States of America
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Sun Y, Shen A, Du C, Sun J, Chen X, Gao X. A Real-Time Non-Implantation Bi-Directional Brain-Computer Interface Solution Without Stimulation Artifacts. IEEE Trans Neural Syst Rehabil Eng 2023; 31:3566-3575. [PMID: 37665696 DOI: 10.1109/tnsre.2023.3311750] [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: 09/06/2023]
Abstract
The non-implantation bi-directional brain-computer interface (BCI) is a neural interface technology that enables direct two-way communication between the brain and the external world by both "reading" neural signals and "writing" stimulation patterns to the brain. This technology has vast potential applications, such as improving the quality of life for individuals with neurological and mental illnesses and even expanding the boundaries of human capabilities. Nonetheless, non-implantation bi-directional BCIs face challenges in generating real-time feedback and achieving compatibility between stimulation and recording. These issues arise due to the considerable overlap between electrical stimulation frequencies and electrophysiological recording frequencies, as well as the impediment caused by the skull to the interaction of external and internal currents. To address those challenges, this work proposes a novel solution that combines the temporal interference stimulation paradigm and minimally invasive skull modification. A longitudinal animal experiment has preliminarily validated the feasibility of the proposed method. In signal recording experiments, the average impedance of our scheme decreased by 4.59 kΩ , about 67%, compared to the conventional technique at 18 points. The peak-to-peak value of the Somatosensory Evoked Potential increased by 8%. Meanwhile, the signal-to-noise ratio of Steady-State Visual Evoked Potential increased by 5.13 dB, and its classification accuracy increased by 44%. The maximum bandwidth of the resting state rose by 63%. In electrical stimulation experiments, the signal-to-noise ratio of the low-frequency response evoked by our scheme rose by 8.04 dB, and no stimulation artifacts were generated. The experimental results show that signal quality in acquisition has significantly improved, and frequency-band isolation eliminates stimulation artifacts at the source. The acquisition and stimulation pathways are real-time compatible in this non-implantation bi-directional BCI solution, which can provide technical support and theoretical guidance for creating closed-loop adaptive systems coupled with particular application scenarios in the future.
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Hughes C, Kozai T. Dynamic amplitude modulation of microstimulation evokes biomimetic onset and offset transients and reduces depression of evoked calcium responses in sensory cortices. Brain Stimul 2023; 16:939-965. [PMID: 37244370 PMCID: PMC10330928 DOI: 10.1016/j.brs.2023.05.013] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2022] [Revised: 05/15/2023] [Accepted: 05/17/2023] [Indexed: 05/29/2023] Open
Abstract
BACKGROUND Intracortical microstimulation (ICMS) is an emerging approach to restore sensation to people with neurological injury or disease. Biomimetic microstimulation, or stimulus trains that mimic neural activity in the brain through encoding of onset and offset transients, could improve the utility of ICMS for brain-computer interface (BCI) applications, but how biomimetic microstimulation affects neural activation is not understood. Current "biomimetic" ICMS trains aim to reproduce the strong onset and offset transients evoked in the brain by sensory input through dynamic modulation of stimulus parameters. Stimulus induced depression of neural activity (decreases in evoked intensity over time) is also a potential barrier to clinical implementation of sensory feedback, and dynamic microstimulation may reduce this effect. OBJECTIVE We evaluated how bio-inspired ICMS trains with dynamic modulation of amplitude and/or frequency change the calcium response, spatial distribution, and depression of neurons in the somatosensory and visual cortices. METHODS Calcium responses of neurons were measured in Layer 2/3 of visual and somatosensory cortices of anesthetized GCaMP6s mice in response to ICMS trains with fixed amplitude and frequency (Fixed) and three dynamic ICMS trains that increased the stimulation intensity during the onset and offset of stimulation by modulating the amplitude (DynAmp), frequency (DynFreq), or amplitude and frequency (DynBoth). ICMS was provided for either 1-s with 4-s breaks (Short) or for 30-s with 15-s breaks (Long). RESULTS DynAmp and DynBoth trains evoked distinct onset and offset transients in recruited neural populations, while DynFreq trains evoked population activity similar to Fixed trains. Individual neurons had heterogeneous responses primarily based on how quickly they depressed to ICMS, where neurons farther from the electrode depressed faster and a small subpopulation (1-5%) were modulated by DynFreq trains. Neurons that depressed to Short trains were also more likely to depress to Long trains, but Long trains induced more depression overall due to the increased stimulation length. Increasing the amplitude during the hold phase resulted in an increase in recruitment and intensity which resulted in more depression and reduced offset responses. Dynamic amplitude modulation reduced stimulation induced depression by 14.6 ± 0.3% for Short and 36.1 ± 0.6% for Long trains. Ideal observers were 0.031 ± 0.009 s faster for onset detection and 1.33 ± 0.21 s faster for offset detection with dynamic amplitude encoding. CONCLUSIONS Dynamic amplitude modulation evokes distinct onset and offset transients, reduces depression of neural calcium activity, and decreases total charge injection for sensory feedback in BCIs by lowering recruitment of neurons during long maintained periods of ICMS. In contrast, dynamic frequency modulation evokes distinct onset and offset transients in a small subpopulation of neurons but also reduces depression in recruited neurons by reducing the rate of activation.
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Affiliation(s)
- Christopher Hughes
- Department of Bioengineering, University of Pittsburgh, USA; Center for the Neural Basis of Cognition, USA
| | - Takashi Kozai
- Department of Bioengineering, University of Pittsburgh, USA; Center for the Neural Basis of Cognition, USA; Department of Neuroscience, University of Pittsburgh, USA; McGowan Institute for Regenerative Medicine, University of Pittsburgh, Pittsburgh, PA, USA; NeuroTech Center, University of Pittsburgh Brain Institute, Pittsburgh, PA, USA.
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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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Gao W, Cui Z, Yu Y, Mao J, Xu J, Ji L, Kan X, Shen X, Li X, Zhu S, Hong Y. Application of a Brain–Computer Interface System with Visual and Motor Feedback in Limb and Brain Functional Rehabilitation after Stroke: Case Report. Brain Sci 2022; 12:brainsci12081083. [PMID: 36009146 PMCID: PMC9405856 DOI: 10.3390/brainsci12081083] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2022] [Revised: 08/05/2022] [Accepted: 08/10/2022] [Indexed: 11/25/2022] Open
Abstract
(1) Objective: To investigate the feasibility, safety, and effectiveness of a brain–computer interface (BCI) system with visual and motor feedback in limb and brain function rehabilitation after stroke. (2) Methods: First, we recruited three hemiplegic stroke patients to perform rehabilitation training using a BCI system with visual and motor feedback for two consecutive days (four sessions) to verify the feasibility and safety of the system. Then, we recruited five other hemiplegic stroke patients for rehabilitation training (6 days a week, lasting for 12–14 days) using the same BCI system to verify the effectiveness. The mean and Cohen’s w were used to compare the changes in limb motor and brain functions before and after training. (3) Results: In the feasibility verification, the continuous motor state switching time (CMSST) of the three patients was 17.8 ± 21.0s, and the motor state percentages (MSPs) in the upper and lower limb training were 52.6 ± 25.7% and 72.4 ± 24.0%, respectively. The effective training revolutions (ETRs) per minute were 25.8 ± 13.0 for upper limb and 24.8 ± 6.4 for lower limb. There were no adverse events during the training process. Compared with the baseline, the motor function indices of the five patients were improved, including sitting balance ability, upper limb Fugel–Meyer assessment (FMA), lower limb FMA, 6 min walking distance, modified Barthel index, and root mean square (RMS) value of triceps surae, which increased by 0.4, 8.0, 5.4, 11.4, 7.0, and 0.9, respectively, and all had large effect sizes (Cohen’s w ≥ 0.5). The brain function indices of the five patients, including the amplitudes of the motor evoked potentials (MEP) on the non-lesion side and lesion side, increased by 3.6 and 3.7, respectively; the latency of MEP on the non-lesion side was shortened by 2.6 ms, and all had large effect sizes (Cohen’s w ≥ 0.5). (4) Conclusions: The BCI system with visual and motor feedback is applicable in active rehabilitation training of stroke patients with hemiplegia, and the pilot results show potential multidimensional benefits after a short course of treatment.
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Affiliation(s)
- Wen Gao
- Department of Rehabilitation Medicine, The Second Hospital of Anhui Medical University, No. 678 Furong Road, Economic and Technological Development Zone, Hefei 230601, China
| | - Zhengzhe Cui
- Zhejiang Laboratory, Department of Intelligent Robot, Keji Avenue, Yuhang Zone, Hangzhou 311100, China
| | - Yang Yu
- Department of Rehabilitation Medicine, The Second Hospital of Anhui Medical University, No. 678 Furong Road, Economic and Technological Development Zone, Hefei 230601, China
| | - Jing Mao
- Department of Rehabilitation Medicine, The Second Hospital of Anhui Medical University, No. 678 Furong Road, Economic and Technological Development Zone, Hefei 230601, China
| | - Jun Xu
- Department of Rehabilitation Medicine, The Second Hospital of Anhui Medical University, No. 678 Furong Road, Economic and Technological Development Zone, Hefei 230601, China
| | - Leilei Ji
- Department of Rehabilitation Medicine, The Second Hospital of Anhui Medical University, No. 678 Furong Road, Economic and Technological Development Zone, Hefei 230601, China
| | - Xiuli Kan
- Department of Rehabilitation Medicine, The Second Hospital of Anhui Medical University, No. 678 Furong Road, Economic and Technological Development Zone, Hefei 230601, China
| | - Xianshan Shen
- Department of Rehabilitation Medicine, The Second Hospital of Anhui Medical University, No. 678 Furong Road, Economic and Technological Development Zone, Hefei 230601, China
| | - Xueming Li
- Department of Rehabilitation Medicine, The Second Hospital of Anhui Medical University, No. 678 Furong Road, Economic and Technological Development Zone, Hefei 230601, China
| | - Shiqiang Zhu
- Zhejiang Laboratory, Department of Intelligent Robot, Keji Avenue, Yuhang Zone, Hangzhou 311100, China
- Ocean College, Zhejiang University, No. 866 Yuhangtang Road, Xihu Zone, Hangzhou 310030, China
- Correspondence: (S.Z.); (Y.H.); Tel.: +86-0571-56390588 (S.Z.); +86-0551-65997012 (Y.H.)
| | - Yongfeng Hong
- Department of Rehabilitation Medicine, The Second Hospital of Anhui Medical University, No. 678 Furong Road, Economic and Technological Development Zone, Hefei 230601, China
- Correspondence: (S.Z.); (Y.H.); Tel.: +86-0571-56390588 (S.Z.); +86-0551-65997012 (Y.H.)
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8
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Effects of stimulus pulse rate on somatosensory adaptation in the human cortex. Brain Stimul 2022; 15:987-995. [DOI: 10.1016/j.brs.2022.05.021] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2021] [Revised: 05/26/2022] [Accepted: 05/31/2022] [Indexed: 01/01/2023] Open
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Woeppel K, Hughes C, Herrera AJ, Eles JR, Tyler-Kabara EC, Gaunt RA, Collinger JL, Cui XT. Explant Analysis of Utah Electrode Arrays Implanted in Human Cortex for Brain-Computer-Interfaces. Front Bioeng Biotechnol 2021; 9:759711. [PMID: 34950640 PMCID: PMC8688945 DOI: 10.3389/fbioe.2021.759711] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2021] [Accepted: 10/29/2021] [Indexed: 01/11/2023] Open
Abstract
Brain-computer interfaces are being developed to restore movement for people living with paralysis due to injury or disease. Although the therapeutic potential is great, long-term stability of the interface is critical for widespread clinical implementation. While many factors can affect recording and stimulation performance including electrode material stability and host tissue reaction, these factors have not been investigated in human implants. In this clinical study, we sought to characterize the material integrity and biological tissue encapsulation via explant analysis in an effort to identify factors that influence electrophysiological performance. We examined a total of six Utah arrays explanted from two human participants involved in intracortical BCI studies. Two platinum (Pt) arrays were implanted for 980 days in one participant (P1) and two Pt and two iridium oxide (IrOx) arrays were implanted for 182 days in the second participant (P2). We observed that the recording quality followed a similar trend in all six arrays with an initial increase in peak-to-peak voltage during the first 30–40 days and gradual decline thereafter in P1. Using optical and two-photon microscopy we observed a higher degree of tissue encapsulation on both arrays implanted for longer durations in participant P1. We then used scanning electron microscopy and energy dispersive X-ray spectroscopy to assess material degradation. All measures of material degradation for the Pt arrays were found to be more prominent in the participant with a longer implantation time. Two IrOx arrays were subjected to brief survey stimulations, and one of these arrays showed loss of iridium from most of the stimulated sites. Recording performance appeared to be unaffected by this loss of iridium, suggesting that the adhesion of IrOx coating may have been compromised by the stimulation, but the metal layer did not detach until or after array removal. In summary, both tissue encapsulation and material degradation were more pronounced in the arrays that were implanted for a longer duration. Additionally, these arrays also had lower signal amplitude and impedance. New biomaterial strategies that minimize fibrotic encapsulation and enhance material stability should be developed to achieve high quality recording and stimulation for longer implantation periods.
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Affiliation(s)
- Kevin Woeppel
- Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA, United States.,Center for the Neural Basis of Cognition, Pittsburgh, PA, United States
| | - Christopher Hughes
- Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA, United States.,Center for the Neural Basis of Cognition, Pittsburgh, PA, United States.,Rehab Neural Engineering Labs, Pittsburgh, PA, United States
| | - Angelica J Herrera
- Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA, United States.,Center for the Neural Basis of Cognition, Pittsburgh, PA, United States.,Rehab Neural Engineering Labs, Pittsburgh, PA, United States
| | - James R Eles
- Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA, United States
| | - Elizabeth C Tyler-Kabara
- Center for the Neural Basis of Cognition, Pittsburgh, PA, United States.,Department of Neurosurgery, The University of Texas at Austin, Austin, TX, United States
| | - Robert A Gaunt
- Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA, United States.,Center for the Neural Basis of Cognition, Pittsburgh, PA, United States.,Rehab Neural Engineering Labs, Pittsburgh, PA, United States.,Department of Physical Medicine and Rehabilitation, University of Pittsburgh, Pittsburgh, PA, United States
| | - Jennifer L Collinger
- Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA, United States.,Center for the Neural Basis of Cognition, Pittsburgh, PA, United States.,Rehab Neural Engineering Labs, Pittsburgh, PA, United States.,Department of Physical Medicine and Rehabilitation, University of Pittsburgh, Pittsburgh, PA, United States
| | - Xinyan Tracy Cui
- Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA, United States.,Center for the Neural Basis of Cognition, Pittsburgh, PA, United States
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10
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Hossaini A, Valeriani D, Nam CS, Ferrante R, Mahmud M. A Functional BCI Model by the P2731 working group: Physiology. BRAIN-COMPUTER INTERFACES 2021. [DOI: 10.1080/2326263x.2021.1968665] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
Affiliation(s)
- Ali Hossaini
- Department of Engineering, King’s College London, London, UK
| | | | - Chang S. Nam
- Department of Industrial and Systems Engineering, North Carolina State University, Raleigh, NC, USA
| | | | - Mufti Mahmud
- School of Science and Technology, Nottingham Trent University, Nottingham, UK
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11
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Smirnov Y, Smirnov D, Popov A, Yakovenko S. Solving musculoskeletal biomechanics with machine learning. PeerJ Comput Sci 2021; 7:e663. [PMID: 34541309 PMCID: PMC8409332 DOI: 10.7717/peerj-cs.663] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2021] [Accepted: 07/16/2021] [Indexed: 06/13/2023]
Abstract
Deep learning is a relatively new computational technique for the description of the musculoskeletal dynamics. The experimental relationships of muscle geometry in different postures are the high-dimensional spatial transformations that can be approximated by relatively simple functions, which opens the opportunity for machine learning (ML) applications. In this study, we challenged general ML algorithms with the problem of approximating the posture-dependent moment arm and muscle length relationships of the human arm and hand muscles. We used two types of algorithms, light gradient boosting machine (LGB) and fully connected artificial neural network (ANN) solving the wrapping kinematics of 33 muscles spanning up to six degrees of freedom (DOF) each for the arm and hand model with 18 DOFs. The input-output training and testing datasets, where joint angles were the input and the muscle length and moment arms were the output, were generated by our previous phenomenological model based on the autogenerated polynomial structures. Both models achieved a similar level of errors: ANN model errors were 0.08 ± 0.05% for muscle lengths and 0.53 ± 0.29% for moment arms, and LGB model made similar errors-0.18 ± 0.06% and 0.13 ± 0.07%, respectively. LGB model reached the training goal with only 103 samples, while ANN required 106 samples; however, LGB models were about 39 times slower than ANN models in the evaluation. The sufficient performance of developed models demonstrates the future applicability of ML for musculoskeletal transformations in a variety of applications, such as in advanced powered prosthetics.
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Affiliation(s)
- Yaroslav Smirnov
- Department of Electronic Engineering, Igor Sikorsky Kyiv Polytechnic Institute, Kyiv, Ukraine
| | - Denys Smirnov
- Department of Computer-aided Management and Data Processing Systems, Igor Sikorsky Kyiv Polytechnic Institute, Kyiv, Ukraine
| | - Anton Popov
- Department of Electronic Engineering, Igor Sikorsky Kyiv Polytechnic Institute, Kyiv, Ukraine
- Data & Analytics, Ciklum, Kyiv, Ukraine
| | - Sergiy Yakovenko
- Department of Human Performance—Exercise Physiology, School of Medicine, West Virginia University, Morgantown, West Virginia, United States
- Department of Biomedical Engineering, Benjamin M. Statler College of Engineering and Mineral Resources, West Virginia University, Morgantown, West Virginia, United States
- Rockefeller Neuroscience Institute, School of Medicine, West Virginia University, Morgantown, West Virginia, United States
- Mechanical and Aerospace Engineering, Benjamin M. Statler College of Engineering and Mineral Resources, West Virginia University, Morgantown, West Virginia, United States
- Department of Neuroscience, School of Medicine, West Virginia University, Morgantown, West Virginia, United States
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12
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Hughes CL, Flesher SN, Weiss JM, Boninger M, Collinger JL, Gaunt RA. Perception of microstimulation frequency in human somatosensory cortex. eLife 2021; 10:65128. [PMID: 34313221 PMCID: PMC8376245 DOI: 10.7554/elife.65128] [Citation(s) in RCA: 30] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2020] [Accepted: 07/22/2021] [Indexed: 12/11/2022] Open
Abstract
Microstimulation in the somatosensory cortex can evoke artificial tactile percepts and can be incorporated into bidirectional brain–computer interfaces (BCIs) to restore function after injury or disease. However, little is known about how stimulation parameters themselves affect perception. Here, we stimulated through microelectrode arrays implanted in the somatosensory cortex of two human participants with cervical spinal cord injury and varied the stimulus amplitude, frequency, and train duration. Increasing the amplitude and train duration increased the perceived intensity on all tested electrodes. Surprisingly, we found that increasing the frequency evoked more intense percepts on some electrodes but evoked less-intense percepts on other electrodes. These different frequency–intensity relationships were divided into three groups, which also evoked distinct percept qualities at different stimulus frequencies. Neighboring electrode sites were more likely to belong to the same group. These results support the idea that stimulation frequency directly controls tactile perception and that these different percepts may be related to the organization of somatosensory cortex, which will facilitate principled development of stimulation strategies for bidirectional BCIs.
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Affiliation(s)
- Christopher L Hughes
- Rehab Neural Engineering Labs, University of Pittsburgh, Pittsburgh, United States.,Department of Bioengineering, University of Pittsburgh, Pittsburgh, United States.,Center for the Neural Basis of Cognition, University of Pittsburgh, Pittsburgh, United States
| | - Sharlene N Flesher
- Rehab Neural Engineering Labs, University of Pittsburgh, Pittsburgh, United States.,Department of Bioengineering, University of Pittsburgh, Pittsburgh, United States.,Center for the Neural Basis of Cognition, University of Pittsburgh, Pittsburgh, United States.,Department of Neurosurgery, Stanford University, Stanford, United States.,Department of Electrical Engineering, Stanford University, Stanford, United States
| | - Jeffrey M Weiss
- Rehab Neural Engineering Labs, University of Pittsburgh, Pittsburgh, United States.,Department of Physical Medicine and Rehabilitation, University of Pittsburgh, Pittsburgh, United States
| | - Michael Boninger
- Rehab Neural Engineering Labs, University of Pittsburgh, Pittsburgh, United States.,Department of Bioengineering, University of Pittsburgh, Pittsburgh, United States.,Department of Physical Medicine and Rehabilitation, University of Pittsburgh, Pittsburgh, United States.,Human Engineering Research Laboratories, VA Center of Excellence, Department of Veterans Affairs, Pittsburgh, United States
| | - Jennifer L Collinger
- Rehab Neural Engineering Labs, University of Pittsburgh, Pittsburgh, United States.,Department of Bioengineering, University of Pittsburgh, Pittsburgh, United States.,Center for the Neural Basis of Cognition, University of Pittsburgh, Pittsburgh, United States.,Department of Physical Medicine and Rehabilitation, University of Pittsburgh, Pittsburgh, United States.,Human Engineering Research Laboratories, VA Center of Excellence, Department of Veterans Affairs, Pittsburgh, United States
| | - Robert A Gaunt
- Rehab Neural Engineering Labs, University of Pittsburgh, Pittsburgh, United States.,Department of Bioengineering, University of Pittsburgh, Pittsburgh, United States.,Center for the Neural Basis of Cognition, University of Pittsburgh, Pittsburgh, United States.,Department of Physical Medicine and Rehabilitation, University of Pittsburgh, Pittsburgh, United States
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13
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Exploring the Use of Brain-Computer Interfaces in Stroke Neurorehabilitation. BIOMED RESEARCH INTERNATIONAL 2021; 2021:9967348. [PMID: 34239936 PMCID: PMC8235968 DOI: 10.1155/2021/9967348] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/20/2021] [Accepted: 06/04/2021] [Indexed: 11/17/2022]
Abstract
With the continuous development of artificial intelligence technology, "brain-computer interfaces" are gradually entering the field of medical rehabilitation. As a result, brain-computer interfaces (BCIs) have been included in many countries' strategic plans for innovating this field, and subsequently, major funding and talent have been invested in this technology. In neurological rehabilitation for stroke patients, the use of BCIs opens up a new chapter in "top-down" rehabilitation. In our study, we first reviewed the latest BCI technologies, then presented recent research advances and landmark findings in BCI-based neurorehabilitation for stroke patients. Neurorehabilitation was focused on the areas of motor, sensory, speech, cognitive, and environmental interactions. Finally, we summarized the shortcomings of BCI use in the field of stroke neurorehabilitation and the prospects for BCI technology development for rehabilitation.
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14
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Gao X, Wang Y, Chen X, Gao S. Interface, interaction, and intelligence in generalized brain-computer interfaces. Trends Cogn Sci 2021; 25:671-684. [PMID: 34116918 DOI: 10.1016/j.tics.2021.04.003] [Citation(s) in RCA: 42] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2020] [Revised: 03/07/2021] [Accepted: 04/05/2021] [Indexed: 11/16/2022]
Abstract
A brain-computer interface (BCI) establishes a direct communication channel between a brain and an external device. With recent advances in neurotechnology and artificial intelligence (AI), the brain signals in BCI communication have been advanced from sensation and perception to higher-level cognition activities. While the field of BCI has grown rapidly in the past decades, the core technologies and innovative ideas behind seemingly unrelated BCI systems have never been summarized from an evolutionary point of view. Here, we review various BCI paradigms and present an evolutionary model of generalized BCI technology which comprises three stages: interface, interaction, and intelligence (I3). We also highlight challenges, opportunities, and future perspectives in the development of new BCI technology.
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Affiliation(s)
- Xiaorong Gao
- Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, China
| | - Yijun Wang
- Institute of Semiconductors, Chinese Academy of Sciences, Beijing, China
| | - Xiaogang Chen
- Institute of Biomedical Engineering, Chinese Academy of Medical Sciences, Tianjin, China
| | - Shangkai Gao
- Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, China.
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15
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Flesher SN, Downey JE, Weiss JM, Hughes CL, Herrera AJ, Tyler-Kabara EC, Boninger ML, Collinger JL, Gaunt RA. A brain-computer interface that evokes tactile sensations improves robotic arm control. Science 2021; 372:831-836. [PMID: 34016775 DOI: 10.1126/science.abd0380] [Citation(s) in RCA: 150] [Impact Index Per Article: 50.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2020] [Accepted: 03/25/2021] [Indexed: 12/14/2022]
Abstract
Prosthetic arms controlled by a brain-computer interface can enable people with tetraplegia to perform functional movements. However, vision provides limited feedback because information about grasping objects is best relayed through tactile feedback. We supplemented vision with tactile percepts evoked using a bidirectional brain-computer interface that records neural activity from the motor cortex and generates tactile sensations through intracortical microstimulation of the somatosensory cortex. This enabled a person with tetraplegia to substantially improve performance with a robotic limb; trial times on a clinical upper-limb assessment were reduced by half, from a median time of 20.9 to 10.2 seconds. Faster times were primarily due to less time spent attempting to grasp objects, revealing that mimicking known biological control principles results in task performance that is closer to able-bodied human abilities.
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Affiliation(s)
- Sharlene N Flesher
- Rehab Neural Engineering Labs, University of Pittsburgh, Pittsburgh, PA, USA.,Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA, USA.,Center for the Neural Basis of Cognition, Pittsburgh, PA, USA
| | - John E Downey
- Rehab Neural Engineering Labs, University of Pittsburgh, Pittsburgh, PA, USA.,Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA, USA.,Center for the Neural Basis of Cognition, Pittsburgh, PA, USA.,Department of Organismal Biology, University of Chicago, Chicago, IL, USA
| | - Jeffrey M Weiss
- Rehab Neural Engineering Labs, University of Pittsburgh, Pittsburgh, PA, USA.,Department of Physical Medicine and Rehabilitation, University of Pittsburgh, Pittsburgh, PA, USA
| | - Christopher L Hughes
- Rehab Neural Engineering Labs, University of Pittsburgh, Pittsburgh, PA, USA.,Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA, USA.,Center for the Neural Basis of Cognition, Pittsburgh, PA, USA
| | - Angelica J Herrera
- Rehab Neural Engineering Labs, University of Pittsburgh, Pittsburgh, PA, USA.,Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA, USA.,Center for the Neural Basis of Cognition, Pittsburgh, PA, USA
| | | | - Michael L Boninger
- Rehab Neural Engineering Labs, University of Pittsburgh, Pittsburgh, PA, USA.,Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA, USA.,Department of Physical Medicine and Rehabilitation, University of Pittsburgh, Pittsburgh, PA, USA.,McGowan Institute for Regenerative Medicine, University of Pittsburgh, Pittsburgh, PA, USA.,VA Center of Excellence, Department of Veterans Affairs, Pittsburgh, PA, USA
| | - Jennifer L Collinger
- Rehab Neural Engineering Labs, University of Pittsburgh, Pittsburgh, PA, USA. .,Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA, USA.,Center for the Neural Basis of Cognition, Pittsburgh, PA, USA.,Department of Physical Medicine and Rehabilitation, University of Pittsburgh, Pittsburgh, PA, USA.,VA Center of Excellence, Department of Veterans Affairs, Pittsburgh, PA, USA
| | - Robert A Gaunt
- Rehab Neural Engineering Labs, University of Pittsburgh, Pittsburgh, PA, USA. .,Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA, USA.,Center for the Neural Basis of Cognition, Pittsburgh, PA, USA.,Department of Physical Medicine and Rehabilitation, University of Pittsburgh, Pittsburgh, PA, USA
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16
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Mazurek KA, Schieber MH. Injecting Information into the Mammalian Cortex: Progress, Challenges, and Promise. Neuroscientist 2020; 27:129-142. [PMID: 32648527 DOI: 10.1177/1073858420936253] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
For 150 years artificial stimulation has been used to study the function of the nervous system. Such stimulation-whether electrical or optogenetic-eventually may be used in neuroprosthetic devices to replace lost sensory inputs and to otherwise introduce information into the nervous system. Efforts toward this goal can be classified broadly as either biomimetic or arbitrary. Biomimetic stimulation aims to mimic patterns of natural neural activity, so that the subject immediately experiences the artificial stimulation as if it were natural sensation. Arbitrary stimulation, in contrast, makes no attempt to mimic natural patterns of neural activity. Instead, different stimuli-at different locations and/or in different patterns-are assigned different meanings randomly. The subject's time and effort then are required to learn to interpret different stimuli, a process that engages the brain's inherent plasticity. Here we will examine progress in using artificial stimulation to inject information into the cerebral cortex and discuss the challenges for and the promise of future development.
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Affiliation(s)
- Kevin A Mazurek
- Department of Neuroscience, University of Rochester, Rochester, NY, USA.,Del Monte Institute for Neuroscience, University of Rochester, Rochester, NY, USA
| | - Marc H Schieber
- Department of Neuroscience, University of Rochester, Rochester, NY, USA.,Del Monte Institute for Neuroscience, University of Rochester, Rochester, NY, USA.,Department of Neurology, University of Rochester, Rochester, NY, USA.,Department of Biomedical Engineering, University of Rochester, Rochester, NY, USA
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