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Tacca N, Dunlap C, Donegan SP, Hardin JO, Meyers E, Darrow MJ, Colachis Iv S, Gillman A, Friedenberg DA. Wearable high-density EMG sleeve for complex hand gesture classification and continuous joint angle estimation. Sci Rep 2024; 14:18564. [PMID: 39122791 PMCID: PMC11316006 DOI: 10.1038/s41598-024-64458-x] [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: 03/25/2024] [Accepted: 06/10/2024] [Indexed: 08/12/2024] Open
Abstract
High-density electromyography (HD-EMG) can provide a natural interface to enhance human-computer interaction (HCI). This study aims to demonstrate the capability of a novel HD-EMG forearm sleeve equipped with up to 150 electrodes to capture high-resolution muscle activity, decode complex hand gestures, and estimate continuous hand position via joint angle predictions. Ten able-bodied participants performed 37 hand movements and grasps while EMG was recorded using the HD-EMG sleeve. Simultaneously, an 18-sensor motion capture glove calculated 23 joint angles from the hand and fingers across all movements for training regression models. For classifying across the 37 gestures, our decoding algorithm was able to differentiate between sequential movements with 97.3 ± 0.3 % accuracy calculated on a 100 ms bin-by-bin basis. In a separate mixed dataset consisting of 19 movements randomly interspersed, decoding performance achieved an average bin-wise accuracy of 92.8 ± 0.8 % . When evaluating decoders for use in real-time scenarios, we found that decoders can reliably decode both movements and movement transitions, achieving an average accuracy of 93.3 ± 0.9 % on the sequential set and 88.5 ± 0.9 % on the mixed set. Furthermore, we estimated continuous joint angles from the EMG sleeve data, achieving a R 2 of 0.884 ± 0.003 in the sequential set and 0.750 ± 0.008 in the mixed set. Median absolute error (MAE) was kept below 10° across all joints, with a grand average MAE of 1.8 ± 0 . 04 ∘ and 3.4 ± 0 . 07 ∘ for the sequential and mixed datasets, respectively. We also assessed two algorithm modifications to address specific challenges for EMG-driven HCI applications. To minimize decoder latency, we used a method that accounts for reaction time by dynamically shifting cue labels in time. To reduce training requirements, we show that pretraining models with historical data provided an increase in decoding performance compared with models that were not pretrained when reducing the in-session training data to only one attempt of each movement. The HD-EMG sleeve, combined with sophisticated machine learning algorithms, can be a powerful tool for hand gesture recognition and joint angle estimation. This technology holds significant promise for applications in HCI, such as prosthetics, assistive technology, rehabilitation, and human-robot collaboration.
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Affiliation(s)
- Nicholas Tacca
- Battelle Memorial Institute, Neurotechnology, Columbus, OH, USA.
| | - Collin Dunlap
- Battelle Memorial Institute, Neurotechnology, Columbus, OH, USA
| | - Sean P Donegan
- Air Force Research Laboratory, Materials And Manufacturing Directorate, Wright-Patterson AFB, OH, USA
| | - James O Hardin
- Air Force Research Laboratory, Materials And Manufacturing Directorate, Wright-Patterson AFB, OH, USA
| | - Eric Meyers
- Battelle Memorial Institute, Neurotechnology, Columbus, OH, USA
| | | | | | - Andrew Gillman
- Air Force Research Laboratory, Materials And Manufacturing Directorate, Wright-Patterson AFB, OH, USA
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Levett JJ, Elkaim LM, Niazi F, Weber MH, Iorio-Morin C, Bonizzato M, Weil AG. Invasive Brain Computer Interface for Motor Restoration in Spinal Cord Injury: A Systematic Review. Neuromodulation 2024; 27:597-603. [PMID: 37943244 DOI: 10.1016/j.neurom.2023.10.006] [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: 06/12/2023] [Revised: 09/10/2023] [Accepted: 10/09/2023] [Indexed: 11/10/2023]
Abstract
STUDY DESIGN Systematic review of the literature. OBJECTIVES In recent years, brain-computer interface (BCI) has emerged as a potential treatment for patients with spinal cord injury (SCI). This is the first systematic review of the literature on invasive closed-loop BCI technologies for the treatment of SCI in humans. MATERIALS AND METHODS A comprehensive search of PubMed MEDLINE, Web of Science, and Ovid EMBASE was conducted following Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. RESULTS Of 8316 articles collected, 19 studies met all the inclusion criteria. Data from 21 patients were extracted from these studies. All patients sustained a cervical SCI and were treated using either a BCI with intracortical microelectrode arrays (n = 18, 85.7%) or electrocorticography (n = 3, 14.3%). To decode these neural signals, machine learning and statistical models were used: support vector machine in eight patients (38.1%), linear estimator in seven patients (33.3%), Hidden Markov Model in three patients (14.3%), and other in three patients (14.3%). As the outputs, ten patients (47.6%) underwent noninvasive functional electrical stimulation (FES) with a cuff; one (4.8%) had an invasive FES with percutaneous stimulation, and ten (47.6%) used an external device (neuroprosthesis or virtual avatar). Motor function was restored in all patients for each assigned task. Clinical outcome measures were heterogeneous across all studies. CONCLUSIONS Invasive techniques of BCI show promise for the treatment of SCI, but there is currently no technology that can restore complete functional autonomy in patients with SCI. The current techniques and outcomes of BCI vary greatly. Because invasive BCIs are still in the early stages of development, further clinical studies should be conducted to optimize the prognosis for patients with SCI.
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Affiliation(s)
- Jordan J Levett
- Faculty of Medicine, University of Montreal, Montreal, Quebec, Canada
| | - Lior M Elkaim
- Department of Neurology and Neurosurgery, McGill University, Montreal, Quebec, Canada
| | - Farbod Niazi
- Faculty of Medicine, University of Montreal, Montreal, Quebec, Canada
| | - Michael H Weber
- Department of Orthopaedic Surgery, McGill University, Montreal, Quebec, Canada
| | | | - Marco Bonizzato
- Department of Electrical Engineering and Institute of Biomedical Engineering, Polytechnique Montréal, Montreal, Quebec, Canada; Department of Neuroscience and Centre interdisciplinaire sur le cerveau et l'apprentissage, University of Montreal, Montreal, Quebec, Canada
| | - Alexander G Weil
- Division of Neurosurgery, St-Justine University Hospital, Montreal, Quebec, Canada.
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Barmpas K, Panagakis Y, Zoumpourlis G, Adamos DA, Laskaris N, Zafeiriou S. A causal perspective on brainwave modeling for brain-computer interfaces. J Neural Eng 2024; 21:036001. [PMID: 38621380 DOI: 10.1088/1741-2552/ad3eb5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2023] [Accepted: 04/15/2024] [Indexed: 04/17/2024]
Abstract
Objective. Machine learning (ML) models have opened up enormous opportunities in the field of brain-computer Interfaces (BCIs). Despite their great success, they usually face severe limitations when they are employed in real-life applications outside a controlled laboratory setting.Approach. Mixing causal reasoning, identifying causal relationships between variables of interest, with brainwave modeling can change one's viewpoint on some of these major challenges which can be found in various stages in the ML pipeline, ranging from data collection and data pre-processing to training methods and techniques.Main results. In this work, we employ causal reasoning and present a framework aiming to breakdown and analyze important challenges of brainwave modeling for BCIs.Significance. Furthermore, we present how general ML practices as well as brainwave-specific techniques can be utilized and solve some of these identified challenges. And finally, we discuss appropriate evaluation schemes in order to measure these techniques' performance and efficiently compare them with other methods that will be developed in the future.
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Affiliation(s)
- Konstantinos Barmpas
- Department of Computing, Imperial College London, London SW7 2RH, United Kingdom
- Cogitat Ltd, London, United Kingdom
| | - Yannis Panagakis
- Department of Informatics and Telecommunications, National and Kapodistrian University of Athens, Athens 15784, Greece
- Archimedes Research Unit, Research Center Athena, Athens 15125, Greece
- Cogitat Ltd, London, United Kingdom
| | | | - Dimitrios A Adamos
- Department of Computing, Imperial College London, London SW7 2RH, United Kingdom
- Cogitat Ltd, London, United Kingdom
| | - Nikolaos Laskaris
- School of Informatics, Aristotle University of Thessaloniki, Thessaloniki 54124, Greece
- Cogitat Ltd, London, United Kingdom
| | - Stefanos Zafeiriou
- Department of Computing, Imperial College London, London SW7 2RH, United Kingdom
- Cogitat Ltd, London, United Kingdom
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Holt MW, Robinson EC, Shlobin NA, Hanson JT, Bozkurt I. Intracortical brain-computer interfaces for improved motor function: a systematic review. Rev Neurosci 2024; 35:213-223. [PMID: 37845811 DOI: 10.1515/revneuro-2023-0077] [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: 07/18/2023] [Accepted: 09/23/2023] [Indexed: 10/18/2023]
Abstract
In this systematic review, we address the status of intracortical brain-computer interfaces (iBCIs) applied to the motor cortex to improve function in patients with impaired motor ability. This study followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 Guidelines for Systematic Reviews. Risk Of Bias In Non-randomized Studies - of Interventions (ROBINS-I) and the Effective Public Health Practice Project (EPHPP) were used to assess bias and quality. Advances in iBCIs in the last two decades demonstrated the use of iBCI to activate limbs for functional tasks, achieve neural typing for communication, and other applications. However, the inconsistency of performance metrics employed by these studies suggests the need for standardization. Each study was a pilot clinical trial consisting of 1-4, majority male (64.28 %) participants, with most trials featuring participants treated for more than 12 months (55.55 %). The systems treated patients with various conditions: amyotrophic lateral sclerosis, stroke, spinocerebellar degeneration without cerebellar involvement, and spinal cord injury. All participants presented with tetraplegia at implantation and were implanted with microelectrode arrays via pneumatic insertion, with nearly all electrode locations solely at the precentral gyrus of the motor cortex (88.88 %). The development of iBCI devices using neural signals from the motor cortex to improve motor-impaired patients has enhanced the ability of these systems to return ability to their users. However, many milestones remain before these devices can prove their feasibility for recovery. This review summarizes the achievements and shortfalls of these systems and their respective trials.
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Affiliation(s)
- Matthew W Holt
- Department of Natural Sciences, University of South Carolina Beaufort, 1 University Blvd, Bluffton, 29909, USA
| | | | - Nathan A Shlobin
- Department of Neurological Surgery, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA
| | - Jacob T Hanson
- Rocky Vista University College of Osteopathic Medicine, Englewood, CO 80112, USA
| | - Ismail Bozkurt
- Department of Neurosurgery, School of Medicine, Yuksek Ihtisas University, 06530 Ankara, Türkiye
- Department of Neurosurgery, Medical Park Ankara Hospital, 06680 Ankara, Türkiye
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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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Rouzitalab A, Boulay CB, Park J, Sachs AJ. Intracortical brain-computer interfaces in primates: a review and outlook. Biomed Eng Lett 2023; 13:375-390. [PMID: 37519868 PMCID: PMC10382423 DOI: 10.1007/s13534-023-00286-8] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2022] [Revised: 05/04/2023] [Accepted: 05/14/2023] [Indexed: 08/01/2023] Open
Abstract
Brain-computer interfaces (BCI) translate brain signals into artificial output to restore or replace natural central nervous system (CNS) functions. Multiple processes, including sensorimotor integration, decision-making, motor planning, execution, and updating, are involved in any movement. For example, a BCI may be better able to restore naturalistic motor behaviors if it uses signals from multiple brain areas and decodes natural behaviors' cognitive and motor aspects. This review provides an overview of the preliminary information necessary to plan a BCI project focusing on intracortical implants in primates. Since the brain structure and areas of non-human primates (NHP) are similar to humans, exploring the result of NHP studies will eventually benefit human BCI studies. The different types of BCI systems based on the target cortical area, types of signals, and decoding methods will be discussed. In addition, various successful state-of-the-art cases will be reviewed in more detail, focusing on the general algorithm followed in the real-time system. Finally, an outlook for improving the current BCI research studies will be debated.
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Affiliation(s)
- Alireza Rouzitalab
- School of Electrical Engineering and Computer Science, University of Ottawa, Ottawa, ON K1N 6N5 Canada
- The Ottawa Hospital Research Institute, Ottawa, ON Canada
| | | | - Jeongwon Park
- School of Electrical Engineering and Computer Science, University of Ottawa, Ottawa, ON K1N 6N5 Canada
- Department of Electrical and Biomedical Engineering, University of Nevada, Reno, NV 89557 USA
| | - Adam J. Sachs
- The Ottawa Hospital Research Institute, Ottawa, ON Canada
- The University of Ottawa Brain and Mind Research Institute, Ottawa, ON Canada
- Division of Neurosurgery, Department of Surgery, The Ottawa Hospital, Ottawa, ON Canada
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Jadavji Z, Kirton A, Metzler MJ, Zewdie E. BCI-activated electrical stimulation in children with perinatal stroke and hemiparesis: A pilot study. Front Hum Neurosci 2023; 17:1006242. [PMID: 37007682 PMCID: PMC10063823 DOI: 10.3389/fnhum.2023.1006242] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2022] [Accepted: 03/03/2023] [Indexed: 03/19/2023] Open
Abstract
BackgroundPerinatal stroke (PS) causes most hemiparetic cerebral palsy (CP) and results in lifelong disability. Children with severe hemiparesis have limited rehabilitation options. Brain computer interface- activated functional electrical stimulation (BCI-FES) of target muscles may enhance upper extremity function in hemiparetic adults. We conducted a pilot clinical trial to assess the safety and feasibility of BCI-FES in children with hemiparetic CP.MethodsThirteen participants (mean age = 12.2 years, 31% female) were recruited from a population-based cohort. Inclusion criteria were: (1) MRI-confirmed PS, (2) disabling hemiparetic CP, (3) age 6–18 years, (4) informed consent/assent. Those with neurological comorbidities or unstable epilepsy were excluded. Participants attended two BCI sessions: training and rehabilitation. They wore an EEG-BCI headset and two forearm extensor stimulation electrodes. Participants’ imagination of wrist extension was classified on EEG, after which muscle stimulation and visual feedback were provided when the correct visualization was detected.ResultsNo serious adverse events or dropouts occurred. The most common complaints were mild headache, headset discomfort and muscle fatigue. Children ranked the experience as comparable to a long car ride and none reported as unpleasant. Sessions lasted a mean of 87 min with 33 min of stimulation delivered. Mean classification accuracies were (M = 78.78%, SD = 9.97) for training and (M = 73.48, SD = 12.41) for rehabilitation. Mean Cohen’s Kappa across rehabilitation trials was M = 0.43, SD = 0.29, range = 0.019–1.00, suggesting BCI competency.ConclusionBrain computer interface-FES was well -tolerated and feasible in children with hemiparesis. This paves the way for clinical trials to optimize approaches and test efficacy.
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Affiliation(s)
- Zeanna Jadavji
- Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
- Department of Pediatrics, University of Calgary, Calgary, AB, Canada
- Alberta Children’s Hospital Research Institute, Calgary, AB, Canada
| | - Adam Kirton
- Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
- Alberta Children’s Hospital Research Institute, Calgary, AB, Canada
- Department of Pediatrics, Alberta Children’s Hospital, Calgary, AB, Canada
| | - Megan J. Metzler
- Department of Clinical Neurosciences, Alberta Children’s Hospital, Calgary, AB, Canada
| | - Ephrem Zewdie
- Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
- Department of Pediatrics, University of Calgary, Calgary, AB, Canada
- Alberta Children’s Hospital Research Institute, Calgary, AB, Canada
- *Correspondence: Ephrem Zewdie,
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Zuccaroli I, Lucke-Wold B, Palla A, Eremiev A, Sorrentino Z, Zakare-Fagbamila R, McNulty J, Christie C, Chandra V, Mampre D. Neural Bypasses: Literature Review and Future Directions in Developing Artificial Neural Connections. OBM NEUROBIOLOGY 2023; 7:158. [PMID: 36908763 PMCID: PMC9997488 DOI: 10.21926/obm.neurobiol.2301158] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/24/2023]
Abstract
Reported neuro-modulation schemes in the literature are typically classified as closed-loop or open-loop. A novel group of recently developed neuro-modulation devices may be better described as a neural bypass, which attempts to transmit neural data from one location of the nervous system to another. The most common form of neural bypasses in the literature utilize EEG recordings of cortical information paired with functional electrical stimulation for effector muscle output, most commonly for assistive applications and rehabilitation in spinal cord injury or stroke. Other neural bypass locations that have also been described, or may soon be in development, include cortical-spinal bypasses, cortical-cortical bypasses, autonomic bypasses, peripheral-central bypasses, and inter-subject bypasses. The most common recording devices include EEG, ECoG, and microelectrode arrays, while stimulation devices include both invasive and noninvasive electrodes. Several devices are in development to improve the temporal and spatial resolution and biocompatibility for neuronal recording and stimulation. A major barrier to entry includes neuroplasticity and current decoding mechanisms that regularly require retraining. Neural bypasses are a unique class of neuro-modulation. Continued advancement of neural recording and stimulating devices with high spatial and temporal resolution, combined with decoding mechanisms uninhibited by neuroplasticity, can expand the therapeutic capability of neural bypassing. Overall, neural bypasses are a promising modality to improve the treatment of common neurologic disorders, including stroke, spinal cord injury, peripheral nerve injury, brain injury and more.
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Affiliation(s)
| | | | | | - Alexander Eremiev
- Department of Neurosurgery, New York University School of Medicine, New York, USA
| | | | | | - Jack McNulty
- Department of Neurosurgery, University of Iowa, Iowa City, IA, USA
| | - Carlton Christie
- Department of Neurosurgery, University of Florida, Gainesville, USA
| | - Vyshak Chandra
- Department of Neurosurgery, University of Florida, Gainesville, USA
| | - David Mampre
- Johns Hopkins University, Baltimore, USA
- Department of Neurosurgery, University of Florida, Gainesville, USA
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Shen X, Wu Y, Lou X, Li Z, Ma L, Bian X. Central pattern generator network model for the alternating hind limb gait of rats based on the modified Van der Pol equation. Med Biol Eng Comput 2023; 61:555-566. [PMID: 36538267 DOI: 10.1007/s11517-022-02734-6] [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: 03/03/2022] [Accepted: 12/09/2022] [Indexed: 12/24/2022]
Abstract
Herein, we employed a central pattern generator (CPG), a spinal cord neural network that regulates lower-limb gait during intra-spinal micro-stimulation (ISMS). Particularly, ISMS was used to determine the spatial distribution pattern of CPG sites in the spinal cord and the signal regulation pattern that induced the CPG network to produce coordinated actions. Based on the oscillation phenomenon of the single CPG neurons of Van der Pol (VDP) oscillators, a double-cell CPG neural network model was constructed to realise double lower limbs, six-joint modelling, the simulation of 12 neural circuits, the CPG loci characterising stimuli-inducing alternating movements and changes in polarity stimulation signals in rat hindlimbs, and leg-state change movements. The feasibility and effectiveness of the CPG neural network were verified by recording the electromyographic burst-release mode of the flexor and extensor muscles of the knee joints during CPG electrical stimulation. The results revealed that the output pattern of the CPG presented stable rhythm and coordination characteristics. The 12-neuron CPG model based on the improved VDP equation realised single-point control while significantly reducing the number of stimulation electrodes in the gait training of spinal cord injury patients. We believe that this study advances motor function recovery in rehabilitation medicine.
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Affiliation(s)
- Xiaoyan Shen
- School of Information Science and Technology, Nantong University, 9 Seyuan Road, Nantong, 226019, Jiangsu Province, China. .,Nantong Research Institute for Advanced Communication Technologies, Nantong, Jiangsu, China. .,Co-Innovation Center of Neuroregeneration, Nantong University, Nantong, Jiangsu, China.
| | - Yan Wu
- School of Information Science and Technology, Nantong University, 9 Seyuan Road, Nantong, 226019, Jiangsu Province, China
| | - Xiongjie Lou
- School of Information Science and Technology, Nantong University, 9 Seyuan Road, Nantong, 226019, Jiangsu Province, China
| | - Zhiling Li
- School of Information Science and Technology, Nantong University, 9 Seyuan Road, Nantong, 226019, Jiangsu Province, China
| | - Lei Ma
- School of Information Science and Technology, Nantong University, 9 Seyuan Road, Nantong, 226019, Jiangsu Province, China
| | - Xiongheng Bian
- School of Information Science and Technology, Nantong University, 9 Seyuan Road, Nantong, 226019, Jiangsu Province, China
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Si X, Zhou Y, Li S, Zhang X, Han S, Xiang S, Ming D. Brain-Computer Interfaces in Visualized Medicine. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2023; 1199:127-153. [PMID: 37460730 DOI: 10.1007/978-981-32-9902-3_7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/20/2023]
Abstract
The brain-computer interface (BCI), also known as a brain-machine interface (BMI), has attracted extensive attention in biomedical applications. More importantly, BCI technologies have substantially revolutionized early predictions, diagnostic techniques, and rehabilitation strategies addressing acute diseases because of BCI's innovations and clinical translations. Therefore, in this chapter, a comprehensive description of the basic concepts of BCI will be exhibited, and various visualization techniques employed in BCI's medical applications will be discussed.
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Affiliation(s)
- Xiaopeng Si
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China.
- Tianjin Key Laboratory of Brain Science and Neural Engineering, Tianjin University, Tianjin, China.
| | - Yu Zhou
- College of Medical Technology and Engineering, Henan University of Science and Technology, Henan, China
| | - Sicheng Li
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China
- Tianjin Key Laboratory of Brain Science and Neural Engineering, Tianjin University, Tianjin, China
| | - Xingjian Zhang
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China
- Tianjin Key Laboratory of Brain Science and Neural Engineering, Tianjin University, Tianjin, China
| | - Shunli Han
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China
- Tianjin Key Laboratory of Brain Science and Neural Engineering, Tianjin University, Tianjin, China
| | - Shaoxin Xiang
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China
- Tianjin Key Laboratory of Brain Science and Neural Engineering, Tianjin University, Tianjin, China
| | - Dong Ming
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China
- Tianjin Key Laboratory of Brain Science and Neural Engineering, Tianjin University, Tianjin, China
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Vasko JL, Aume L, Tamrakar S, Colachis SCI, Dunlap CF, Rich A, Meyers EC, Gabrieli D, Friedenberg DA. Increasing Robustness of Brain-Computer Interfaces Through Automatic Detection and Removal of Corrupted Input Signals. Front Neurosci 2022; 16:858377. [PMID: 35573306 PMCID: PMC9096265 DOI: 10.3389/fnins.2022.858377] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2022] [Accepted: 03/15/2022] [Indexed: 11/29/2022] Open
Abstract
For brain–computer interfaces (BCIs) to be viable for long-term daily usage, they must be able to quickly identify and adapt to signal disruptions. Furthermore, the detection and mitigation steps need to occur automatically and without the need for user intervention while also being computationally tractable for the low-power hardware that will be used in a deployed BCI system. Here, we focus on disruptions that are likely to occur during chronic use that cause some recording channels to fail but leave the remaining channels unaffected. In these cases, the algorithm that translates recorded neural activity into actions, the neural decoder, should seamlessly identify and adjust to the altered neural signals with minimal inconvenience to the user. First, we introduce an adapted statistical process control (SPC) method that automatically identifies disrupted channels so that both decoding algorithms can be adjusted, and technicians can be alerted. Next, after identifying corrupted channels, we demonstrate the automated and rapid removal of channels from a neural network decoder using a masking approach that does not change the decoding architecture, making it amenable for transfer learning. Finally, using transfer and unsupervised learning techniques, we update the model weights to adjust for the corrupted channels without requiring the user to collect additional calibration data. We demonstrate with both real and simulated neural data that our approach can maintain high-performance while simultaneously minimizing computation time and data storage requirements. This framework is invisible to the user but can dramatically increase BCI robustness and usability.
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Affiliation(s)
- Jordan L Vasko
- Battelle Memorial Institute, Columbus, OH, United States
| | - Laura Aume
- Battelle Memorial Institute, Columbus, OH, United States
| | | | | | - Collin F Dunlap
- Battelle Memorial Institute, Columbus, OH, United States.,Department of Biomedical Engineering, The Ohio State University, Columbus, OH, United States
| | - Adam Rich
- Battelle Memorial Institute, Columbus, OH, United States
| | - Eric C Meyers
- Battelle Memorial Institute, Columbus, OH, United States
| | - David Gabrieli
- Battelle Memorial Institute, Columbus, OH, United States
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Sabir Z, Wahab HA, Nguyen TG, Altamirano GC, Erdoğan F, Ali MR. Intelligent computing technique for solving singular multi-pantograph delay differential equation. Soft comput 2022. [DOI: 10.1007/s00500-022-07065-0] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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13
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Developments and challenges in human performance enhancement technology. MEDICINE IN NOVEL TECHNOLOGY AND DEVICES 2021. [DOI: 10.1016/j.medntd.2021.100095] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
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Chandrasekaran S, Fifer M, Bickel S, Osborn L, Herrero J, Christie B, Xu J, Murphy RKJ, Singh S, Glasser MF, Collinger JL, Gaunt R, Mehta AD, Schwartz A, Bouton CE. Historical perspectives, challenges, and future directions of implantable brain-computer interfaces for sensorimotor applications. Bioelectron Med 2021; 7:14. [PMID: 34548098 PMCID: PMC8456563 DOI: 10.1186/s42234-021-00076-6] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2021] [Accepted: 08/29/2021] [Indexed: 11/10/2022] Open
Abstract
Almost 100 years ago experiments involving electrically stimulating and recording from the brain and the body launched new discoveries and debates on how electricity, movement, and thoughts are related. Decades later the development of brain-computer interface technology began, which now targets a wide range of applications. Potential uses include augmentative communication for locked-in patients and restoring sensorimotor function in those who are battling disease or have suffered traumatic injury. Technical and surgical challenges still surround the development of brain-computer technology, however, before it can be widely deployed. In this review we explore these challenges, historical perspectives, and the remarkable achievements of clinical study participants who have bravely forged new paths for future beneficiaries.
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Affiliation(s)
- Santosh Chandrasekaran
- Neural Bypass and Brain Computer Interface Laboratory, Feinstein Institutes for Medical Research, Northwell Health, Manhasset, NY, USA
| | - Matthew Fifer
- Research and Exploratory Development Department, Johns Hopkins University Applied Physics Laboratory, Laurel, MD, USA
| | - Stephan Bickel
- The Human Brain Mapping Laboratory, Feinstein Institutes for Medical Research, Northwell Health, Manhasset, NY, USA
- Department of Neurosurgery, Barbara Zucker School of Medicine at Hofstra/Northwell, Manhasset, NY, USA
- Department of Neurology, Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Manhasset, NY, USA
| | - Luke Osborn
- Research and Exploratory Development Department, Johns Hopkins University Applied Physics Laboratory, Laurel, MD, USA
| | - Jose Herrero
- The Human Brain Mapping Laboratory, Feinstein Institutes for Medical Research, Northwell Health, Manhasset, NY, USA
| | - Breanne Christie
- Research and Exploratory Development Department, Johns Hopkins University Applied Physics Laboratory, Laurel, MD, USA
| | - Junqian Xu
- Departments of Radiology and Psychiatry, Baylor College of Medicine, Houston, TX, USA
| | - Rory K J Murphy
- Department of Neurosurgery, Barrow Neurological Institute, St. Joseph's Hospital and Medical Center, Phoenix, AZ, USA
| | - Sandeep Singh
- Good Shepherd Rehabilitation Hospital, Allentown, PA, USA
| | - Matthew F Glasser
- Departments of Radiology and Neuroscience, Washington University in St Louis, Saint Louis, MO, USA
| | - Jennifer L Collinger
- Rehabilitation Neural Engineering Labs, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Physical Medicine and Rehabilitation, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA, USA
- Center for the Neural Basis of Cognition, University of Pittsburgh, Pittsburgh, PA, USA
| | - Robert Gaunt
- Rehabilitation Neural Engineering Labs, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Physical Medicine and Rehabilitation, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA, USA
- Center for the Neural Basis of Cognition, University of Pittsburgh, Pittsburgh, PA, USA
| | - Ashesh D Mehta
- The Human Brain Mapping Laboratory, Feinstein Institutes for Medical Research, Northwell Health, Manhasset, NY, USA
- Department of Neurosurgery, Barbara Zucker School of Medicine at Hofstra/Northwell, Manhasset, NY, USA
| | - Andrew Schwartz
- McGowan Institute of Regenerative Medicine, University of Pittsburgh, Pittsburgh, PA, USA
| | - Chad E Bouton
- Neural Bypass and Brain Computer Interface Laboratory, Feinstein Institutes for Medical Research, Northwell Health, Manhasset, NY, USA.
- Department of Molecular Medicine, Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Manhasset, NY, USA.
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15
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Jadavji Z, Zhang J, Paffrath B, Zewdie E, Kirton A. Can Children With Perinatal Stroke Use a Simple Brain Computer Interface? Stroke 2021; 52:2363-2370. [PMID: 34039029 DOI: 10.1161/strokeaha.120.030596] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
[Figure: see text].
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Affiliation(s)
- Zeanna Jadavji
- Calgary Pediatric Stroke Program (Z.J., J.Z., B.P., E.Z., A.K.), Cumming School of Medicine, University of Calgary, Canada.,Hotchkiss Brain Institute (Z.J., J.Z., B.P., E.Z., A.K.), Cumming School of Medicine, University of Calgary, Canada.,Alberta Children's Hospital Research Institute (Z.J., J.Z., B.P., E.Z., A.K.), Cumming School of Medicine, University of Calgary, Canada
| | - Jack Zhang
- Calgary Pediatric Stroke Program (Z.J., J.Z., B.P., E.Z., A.K.), Cumming School of Medicine, University of Calgary, Canada.,Hotchkiss Brain Institute (Z.J., J.Z., B.P., E.Z., A.K.), Cumming School of Medicine, University of Calgary, Canada.,Alberta Children's Hospital Research Institute (Z.J., J.Z., B.P., E.Z., A.K.), Cumming School of Medicine, University of Calgary, Canada
| | - Brett Paffrath
- Calgary Pediatric Stroke Program (Z.J., J.Z., B.P., E.Z., A.K.), Cumming School of Medicine, University of Calgary, Canada.,Hotchkiss Brain Institute (Z.J., J.Z., B.P., E.Z., A.K.), Cumming School of Medicine, University of Calgary, Canada.,Alberta Children's Hospital Research Institute (Z.J., J.Z., B.P., E.Z., A.K.), Cumming School of Medicine, University of Calgary, Canada
| | - Ephrem Zewdie
- Calgary Pediatric Stroke Program (Z.J., J.Z., B.P., E.Z., A.K.), Cumming School of Medicine, University of Calgary, Canada.,Hotchkiss Brain Institute (Z.J., J.Z., B.P., E.Z., A.K.), Cumming School of Medicine, University of Calgary, Canada.,Alberta Children's Hospital Research Institute (Z.J., J.Z., B.P., E.Z., A.K.), Cumming School of Medicine, University of Calgary, Canada.,Department of Pediatrics (E.Z.), Cumming School of Medicine, University of Calgary, Canada
| | - Adam Kirton
- Calgary Pediatric Stroke Program (Z.J., J.Z., B.P., E.Z., A.K.), Cumming School of Medicine, University of Calgary, Canada.,Hotchkiss Brain Institute (Z.J., J.Z., B.P., E.Z., A.K.), Cumming School of Medicine, University of Calgary, Canada.,Alberta Children's Hospital Research Institute (Z.J., J.Z., B.P., E.Z., A.K.), Cumming School of Medicine, University of Calgary, Canada.,Department of Clinical Neurosciences (A.K.), Cumming School of Medicine, University of Calgary, Canada
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16
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Solving a novel designed second order nonlinear Lane–Emden delay differential model using the heuristic techniques. Appl Soft Comput 2021. [DOI: 10.1016/j.asoc.2021.107105] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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17
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Intra-cortical brain-machine interfaces for controlling upper-limb powered muscle and robotic systems in spinal cord injury. Clin Neurol Neurosurg 2020; 196:106069. [DOI: 10.1016/j.clineuro.2020.106069] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2020] [Revised: 07/03/2020] [Accepted: 07/04/2020] [Indexed: 12/20/2022]
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18
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Heravi MAY, Maghooli K, Nowshiravan Rahatabad F, Rezaee R. Application of a neural interface for restoration of leg movements: Intra-spinal stimulation using the brain electrical activity in spinally injured rabbits. J Appl Biomed 2020; 18:33-40. [PMID: 34907723 DOI: 10.32725/jab.2020.009] [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] [Received: 05/01/2020] [Accepted: 06/12/2020] [Indexed: 11/05/2022] Open
Abstract
This study aimed to design a neural interface that extracts movement commands from the brain to generate appropriate intra-spinal stimulation to restore leg movement. This study comprised four steps: (1) Recording electrocorticographic (ECoG) signals and corresponding leg movements in different trials. (2) Partial laminectomy to induce spinal cord injury (SCI) and detect motor modules in the spinal cord. (3) Delivering appropriate intra-spinal stimulation to the motor modules for restoration of the movements to those documented before SCI. (4) Development of a neural interface created by sparse linear regression (SLiR) model to detect movement commands transmitted from the brain to the modules. Correlation coefficient (CC) and normalized root mean square (NRMS) error was calculated to evaluate the neural interface effectiveness. It was found that by stimulating detected spinal cord modules, joint angle evaluated before SCI was not significantly different from that of post-SCI (P > 0.05). Based on results of SLiR model, overall CC and NRMS values were 0.63 ± 0.14 and 0.34 ± 0.16 (mean ± SD), respectively. These results indicated that ECoG data contained information about intra-spinal stimulations and the developed neural interface could produce intra-spinal stimulation based on ECoG data, for restoration of leg movements after SCI.
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Affiliation(s)
| | - Keivan Maghooli
- Islamic Azad University, Science and Research Branch, Department of Biomedical Engineering, Tehran, Iran
| | | | - Ramin Rezaee
- Mashhad University of Medical Sciences, Faculty of Medicine, Clinical Research Unit, Mashhad, Iran.,Mashhad University of Medical Sciences, Neurogenic Inflammation Research Center, Mashhad, Iran
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19
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Ganzer PD, Colachis SC, Schwemmer MA, Friedenberg DA, Dunlap CF, Swiftney CE, Jacobowitz AF, Weber DJ, Bockbrader MA, Sharma G. Restoring the Sense of Touch Using a Sensorimotor Demultiplexing Neural Interface. Cell 2020; 181:763-773.e12. [DOI: 10.1016/j.cell.2020.03.054] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2019] [Revised: 10/09/2019] [Accepted: 03/24/2020] [Indexed: 12/11/2022]
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20
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Bouton CE. Restoring Movement in Paralysis with a Bioelectronic Neural Bypass Approach: Current State and Future Directions. Cold Spring Harb Perspect Med 2019; 9:a034306. [PMID: 30745288 PMCID: PMC6824398 DOI: 10.1101/cshperspect.a034306] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
Bioelectronic medicine is a rapidly growing field that explores targeted neuromodulation in new treatment options addressing both disease and injury. New bioelectronic methods are being developed to monitor and modulate neural activity directly. The therapeutic benefit of these approaches has been validated in recent clinical studies in various conditions, including paralysis. By using decoding and modulation strategies together, it is possible to restore lost function to those living with paralysis and other debilitating conditions by interpreting and rerouting signals around the affected portion of the nervous system. This, in effect, creates a bioelectronic "neural bypass" to serve the function of the damaged/degenerated network. By learning the language of neurons and using neural interface technology to tap into critical networks, new approaches to repairing or restoring function in areas impacted by disease or injury may become a reality.
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Affiliation(s)
- Chad E Bouton
- Feinstein Institute for Medical Research, Northwell Health, Manhasset, New York 11030
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21
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Ciancibello J, King K, Meghrazi MA, Padmanaban S, Levy T, Ramdeo R, Straka M, Bouton C. Closed-loop neuromuscular electrical stimulation using feedforward-feedback control and textile electrodes to regulate grasp force in quadriplegia. Bioelectron Med 2019; 5:19. [PMID: 32232108 PMCID: PMC7098255 DOI: 10.1186/s42234-019-0034-y] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2019] [Accepted: 10/14/2019] [Indexed: 11/22/2022] Open
Abstract
BACKGROUND Transcutaneous neuromuscular electrical stimulation is routinely used in physical rehabilitation and more recently in brain-computer interface applications for restoring movement in paralyzed limbs. Due to variable muscle responses to repeated or sustained stimulation, grasp force levels can change significantly over time. Here we develop and assess closed-loop methods to regulate individual finger forces to facilitate functional movement. We combined this approach with custom textile-based electrodes to form a light-weight, wearable device and evaluated in paralyzed study participants. METHODS A textile-based electrode sleeve was developed by the study team and Myant, Corp. (Toronto, ON, Canada) and evaluated in a study involving three able-body participants and two participants with quadriplegia. A feedforward-feedback control structure was designed and implemented to accurately maintain finger force levels in a quadriplegic study participant. RESULTS Individual finger flexion and extension movements, along with functional grasping, were evoked during neuromuscular electrical stimulation. Closed-loop control methods allowed accurate steady state performance (< 15% error) with a settling time of 0.67 s (SD = 0.42 s) for individual finger contact force in a participant with quadriplegia. CONCLUSIONS Textile-based electrodes were identified to be a feasible alternative to conventional electrodes and facilitated individual finger movement and functional grasping. Furthermore, closed-loop methods demonstrated accurate control of individual finger flexion force. This approach may be a viable solution for enabling grasp force regulation in quadriplegia. TRIAL REGISTRATION NCT, NCT03385005. Registered Dec. 28, 2017.
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Affiliation(s)
- John Ciancibello
- Feinstein Institute for Medical Research at Northwell Health, New York, USA
- Institute of Bioelectronic Medicine, Feinstein Institute for Medical Research, New York, USA
| | - Kevin King
- Feinstein Institute for Medical Research at Northwell Health, New York, USA
- Institute of Bioelectronic Medicine, Feinstein Institute for Medical Research, New York, USA
| | - Milad Alizadeh Meghrazi
- Institute of Biomaterials and Biomedical Engineering, University of Toronto, Toronto, ON Canada
- Myant Corp, Toronto, ON Canada
| | - Subash Padmanaban
- Feinstein Institute for Medical Research at Northwell Health, New York, USA
- Institute of Bioelectronic Medicine, Feinstein Institute for Medical Research, New York, USA
| | - Todd Levy
- Feinstein Institute for Medical Research at Northwell Health, New York, USA
- Institute of Bioelectronic Medicine, Feinstein Institute for Medical Research, New York, USA
| | - Richard Ramdeo
- Feinstein Institute for Medical Research at Northwell Health, New York, USA
- Institute of Bioelectronic Medicine, Feinstein Institute for Medical Research, New York, USA
| | - Malgorzata Straka
- Feinstein Institute for Medical Research at Northwell Health, New York, USA
- Institute of Bioelectronic Medicine, Feinstein Institute for Medical Research, New York, USA
| | - Chad Bouton
- Feinstein Institute for Medical Research at Northwell Health, New York, USA
- Institute of Bioelectronic Medicine, Feinstein Institute for Medical Research, New York, USA
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22
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Bockbrader MA, Francisco G, Lee R, Olson J, Solinsky R, Boninger ML. Brain Computer Interfaces in Rehabilitation Medicine. PM R 2019; 10:S233-S243. [PMID: 30269808 DOI: 10.1016/j.pmrj.2018.05.028] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2018] [Revised: 05/22/2018] [Accepted: 05/31/2018] [Indexed: 12/24/2022]
Abstract
One innovation currently influencing physical medicine and rehabilitation is brain-computer interface (BCI) technology. BCI systems used for motor control record neural activity associated with thoughts, perceptions, and motor intent; decode brain signals into commands for output devices; and perform the user's intended action through an output device. BCI systems used for sensory augmentation transduce environmental stimuli into neural signals interpretable by the central nervous system. Both types of systems have potential for reducing disability by facilitating a user's interaction with the environment. Investigational BCI systems are being used in the rehabilitation setting both as neuroprostheses to replace lost function and as potential plasticity-enhancing therapy tools aimed at accelerating neurorecovery. Populations benefitting from motor and somatosensory BCI systems include those with spinal cord injury, motor neuron disease, limb amputation, and stroke. This article discusses the basic components of BCI for rehabilitation, including recording systems and locations, signal processing and translation algorithms, and external devices controlled through BCI commands. An overview of applications in motor and sensory restoration is provided, along with ethical questions and user perspectives regarding BCI technology.
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Affiliation(s)
- Marcia A Bockbrader
- Department of Physical Medicine & Rehabilitation, The Ohio State University, 480 Medical Center Dr, Columbus, OH 43210; and Neurological Institute, Ohio State University Wexner Medical Center, Columbus, OH(∗).
| | - Gerard Francisco
- Department of Physical Medicine & Rehabilitation, The University of Texas, Houston, TX(†)
| | - Ray Lee
- Department of Orthopaedic and Rehabilitation, Schwab Rehabilitation Hospital, University of Chicago, Chicago, IL(‡)
| | - Jared Olson
- Department of Physical Medicine and Rehabilitation, University of Colorado, Aurora, CO(§)
| | - Ryan Solinsky
- Spaulding Rehabilitation Hospital, Boston; and Department of Physical Medicine and Rehabilitation, Harvard Medical School, Boston, MA(¶)
| | - Michael L Boninger
- Department of Physical Medicine and Rehabilitation, University of Pittsburgh, Pittsburgh; and VA Pittsburgh Health Care System, Pittsburgh, PA(#)
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23
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Tam WK, Wu T, Zhao Q, Keefer E, Yang Z. Human motor decoding from neural signals: a review. BMC Biomed Eng 2019; 1:22. [PMID: 32903354 PMCID: PMC7422484 DOI: 10.1186/s42490-019-0022-z] [Citation(s) in RCA: 29] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2019] [Accepted: 07/21/2019] [Indexed: 01/24/2023] Open
Abstract
Many people suffer from movement disability due to amputation or neurological diseases. Fortunately, with modern neurotechnology now it is possible to intercept motor control signals at various points along the neural transduction pathway and use that to drive external devices for communication or control. Here we will review the latest developments in human motor decoding. We reviewed the various strategies to decode motor intention from human and their respective advantages and challenges. Neural control signals can be intercepted at various points in the neural signal transduction pathway, including the brain (electroencephalography, electrocorticography, intracortical recordings), the nerves (peripheral nerve recordings) and the muscles (electromyography). We systematically discussed the sites of signal acquisition, available neural features, signal processing techniques and decoding algorithms in each of these potential interception points. Examples of applications and the current state-of-the-art performance were also reviewed. Although great strides have been made in human motor decoding, we are still far away from achieving naturalistic and dexterous control like our native limbs. Concerted efforts from material scientists, electrical engineers, and healthcare professionals are needed to further advance the field and make the technology widely available in clinical use.
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Affiliation(s)
- Wing-kin Tam
- Department of Biomedical Engineering, University of Minnesota Twin Cities, 7-105 Hasselmo Hall, 312 Church St. SE, Minnesota, 55455 USA
| | - Tong Wu
- Department of Biomedical Engineering, University of Minnesota Twin Cities, 7-105 Hasselmo Hall, 312 Church St. SE, Minnesota, 55455 USA
| | - Qi Zhao
- Department of Computer Science and Engineering, University of Minnesota Twin Cities, 4-192 Keller Hall, 200 Union Street SE, Minnesota, 55455 USA
| | - Edward Keefer
- Nerves Incorporated, Dallas, TX P. O. Box 141295 USA
| | - Zhi Yang
- Department of Biomedical Engineering, University of Minnesota Twin Cities, 7-105 Hasselmo Hall, 312 Church St. SE, Minnesota, 55455 USA
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24
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Bockbrader M. Upper limb sensorimotor restoration through brain–computer interface technology in tetraparesis. CURRENT OPINION IN BIOMEDICAL ENGINEERING 2019. [DOI: 10.1016/j.cobme.2019.09.002] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
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25
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Bockbrader M, Annetta N, Friedenberg D, Schwemmer M, Skomrock N, Colachis S, Zhang M, Bouton C, Rezai A, Sharma G, Mysiw WJ. Clinically Significant Gains in Skillful Grasp Coordination by an Individual With Tetraplegia Using an Implanted Brain-Computer Interface With Forearm Transcutaneous Muscle Stimulation. Arch Phys Med Rehabil 2019; 100:1201-1217. [PMID: 30902630 DOI: 10.1016/j.apmr.2018.07.445] [Citation(s) in RCA: 25] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2018] [Revised: 06/28/2018] [Accepted: 07/26/2018] [Indexed: 12/27/2022]
Abstract
OBJECTIVE To demonstrate naturalistic motor control speed, coordinated grasp, and carryover from trained to novel objects by an individual with tetraplegia using a brain-computer interface (BCI)-controlled neuroprosthetic. DESIGN Phase I trial for an intracortical BCI integrated with forearm functional electrical stimulation (FES). Data reported span postimplant days 137 to 1478. SETTING Tertiary care outpatient rehabilitation center. PARTICIPANT A 27-year-old man with C5 class A (on the American Spinal Injury Association Impairment Scale) traumatic spinal cord injury INTERVENTIONS: After array implantation in his left (dominant) motor cortex, the participant trained with BCI-FES to control dynamic, coordinated forearm, wrist, and hand movements. MAIN OUTCOME MEASURES Performance on standardized tests of arm motor ability (Graded Redefined Assessment of Strength, Sensibility, and Prehension [GRASSP], Action Research Arm Test [ARAT], Grasp and Release Test [GRT], Box and Block Test), grip myometry, and functional activity measures (Capabilities of Upper Extremity Test [CUE-T], Quadriplegia Index of Function-Short Form [QIF-SF], Spinal Cord Independence Measure-Self-Report [SCIM-SR]) with and without the BCI-FES. RESULTS With BCI-FES, scores improved from baseline on the following: Grip force (2.9 kg); ARAT cup, cylinders, ball, bar, and blocks; GRT can, fork, peg, weight, and tape; GRASSP strength and prehension (unscrewing lids, pouring from a bottle, transferring pegs); and CUE-T wrist and hand skills. QIF-SF and SCIM-SR eating, grooming, and toileting activities were expected to improve with home use of BCI-FES. Pincer grips and mobility were unaffected. BCI-FES grip skills enabled the participant to play an adapted "Battleship" game and manipulate household objects. CONCLUSIONS Using BCI-FES, the participant performed skillful and coordinated grasps and made clinically significant gains in tests of upper limb function. Practice generalized from training objects to household items and leisure activities. Motor ability improved for palmar, lateral, and tip-to-tip grips. The expects eventual home use to confer greater independence for activities of daily living, consistent with observed neurologic level gains from C5-6 to C7-T1. This marks a critical translational step toward clinical viability for BCI neuroprosthetics.
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Affiliation(s)
- Marcie Bockbrader
- Department of Physical Medicine & Rehabilitation, Ohio State University, Columbus, Ohio; Neurological Institute, Ohio State University Wexner Medical Center, Columbus, Ohio; Department of Biomedical Engineering, Ohio State University, Columbus, Ohio.
| | | | | | | | | | - Samuel Colachis
- Department of Physical Medicine & Rehabilitation, Ohio State University, Columbus, Ohio; Department of Biomedical Engineering, Ohio State University, Columbus, Ohio; Battelle Memorial Institute, Columbus, Ohio
| | | | | | - Ali Rezai
- Neurological Institute, Ohio State University Wexner Medical Center, Columbus, Ohio
| | | | - Walter J Mysiw
- Department of Physical Medicine & Rehabilitation, Ohio State University, Columbus, Ohio; Neurological Institute, Ohio State University Wexner Medical Center, Columbus, Ohio
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26
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Loeb GE. Neural Prosthetics:A Review of Empirical vs. Systems Engineering Strategies. Appl Bionics Biomech 2018; 2018:1435030. [PMID: 30532801 PMCID: PMC6247642 DOI: 10.1155/2018/1435030] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2018] [Revised: 06/28/2018] [Accepted: 08/05/2018] [Indexed: 12/21/2022] Open
Abstract
Implantable electrical interfaces with the nervous system were first enabled by cardiac pacemaker technology over 50 years ago and have since diverged into almost all of the physiological functions controlled by the nervous system. There have been a few major clinical and commercial successes, many contentious claims, and some outright failures. These tend to be reviewed within each clinical subspecialty, obscuring the many commonalities of neural control, biophysics, interface materials, electronic technologies, and medical device regulation that they share. This review cites a selection of foundational and recent journal articles and reviews for all major applications of neural prosthetic interfaces in clinical use, trials, or development. The hard-won knowledge and experience across all of these fields can now be amalgamated and distilled into more systematic processes for development of clinical products instead of the often empirical (trial and error) approaches to date. These include a frank assessment of a specific clinical problem, the state of its underlying science, the identification of feasible targets, the availability of suitable technologies, and the path to regulatory and reimbursement approval. Increasing commercial interest and investment facilitates this systematic approach, but it also motivates projects and products whose claims are dubious.
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Affiliation(s)
- Gerald E. Loeb
- Professor of Biomedical Engineering, University of Southern California, 1042 Downey Way (DRB-B11) Los Angeles, CA 90089, USA
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27
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Skomrock ND, Schwemmer MA, Ting JE, Trivedi HR, Sharma G, Bockbrader MA, Friedenberg DA. A Characterization of Brain-Computer Interface Performance Trade-Offs Using Support Vector Machines and Deep Neural Networks to Decode Movement Intent. Front Neurosci 2018; 12:763. [PMID: 30459542 PMCID: PMC6232881 DOI: 10.3389/fnins.2018.00763] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2018] [Accepted: 10/03/2018] [Indexed: 12/18/2022] Open
Abstract
Laboratory demonstrations of brain-computer interface (BCI) systems show promise for reducing disability associated with paralysis by directly linking neural activity to the control of assistive devices. Surveys of potential users have revealed several key BCI performance criteria for clinical translation of such a system. Of these criteria, high accuracy, short response latencies, and multi-functionality are three key characteristics directly impacted by the neural decoding component of the BCI system, the algorithm that translates neural activity into control signals. Building a decoder that simultaneously addresses these three criteria is complicated because optimizing for one criterion may lead to undesirable changes in the other criteria. Unfortunately, there has been little work to date to quantify how decoder design simultaneously affects these performance characteristics. Here, we systematically explore the trade-off between accuracy, response latency, and multi-functionality for discrete movement classification using two different decoding strategies-a support vector machine (SVM) classifier which represents the current state-of-the-art for discrete movement classification in laboratory demonstrations and a proposed deep neural network (DNN) framework. We utilized historical intracortical recordings from a human tetraplegic study participant, who imagined performing several different hand and finger movements. For both decoders, we found that response time increases (i.e., slower reaction) and accuracy decreases as the number of functions increases. However, we also found that both the increase of response times and the decline in accuracy with additional functions is less for the DNN than the SVM. We also show that data preprocessing steps can affect the performance characteristics of the two decoders in drastically different ways. Finally, we evaluated the performance of our tetraplegic participant using the DNN decoder in real-time to control functional electrical stimulation (FES) of his paralyzed forearm. We compared his performance to that of able-bodied participants performing the same task, establishing a quantitative target for ideal BCI-FES performance on this task. Cumulatively, these results help quantify BCI decoder performance characteristics relevant to potential users and the complex interactions between them.
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Affiliation(s)
- Nicholas D. Skomrock
- Advanced Analytics and Health Research, Battelle Memorial Institute, Columbus, OH, United States
| | - Michael A. Schwemmer
- Advanced Analytics and Health Research, Battelle Memorial Institute, Columbus, OH, United States
| | - Jordyn E. Ting
- Medical Devices and Neuromodulation, Battelle Memorial Institute, Columbus, OH, United States
| | - Hemang R. Trivedi
- Medical Devices and Neuromodulation, Battelle Memorial Institute, Columbus, OH, United States
| | - Gaurav Sharma
- Medical Devices and Neuromodulation, Battelle Memorial Institute, Columbus, OH, United States
| | - Marcia A. Bockbrader
- Neurological Institute, The Ohio State University, Columbus, OH, United States
- Department of Physical Medicine and Rehabilitation, The Ohio State University, Columbus, OH, United States
| | - David A. Friedenberg
- Advanced Analytics and Health Research, Battelle Memorial Institute, Columbus, OH, United States
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Schwemmer MA, Skomrock ND, Sederberg PB, Ting JE, Sharma G, Bockbrader MA, Friedenberg DA. Meeting brain-computer interface user performance expectations using a deep neural network decoding framework. Nat Med 2018; 24:1669-1676. [PMID: 30250141 DOI: 10.1038/s41591-018-0171-y] [Citation(s) in RCA: 73] [Impact Index Per Article: 12.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2017] [Accepted: 07/31/2018] [Indexed: 12/12/2022]
Abstract
Brain-computer interface (BCI) neurotechnology has the potential to reduce disability associated with paralysis by translating neural activity into control of assistive devices1-9. Surveys of potential end-users have identified key BCI system features10-14, including high accuracy, minimal daily setup, rapid response times, and multifunctionality. These performance characteristics are primarily influenced by the BCI's neural decoding algorithm1,15, which is trained to associate neural activation patterns with intended user actions. Here, we introduce a new deep neural network16 decoding framework for BCI systems enabling discrete movements that addresses these four key performance characteristics. Using intracortical data from a participant with tetraplegia, we provide offline results demonstrating that our decoder is highly accurate, sustains this performance beyond a year without explicit daily retraining by combining it with an unsupervised updating procedure3,17-20, responds faster than competing methods8, and can increase functionality with minimal retraining by using a technique known as transfer learning21. We then show that our participant can use the decoder in real-time to reanimate his paralyzed forearm with functional electrical stimulation (FES), enabling accurate manipulation of three objects from the grasp and release test (GRT)22. These results demonstrate that deep neural network decoders can advance the clinical translation of BCI technology.
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Affiliation(s)
| | | | - Per B Sederberg
- Department of Psychology, University of Virginia, Charlottesville, VA, USA
| | - Jordyn E Ting
- Medical Devices and Neuromodulation, Battelle Memorial Institute, Columbus, OH, USA
| | - Gaurav Sharma
- Medical Devices and Neuromodulation, Battelle Memorial Institute, Columbus, OH, USA
| | - Marcia A Bockbrader
- Neurological Institute, Ohio State University, Columbus, OH, USA.,Department of Physical Medicine and Rehabilitation, Ohio State University, Columbus, OH, USA
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Annetta NV, Friend J, Schimmoeller A, Buck VS, Friedenberg DA, Bouton CE, Bockbrader MA, Ganzer PD, Colachis Iv SC, Zhang M, Mysiw WJ, Rezai AR, Sharma G. A High Definition Noninvasive Neuromuscular Electrical Stimulation System for Cortical Control of Combinatorial Rotary Hand Movements in a Human With Tetraplegia. IEEE Trans Biomed Eng 2018; 66:910-919. [PMID: 30106673 DOI: 10.1109/tbme.2018.2864104] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
OBJECTIVE Paralysis resulting from spinal cord injury (SCI) can have a devastating effect on multiple arm and hand motor functions. Rotary hand movements, such as supination and pronation, are commonly impaired by upper extremity paralysis, and are essential for many activities of daily living. In this proof-of-concept study, we utilize a neural bypass system (NBS) to decode motor intention from motor cortex to control combinatorial rotary hand movements elicited through stimulation of the arm muscles, effectively bypassing the SCI of the study participant. We describe the NBS system architecture and design that enabled this functionality. METHODS The NBS consists of three main functional components: 1) implanted intracortical microelectrode array, 2) neural data processing using a computer, and, 3) a noninvasive neuromuscular electrical stimulation (NMES) system. RESULTS We address previous limitations of the NBS, and confirm the enhanced capability of the NBS to enable, in real-time, combinatorial hand rotary motor functions during a functionally relevant object manipulation task. CONCLUSION This enhanced capability was enabled by accurate decoding of multiple movement intentions from the participant's motor cortex, interleaving NMES patterns to combine hand movements, and dynamically switching between NMES patterns to adjust for hand position changes during movement. SIGNIFICANCE These results have implications for enabling complex rotary hand functions in sequence with other functionally relevant movements for patients suffering from SCI, stroke, and other sensorimotor dysfunctions.
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Zhang M, Schwemmer MA, Ting JE, Majstorovic CE, Friedenberg DA, Bockbrader MA, Jerry Mysiw W, Rezai AR, Annetta NV, Bouton CE, Bresler HS, Sharma G. Extracting wavelet based neural features from human intracortical recordings for neuroprosthetics applications. Bioelectron Med 2018; 4:11. [PMID: 32232087 PMCID: PMC7098253 DOI: 10.1186/s42234-018-0011-x] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2018] [Accepted: 07/17/2018] [Indexed: 12/15/2022] Open
Abstract
Background Understanding the long-term behavior of intracortically-recorded signals is essential for improving the performance of Brain Computer Interfaces. However, few studies have systematically investigated chronic neural recordings from an implanted microelectrode array in the human brain. Methods In this study, we show the applicability of wavelet decomposition method to extract and demonstrate the utility of long-term stable features in neural signals obtained from a microelectrode array implanted in the motor cortex of a human with tetraplegia. Wavelet decomposition was applied to the raw voltage data to generate mean wavelet power (MWP) features, which were further divided into three sub-frequency bands, low-frequency MWP (lf-MWP, 0–234 Hz), mid-frequency MWP (mf-MWP, 234 Hz–3.75 kHz) and high-frequency MWP (hf-MWP, >3.75 kHz). We analyzed these features using data collected from two experiments that were repeated over the course of about 3 years and compared their signal stability and decoding performance with the more standard threshold crossings, local field potentials (LFP), multi-unit activity (MUA) features obtained from the raw voltage recordings. Results All neural features could stably track neural information for over 3 years post-implantation and were less prone to signal degradation compared to threshold crossings. Furthermore, when used as an input to support vector machine based decoding algorithms, the mf-MWP and MUA demonstrated significantly better performance, respectively, in classifying imagined motor tasks than using the lf-MWP, hf-MWP, LFP, or threshold crossings. Conclusions Our results suggest that using MWP features in the appropriate frequency bands can provide an effective neural feature for brain computer interface intended for chronic applications. Trial registration This study was approved by the U.S. Food and Drug Administration (Investigational Device Exemption) and the Ohio State University Medical Center Institutional Review Board (Columbus, Ohio). The study conformed to institutional requirements for the conduct of human subjects and was filed on ClinicalTrials.gov (Identifier NCT01997125). Electronic supplementary material The online version of this article (10.1186/s42234-018-0011-x) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Mingming Zhang
- 1Battelle Memorial Institute, 505 King Ave, Columbus, OH 43021 USA
| | | | - Jordyn E Ting
- 1Battelle Memorial Institute, 505 King Ave, Columbus, OH 43021 USA
| | | | | | - Marcia A Bockbrader
- 2Department of Physical Medicine and Rehabilitation, The Ohio State University, Columbus, OH 43210 USA
| | - W Jerry Mysiw
- 2Department of Physical Medicine and Rehabilitation, The Ohio State University, Columbus, OH 43210 USA
| | - Ali R Rezai
- 3West Virginia University School of Medicine, 1 Medical Center Dr, Morgantown, WV 26506 USA
| | | | - Chad E Bouton
- 4Feinstein Institute for Medical Research, Manhasset, NY 11030 USA
| | | | - Gaurav Sharma
- 1Battelle Memorial Institute, 505 King Ave, Columbus, OH 43021 USA
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Saniotis A, Henneberg M, Sawalma AR. Integration of Nanobots Into Neural Circuits As a Future Therapy for Treating Neurodegenerative Disorders. Front Neurosci 2018; 12:153. [PMID: 29618966 PMCID: PMC5872519 DOI: 10.3389/fnins.2018.00153] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2017] [Accepted: 02/26/2018] [Indexed: 01/28/2023] Open
Abstract
Recent neuroscientific research demonstrates that the human brain is becoming altered by technological devices. Improvements in biotechnologies and computer based technologies are now increasing the likelihood for the development of brain augmentation devices in the next 20 years. We have developed the idea of an “Endomyccorhizae like interface” (ELI) nanocognitive device as a new kind of future neuroprosthetic which aims to facilitate neuronal network properties in individuals with neurodegenerative disorders. The design of our ELI may overcome the problems of invasive neuroprosthetics, post-operative inflammation, and infection and neuroprosthetic degradation. The method in which our ELI is connected and integrated to neuronal networks is based on a mechanism similar to endomyccorhizae which is the oldest and most widespread form of plant symbiosis. We propose that the principle of Endomyccorhizae could be relevant for developing a crossing point between the ELI and neuronal networks. Similar to endomyccorhizae the ELI will be designed to form webs, each of which connects multiple neurons together. The ELI will function to sense action potentials and deliver it to the neurons it connects to. This is expected to compensate for neuronal loss in some neurodegenerative disorders, such as Alzheimer's disease and Parkinson's disease.
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Affiliation(s)
- Arthur Saniotis
- Biological Anthropology and Comparative Anatomy Unit, School of Medicine, University of Adelaide, Adelaide, SA, Australia.,Institute of Evolutionary Medicine, University of Zürich, Zurich, Switzerland
| | - Maciej Henneberg
- Biological Anthropology and Comparative Anatomy Unit, School of Medicine, University of Adelaide, Adelaide, SA, Australia.,Institute of Evolutionary Medicine, University of Zürich, Zurich, Switzerland
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32
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Colachis SC, Bockbrader MA, Zhang M, Friedenberg DA, Annetta NV, Schwemmer MA, Skomrock ND, Mysiw WJ, Rezai AR, Bresler HS, Sharma G. Dexterous Control of Seven Functional Hand Movements Using Cortically-Controlled Transcutaneous Muscle Stimulation in a Person With Tetraplegia. Front Neurosci 2018; 12:208. [PMID: 29670506 PMCID: PMC5893794 DOI: 10.3389/fnins.2018.00208] [Citation(s) in RCA: 33] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2017] [Accepted: 03/15/2018] [Indexed: 01/05/2023] Open
Abstract
Individuals with tetraplegia identify restoration of hand function as a critical, unmet need to regain their independence and improve quality of life. Brain-Computer Interface (BCI)-controlled Functional Electrical Stimulation (FES) technology addresses this need by reconnecting the brain with paralyzed limbs to restore function. In this study, we quantified performance of an intuitive, cortically-controlled, transcutaneous FES system on standardized object manipulation tasks from the Grasp and Release Test (GRT). We found that a tetraplegic individual could use the system to control up to seven functional hand movements, each with >95% individual accuracy. He was able to select one movement from the possible seven movements available to him and use it to appropriately manipulate all GRT objects in real-time using naturalistic grasps. With the use of the system, the participant not only improved his GRT performance over his baseline, demonstrating an increase in number of transfers for all objects except the Block, but also significantly improved transfer times for the heaviest objects (videocassette (VHS), Can). Analysis of underlying motor cortex neural representations associated with the hand grasp states revealed an overlap or non-separability in neural activation patterns for similarly shaped objects that affected BCI-FES performance. These results suggest that motor cortex neural representations for functional grips are likely more related to hand shape and force required to hold objects, rather than to the objects themselves. These results, demonstrating multiple, naturalistic functional hand movements with the BCI-FES, constitute a further step toward translating BCI-FES technologies from research devices to clinical neuroprosthetics.
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Affiliation(s)
- Samuel C Colachis
- Medical Devices and Neuromodulation Group, Battelle Memorial Institute, Columbus, OH, United States.,Neurological Institute, The Ohio State University, Columbus, OH, United States.,Department of Biomedical Engineering, The Ohio State University, Columbus, OH, United States
| | - Marcie A Bockbrader
- Neurological Institute, The Ohio State University, Columbus, OH, United States.,Department of Biomedical Engineering, The Ohio State University, Columbus, OH, United States.,Department of Physical Medicine and Rehabilitation, The Ohio State University, Columbus, OH, United States
| | - Mingming Zhang
- Medical Devices and Neuromodulation Group, Battelle Memorial Institute, Columbus, OH, United States
| | - David A Friedenberg
- Advanced Analytics Group, Battelle Memorial Institute, Columbus, OH, United States
| | - Nicholas V Annetta
- Medical Devices and Neuromodulation Group, Battelle Memorial Institute, Columbus, OH, United States
| | - Michael A Schwemmer
- Advanced Analytics Group, Battelle Memorial Institute, Columbus, OH, United States
| | - Nicholas D Skomrock
- Advanced Analytics Group, Battelle Memorial Institute, Columbus, OH, United States
| | - Walter J Mysiw
- Neurological Institute, The Ohio State University, Columbus, OH, United States.,Department of Physical Medicine and Rehabilitation, The Ohio State University, Columbus, OH, United States
| | - Ali R Rezai
- Neurological Institute, The Ohio State University, Columbus, OH, United States
| | - Herbert S Bresler
- Medical Devices and Neuromodulation Group, Battelle Memorial Institute, Columbus, OH, United States
| | - Gaurav Sharma
- Medical Devices and Neuromodulation Group, Battelle Memorial Institute, Columbus, OH, United States
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33
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A Mosquito Inspired Strategy to Implant Microprobes into the Brain. Sci Rep 2018; 8:122. [PMID: 29317748 PMCID: PMC5760625 DOI: 10.1038/s41598-017-18522-4] [Citation(s) in RCA: 53] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2017] [Accepted: 12/13/2017] [Indexed: 02/05/2023] Open
Abstract
Mosquitos are among the deadliest insects on the planet due to their ability to transmit diseases like malaria through their bite. In order to bite, a mosquito must insert a set of micro-sized needles through the skin to reach vascular structures. The mosquito uses a combination of mechanisms including an insertion guide to enable it to bite and feed off of larger animals. Here, we report on a biomimetic strategy inspired by the mosquito insertion guide to enable the implantation of intracortical microelectrodes into the brain. Next generation microelectrode designs leveraging ultra-small dimensions and/or flexible materials offer the promise of increased performance, but present difficulties in reliable implantation. With the biomimetic guide in place, the rate of successful microprobe insertion increased from 37.5% to 100% due to the rise in the critical buckling force of the microprobes by 3.8-fold. The prototype guides presented here provide a reproducible method to augment the insertion of small, flexible devices into the brain. In the future, similar approaches may be considered and applied to the insertion of other difficult to implant medical devices.
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34
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Sensing and Decoding Neural Signals for Closed-Loop Neuromodulation and Advanced Diagnostics in Chronic Disease and Injury. Neuromodulation 2018. [DOI: 10.1016/b978-0-12-805353-9.00131-5] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
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35
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Bouton CE. Advances in Invasive Brain–Computer Interface Technology and Decoding Methods for Restoring Movement and Future Applications. Neuromodulation 2018. [DOI: 10.1016/b978-0-12-805353-9.00029-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/17/2022]
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36
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Abstract
Intracortical somatosensory interfaces have now entered the clinical domain. Darie et al. explore the implications of research published in Science Translational Medicine by Flesher et al. (2016), discuss how to design such a system given current technology, and question how to effectively communicate with users about their experience.
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Affiliation(s)
- Radu Darie
- Brown University School of Engineering, Brown University, Providence, RI 02912, USA
| | - Marc Powell
- Brown University School of Engineering, Brown University, Providence, RI 02912, USA
| | - David Borton
- Brown University School of Engineering, Brown University, Providence, RI 02912, USA; Brown Institute for Brain Science, Brown University, Providence, RI 02912, USA.
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37
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Friedenberg DA, Schwemmer MA, Landgraf AJ, Annetta NV, Bockbrader MA, Bouton CE, Zhang M, Rezai AR, Mysiw WJ, Bresler HS, Sharma G. Neuroprosthetic-enabled control of graded arm muscle contraction in a paralyzed human. Sci Rep 2017; 7:8386. [PMID: 28827605 PMCID: PMC5567199 DOI: 10.1038/s41598-017-08120-9] [Citation(s) in RCA: 49] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2017] [Accepted: 07/07/2017] [Indexed: 11/12/2022] Open
Abstract
Neuroprosthetics that combine a brain computer interface (BCI) with functional electrical stimulation (FES) can restore voluntary control of a patients’ own paralyzed limbs. To date, human studies have demonstrated an “all-or-none” type of control for a fixed number of pre-determined states, like hand-open and hand-closed. To be practical for everyday use, a BCI-FES system should enable smooth control of limb movements through a continuum of states and generate situationally appropriate, graded muscle contractions. Crucially, this functionality will allow users of BCI-FES neuroprosthetics to manipulate objects of different sizes and weights without dropping or crushing them. In this study, we present the first evidence that using a BCI-FES system, a human with tetraplegia can regain volitional, graded control of muscle contraction in his paralyzed limb. In addition, we show the critical ability of the system to generalize beyond training states and accurately generate wrist flexion states that are intermediate to training levels. These innovations provide the groundwork for enabling enhanced and more natural fine motor control of paralyzed limbs by BCI-FES neuroprosthetics.
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Affiliation(s)
- David A Friedenberg
- Advanced Analytics and Health Research, Battelle Memorial Institute, 505 King Avenue, Columbus, Ohio, 43201, USA.
| | - Michael A Schwemmer
- Advanced Analytics and Health Research, Battelle Memorial Institute, 505 King Avenue, Columbus, Ohio, 43201, USA
| | - Andrew J Landgraf
- Advanced Analytics and Health Research, Battelle Memorial Institute, 505 King Avenue, Columbus, Ohio, 43201, USA
| | - Nicholas V Annetta
- Medical Devices and Neuromodulation, Battelle Memorial Institute, 505 King Avenue, Columbus, Ohio, 43201, USA
| | - Marcia A Bockbrader
- Center for Neuromodulation, The Ohio State University, Columbus, Ohio, 43210, USA.,Department of Physical Medicine and Rehabilitation, The Ohio State University, Columbus, Ohio, 43210, USA
| | - Chad E Bouton
- Medical Devices and Neuromodulation, Battelle Memorial Institute, 505 King Avenue, Columbus, Ohio, 43201, USA.,Feinstein Institute for Medical Research, 350 Community Drive, Manhasset, New York, 11030, USA
| | - Mingming Zhang
- Medical Devices and Neuromodulation, Battelle Memorial Institute, 505 King Avenue, Columbus, Ohio, 43201, USA
| | - Ali R Rezai
- Center for Neuromodulation, The Ohio State University, Columbus, Ohio, 43210, USA.,Department of Neurological Surgery, The Ohio State University, Columbus, Ohio, 43210, USA
| | - W Jerry Mysiw
- Center for Neuromodulation, The Ohio State University, Columbus, Ohio, 43210, USA.,Department of Physical Medicine and Rehabilitation, The Ohio State University, Columbus, Ohio, 43210, USA
| | - Herbert S Bresler
- Medical Devices and Neuromodulation, Battelle Memorial Institute, 505 King Avenue, Columbus, Ohio, 43201, USA
| | - Gaurav Sharma
- Medical Devices and Neuromodulation, Battelle Memorial Institute, 505 King Avenue, Columbus, Ohio, 43201, USA
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Prochazka A. Neurophysiology and neural engineering: a review. J Neurophysiol 2017; 118:1292-1309. [PMID: 28566462 PMCID: PMC5558026 DOI: 10.1152/jn.00149.2017] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2017] [Revised: 05/30/2017] [Accepted: 05/30/2017] [Indexed: 12/19/2022] Open
Abstract
Neurophysiology is the branch of physiology concerned with understanding the function of neural systems. Neural engineering (also known as neuroengineering) is a discipline within biomedical engineering that uses engineering techniques to understand, repair, replace, enhance, or otherwise exploit the properties and functions of neural systems. In most cases neural engineering involves the development of an interface between electronic devices and living neural tissue. This review describes the origins of neural engineering, the explosive development of methods and devices commencing in the late 1950s, and the present-day devices that have resulted. The barriers to interfacing electronic devices with living neural tissues are many and varied, and consequently there have been numerous stops and starts along the way. Representative examples are discussed. None of this could have happened without a basic understanding of the relevant neurophysiology. I also consider examples of how neural engineering is repaying the debt to basic neurophysiology with new knowledge and insight.
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Affiliation(s)
- Arthur Prochazka
- Department of Physiology, University of Alberta, Edmonton, Alberta, Canada
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39
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Abstract
The human nervous system is a vast network carrying not only sensory and movement information, but also information to and from our organs, intimately linking it to our overall health. Scientists and engineers have been working for decades to tap into this network and 'crack the neural code' by decoding neural signals and learning how to 'speak' the language of the nervous system. Progress has been made in developing neural decoding methods to decipher brain activity and bioelectronic technologies to treat rheumatoid arthritis, paralysis, epilepsy and for diagnosing brain-related diseases such as Parkinson's and Alzheimer's disease. In a recent first-in-human study involving paralysis, a paralysed male study participant regained movement in his hand, years after his injury, through the use of a bioelectronic neural bypass. This work combined neural decoding and neurostimulation methods to translate and re-route signals around damaged neural pathways within the central nervous system. By extending these methods to decipher neural messages in the peripheral nervous system, status information from our bodily functions and specific organs could be gained. This, one day, could allow real-time diagnostics to be performed to give us a deeper insight into a patient's condition, or potentially even predict disease or allow early diagnosis. The future of bioelectronic medicine is extremely bright and is wide open as new diagnostic and treatment options are developed for patients around the world.
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Affiliation(s)
- C Bouton
- Feinstein Institute for Medical Research, Manhasset, NY, USA
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