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Liu Z, Xin H, Chopp M. Axonal remodeling of the corticospinal tract during neurological recovery after stroke. Neural Regen Res 2021; 16:939-943. [PMID: 33229733 PMCID: PMC8178784 DOI: 10.4103/1673-5374.297060] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022] Open
Abstract
Stroke remains the leading cause of long-term disability. Hemiparesis is one of the most common post-stroke motor deficits and is largely attributed to loss or disruption of the motor signals from the affected motor cortex. As the only direct descending motor pathway, the corticospinal tract (CST) is the primary pathway to innervate spinal motor neurons, and thus, forms the neuroanatomical basis to control the peripheral muscles for voluntary movements. Here, we review evidence from both experimental animals and stroke patients, regarding CST axonal damage, functional contribution of CST axonal integrity and remodeling to neurological recovery, and therapeutic approaches aimed to enhance CST axonal remodeling after stroke. The new insights gleaned from preclinical and clinical studies may encourage the development of more rational therapeutics with a strategy targeted to promote axonal rewiring for corticospinal innervation, which will significantly impact the current clinical needs of subacute and chronic stroke treatment.
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Affiliation(s)
- Zhongwu Liu
- Department of Neurology, Henry Ford Hospital, Detroit, MI, USA
| | - Hongqi Xin
- Department of Neurology, Henry Ford Hospital, Detroit, MI, USA
| | - Michael Chopp
- Department of Neurology, Henry Ford Hospital, Detroit; Department of Physics, Oakland University, Rochester, MI, USA
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202
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Jovanovic LI, Popovic MR, Marquez-Chin C. KITE-BCI: A brain-computer interface system for functional electrical stimulation therapy. J Spinal Cord Med 2021; 44:S203-S214. [PMID: 34779740 PMCID: PMC8648007 DOI: 10.1080/10790268.2021.1970895] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/24/2022] Open
Abstract
CONTEXT/OBJECTIVE Integrating brain-computer interface (BCI) technology with functional electrical stimulation therapy (FEST) is an emerging strategy for upper limb motor rehabilitation after spinal cord injury (SCI). Despite promising results, the combined use of these technologies (BCI-FEST) in clinical practice is minimal. To address this issue, we developed KITE-BCI, a BCI system specifically designed for clinical application and integration with dynamic FEST. In this paper, we report its technical features and performance. In addition, we discuss the differences in distributions of the BCI- and therapist-triggered stimulation latencies. DESIGN Two single-arm 40-session interventional studies to test the feasibility of BCI-controlled FEST for upper limb motor rehabilitation in individuals with cervical SCI. SETTING Rehabilitation programs within the University and Lyndhurst Centres of the Toronto Rehabilitation Institute - University Health Network, Toronto, Canada. PARTICIPANTS Five individuals with sub-acute (< 6 months post-injury) SCI at the C4-C5 level, AIS B-D, and three individuals with chronic (> 24 months post-injury) SCI at C4 level, AIS B-C. OUTCOME MEASURES We measured BCI setup duration, and to characterize the performance of KITE-BCI, we recorded BCI sensitivity, defined as the percentage of successful BCI activations out of the total number of cued movements. RESULTS The overall BCI sensitivities were 74.46% and 79.08% for the sub-acute and chronic groups, respectively. The average KITE-BCI setup duration across the two studies was 11 min and 13 s. CONCLUSION KITE-BCI demonstrates a clinically viable single-channel BCI system for integration with FEST resulting in a versatile technology-enhanced upper limb motor rehabilitation strategy after SCI.
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Affiliation(s)
- Lazar I. Jovanovic
- Institute of Biomedical Engineering, University of Toronto, Toronto, Ontario, Canada
- The KITE Research Institute, Toronto Rehabilitation Institute – University Health Network, Toronto, Ontario, Canada
- CRANIA, University Health Network, Toronto, Ontario, Canada
| | - Milos R. Popovic
- Institute of Biomedical Engineering, University of Toronto, Toronto, Ontario, Canada
- The KITE Research Institute, Toronto Rehabilitation Institute – University Health Network, Toronto, Ontario, Canada
- CRANIA, University Health Network, Toronto, Ontario, Canada
| | - Cesar Marquez-Chin
- Institute of Biomedical Engineering, University of Toronto, Toronto, Ontario, Canada
- The KITE Research Institute, Toronto Rehabilitation Institute – University Health Network, Toronto, Ontario, Canada
- CRANIA, University Health Network, Toronto, Ontario, Canada
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203
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Joy MT, Carmichael ST. Encouraging an excitable brain state: mechanisms of brain repair in stroke. Nat Rev Neurosci 2021; 22:38-53. [PMID: 33184469 PMCID: PMC10625167 DOI: 10.1038/s41583-020-00396-7] [Citation(s) in RCA: 105] [Impact Index Per Article: 35.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/04/2020] [Indexed: 02/02/2023]
Abstract
Stroke induces a plastic state in the brain. This period of enhanced plasticity leads to the sprouting of new axons, the formation of new synapses and the remapping of sensory-motor functions, and is associated with motor recovery. This is a remarkable process in the adult brain, which is normally constrained in its levels of neuronal plasticity and connectional change. Recent evidence indicates that these changes are driven by molecular systems that underlie learning and memory, such as changes in cellular excitability during memory formation. This Review examines circuit changes after stroke, the shared mechanisms between memory formation and brain repair, the changes in neuronal excitability that underlie stroke recovery, and the molecular and pharmacological interventions that follow from these findings to promote motor recovery in animal models. From these findings, a framework emerges for understanding recovery after stroke, central to which is the concept of neuronal allocation to damaged circuits. The translation of the concepts discussed here to recovery in humans is underway in clinical trials for stroke recovery drugs.
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Affiliation(s)
- Mary T Joy
- Department of Neurology, David Geffen School of Medicine, UCLA, Los Angeles, CA, USA
| | - S Thomas Carmichael
- Department of Neurology, David Geffen School of Medicine, UCLA, Los Angeles, CA, USA.
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204
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Jeunet C, Hauw D, Millán JDR. Sport Psychology: Technologies Ahead. Front Sports Act Living 2020; 2:10. [PMID: 33345005 PMCID: PMC7739689 DOI: 10.3389/fspor.2020.00010] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2019] [Accepted: 01/24/2020] [Indexed: 11/17/2022] Open
Affiliation(s)
- Camille Jeunet
- CLLE Lab, CNRS, Univ. Toulouse Jean Jaurès, Toulouse, France.,École Polytechnique Fédérale de Lausanne, Geneva, Switzerland
| | - Denis Hauw
- Institut des Sciences du Sport, Université de Lausanne, Lausanne, Switzerland
| | - Jose Del R Millán
- École Polytechnique Fédérale de Lausanne, Geneva, Switzerland.,Department of Electrical and Computer Engineering, The University of Texas at Austin, Austin, TX, United States.,Department of Neurology, The University of Texas at Austin, Austin, TX, United States
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205
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Chung E, Lee BH, Hwang S. Therapeutic effects of brain-computer interface-controlled functional electrical stimulation training on balance and gait performance for stroke: A pilot randomized controlled trial. Medicine (Baltimore) 2020; 99:e22612. [PMID: 33371056 PMCID: PMC7748200 DOI: 10.1097/md.0000000000022612] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/02/2020] [Accepted: 09/04/2020] [Indexed: 11/26/2022] Open
Abstract
BACKGROUND Brain-computer interface-controlled functional electrical stimulation (BCI-FES) approaches as new feedback training is increasingly being investigated for its usefulness in improving the health of adults or partially impaired upper extremity function in individuals with stroke. OBJECTIVE To evaluate the effects of BCI-FES on postural control and gait performance in individuals with chronic hemiparetic stroke. METHODS A total of 25 individuals with chronic hemiparetic stroke (13 individuals received BCI-FES and 12 individuals received functional electrical stimulation [FES]). The BCI-FES group received BCI-FES on the tibialis anterior muscle on the more-affected side for 30 minutes per session, 3 times per week for 5 weeks. The FES group received FES using the same methodology for the same periods. This study used the Mann-Whitney test to compare the two groups before and after training. RESULTS After training, gait velocity (mean value, 29.0 to 42.0 cm/s) (P = .002) and cadence (mean value, 65.2 to 78.9 steps/min) (P = .020) were significantly improved after BCI-FES training compared to those (mean value, 23.6 to 27.7 cm/s, and mean value, 59.4 to 65.5 steps/min, respectively) after FES approach. In the less-affected side, step length was significantly increased after BCI-FES (mean value, from 28.0 cm to 34.7 cm) more than that on FES approach (mean value, from 23.4 to 25.4 cm) (P = .031). CONCLUSION The results of the BCI-FES training shows potential advantages on walking abilities in individuals with chronic hemiparetic stroke.
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Affiliation(s)
- Eunjung Chung
- Department of Physical Therapy, Andong Science College, Andong-si
| | - Byoung-Hee Lee
- Department of Physical Therapy, College of Health and Welfare, Sahmyook University, Seoul
| | - Sujin Hwang
- Department of Physical Therapy, Division of Health Science, Baekseok University, Cheonan-si, Republic of Korea
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206
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Miao Y, Chen S, Zhang X, Jin J, Xu R, Daly I, Jia J, Wang X, Cichocki A, Jung TP. BCI-Based Rehabilitation on the Stroke in Sequela Stage. Neural Plast 2020; 2020:8882764. [PMID: 33414824 PMCID: PMC7752268 DOI: 10.1155/2020/8882764] [Citation(s) in RCA: 37] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2020] [Revised: 11/25/2020] [Accepted: 11/30/2020] [Indexed: 11/24/2022] Open
Abstract
Background Stroke is the leading cause of serious and long-term disability worldwide. Survivors may recover some motor functions after rehabilitation therapy. However, many stroke patients missed the best time period for recovery and entered into the sequela stage of chronic stroke. Method Studies have shown that motor imagery- (MI-) based brain-computer interface (BCI) has a positive effect on poststroke rehabilitation. This study used both virtual limbs and functional electrical stimulation (FES) as feedback to provide patients with a closed-loop sensorimotor integration for motor rehabilitation. An MI-based BCI system acquired, analyzed, and classified motor attempts from electroencephalogram (EEG) signals. The FES system would be activated if the BCI detected that the user was imagining wrist dorsiflexion on the instructed side of the body. Sixteen stroke patients in the sequela stage were randomly assigned to a BCI group and a control group. All of them participated in rehabilitation training for four weeks and were assessed by the Fugl-Meyer Assessment (FMA) of motor function. Results The average improvement score of the BCI group was 3.5, which was higher than that of the control group (0.9). The active EEG patterns of the four patients in the BCI group whose FMA scores increased gradually became centralized and shifted to sensorimotor areas and premotor areas throughout the study. Conclusions Study results showed evidence that patients in the BCI group achieved larger functional improvements than those in the control group and that the BCI-FES system is effective in restoring motor function to upper extremities in stroke patients. This study provides a more autonomous approach than traditional treatments used in stroke rehabilitation.
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Affiliation(s)
- Yangyang Miao
- Key Laboratory of Advanced Control and Optimization for Chemical Processes, Ministry of Education, East China University of Science and Technology, Shanghai, China
| | - Shugeng Chen
- Department of Rehabilitation, Huashan Hospital, Fudan University, Shanghai, China
| | - Xinru Zhang
- Key Laboratory of Advanced Control and Optimization for Chemical Processes, Ministry of Education, East China University of Science and Technology, Shanghai, China
| | - Jing Jin
- Key Laboratory of Advanced Control and Optimization for Chemical Processes, Ministry of Education, East China University of Science and Technology, Shanghai, China
| | - Ren Xu
- Guger Technologies OG, Austria
| | - Ian Daly
- Brain-Computer Interfaces and Neural Engineering Laboratory, School of Computer Science and Electronic Engineering, University of Essex, Colchester, Essex CO4 3SQ, UK
| | - Jie Jia
- Department of Rehabilitation, Huashan Hospital, Fudan University, Shanghai, China
| | - Xingyu Wang
- Key Laboratory of Advanced Control and Optimization for Chemical Processes, Ministry of Education, East China University of Science and Technology, Shanghai, China
| | - Andrzej Cichocki
- Skolkowo Institute of Science and Technology (SKOLTECH), 143026 Moscow, Russia
- Systems Research Institute PAS, Warsaw, Poland
- Nicolaus Copernicus University (UMK), Torun, Poland
| | - Tzyy-Ping Jung
- Institute for Neural Computation and Institute of Engineering in Medicine, University of California San Diego, La Jolla, CA, USA
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207
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Cheng N, Phua KS, Lai HS, Tam PK, Tang KY, Cheng KK, Yeow RCH, Ang KK, Guan C, Lim JH. Brain-Computer Interface-Based Soft Robotic Glove Rehabilitation for Stroke. IEEE Trans Biomed Eng 2020; 67:3339-3351. [DOI: 10.1109/tbme.2020.2984003] [Citation(s) in RCA: 36] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
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208
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Bhagat NA, Yozbatiran N, Sullivan JL, Paranjape R, Losey C, Hernandez Z, Keser Z, Grossman R, Francisco GE, O'Malley MK, Contreras-Vidal JL. Neural activity modulations and motor recovery following brain-exoskeleton interface mediated stroke rehabilitation. NEUROIMAGE-CLINICAL 2020; 28:102502. [PMID: 33395991 PMCID: PMC7749405 DOI: 10.1016/j.nicl.2020.102502] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/14/2020] [Revised: 10/28/2020] [Accepted: 11/09/2020] [Indexed: 01/03/2023]
Abstract
Motor intention based arm training targets activity-dependent neuroplasticity. 80% of stroke participants recovered clinically relevant functional movements. Ipsi-lesional, delta-band EEG activity was highly correlated with motor recovery. Results suggest higher activation of ipsi-lesional hemisphere post-intervention.
Brain-machine interfaces (BMI) based on scalp EEG have the potential to promote cortical plasticity following stroke, which has been shown to improve motor recovery outcomes. However, the efficacy of BMI enabled robotic training for upper-limb recovery is seldom quantified using clinical, EEG-based, and kinematics-based metrics. Further, a movement related neural correlate that can predict the extent of motor recovery still remains elusive, which impedes the clinical translation of BMI-based stroke rehabilitation. To address above knowledge gaps, 10 chronic stroke individuals with stable baseline clinical scores were recruited to participate in 12 therapy sessions involving a BMI enabled powered exoskeleton for elbow training. On average, 132 ± 22 repetitions were performed per participant, per session. BMI accuracy across all sessions and subjects was 79 ± 18% with a false positives rate of 23 ± 20%. Post-training clinical assessments found that FMA for upper extremity and ARAT scores significantly improved over baseline by 3.92 ± 3.73 and 5.35 ± 4.62 points, respectively. Also, 80% participants (7 with moderate-mild impairment, 1 with severe impairment) achieved minimal clinically important difference (MCID: FMA-UE >5.2 or ARAT >5.7) during the course of the study. Kinematic measures indicate that, on average, participants’ movements became faster and smoother. Moreover, modulations in movement related cortical potentials, an EEG-based neural correlate measured contralateral to the impaired arm, were significantly correlated with ARAT scores (ρ = 0.72, p < 0.05) and marginally correlated with FMA-UE (ρ = 0.63, p = 0.051). This suggests higher activation of ipsi-lesional hemisphere post-intervention or inhibition of competing contra-lesional hemisphere, which may be evidence of neuroplasticity and cortical reorganization following BMI mediated rehabilitation therapy.
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Affiliation(s)
- Nikunj A Bhagat
- Non-Invasive Brain Machine Interface Systems Laboratory, University of Houston, Houston, TX 77004, USA.
| | - Nuray Yozbatiran
- Department of Physical Medicine and Rehabilitation, McGovern Medical School, NeuroRecovery Research Center at TIRR Memorial Hermann, University of Texas Health Science Center at Houston, TX 77030, USA
| | - Jennifer L Sullivan
- Mechatronics and Haptic Interfaces Laboratory, Rice University, Houston, TX 77005, USA
| | - Ruta Paranjape
- Department of Physical Medicine and Rehabilitation, McGovern Medical School, NeuroRecovery Research Center at TIRR Memorial Hermann, University of Texas Health Science Center at Houston, TX 77030, USA
| | - Colin Losey
- Mechatronics and Haptic Interfaces Laboratory, Rice University, Houston, TX 77005, USA
| | - Zachary Hernandez
- Non-Invasive Brain Machine Interface Systems Laboratory, University of Houston, Houston, TX 77004, USA
| | - Zafer Keser
- Department of Physical Medicine and Rehabilitation, McGovern Medical School, NeuroRecovery Research Center at TIRR Memorial Hermann, University of Texas Health Science Center at Houston, TX 77030, USA
| | - Robert Grossman
- Houston Methodist Research Institute, Houston, TX 77030, USA
| | - Gerard E Francisco
- Department of Physical Medicine and Rehabilitation, McGovern Medical School, NeuroRecovery Research Center at TIRR Memorial Hermann, University of Texas Health Science Center at Houston, TX 77030, USA
| | - Marcia K O'Malley
- Department of Physical Medicine and Rehabilitation, McGovern Medical School, NeuroRecovery Research Center at TIRR Memorial Hermann, University of Texas Health Science Center at Houston, TX 77030, USA; Mechatronics and Haptic Interfaces Laboratory, Rice University, Houston, TX 77005, USA
| | - Jose L Contreras-Vidal
- Non-Invasive Brain Machine Interface Systems Laboratory, University of Houston, Houston, TX 77004, USA; Houston Methodist Research Institute, Houston, TX 77030, USA; NSF IUCRC BRAIN, University of Houston, Houston, TX 77004, USA
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209
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Iwama S, Tsuchimoto S, Hayashi M, Mizuguchi N, Ushiba J. Scalp electroencephalograms over ipsilateral sensorimotor cortex reflect contraction patterns of unilateral finger muscles. Neuroimage 2020; 222:117249. [PMID: 32798684 DOI: 10.1016/j.neuroimage.2020.117249] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2020] [Revised: 08/02/2020] [Accepted: 08/06/2020] [Indexed: 12/17/2022] Open
Abstract
A variety of neural substrates are implicated in the initiation, coordination, and stabilization of voluntary movements underpinned by adaptive contraction and relaxation of agonist and antagonist muscles. To achieve such flexible and purposeful control of the human body, brain systems exhibit extensive modulation during the transition from resting state to motor execution and to maintain proper joint impedance. However, the neural structures contributing to such sensorimotor control under unconstrained and naturalistic conditions are not fully characterized. To elucidate which brain regions are implicated in generating and coordinating voluntary movements, we employed a physiologically inspired, two-stage method to decode relaxation and three patterns of contraction in unilateral finger muscles (i.e., extension, flexion, and co-contraction) from high-density scalp electroencephalograms (EEG). The decoder consisted of two parts employed in series. The first discriminated between relaxation and contraction. If the EEG data were discriminated as contraction, the second stage then discriminated among the three contraction patterns. Despite the difficulty in dissociating detailed contraction patterns of muscles within a limb from scalp EEG signals, the decoder performance was higher than chance-level by 2-fold in the four-class classification. Moreover, weighted features in the trained decoders revealed EEG features differentially contributing to decoding performance. During the first stage, consistent with previous reports, weighted features were localized around sensorimotor cortex (SM1) contralateral to the activated fingers, while those during the second stage were localized around ipsilateral SM1. The loci of these weighted features suggested that the coordination of unilateral finger muscles induced different signaling patterns in ipsilateral SM1 contributing to motor control. Weighted EEG features enabled a deeper understanding of human sensorimotor processing as well as of a more naturalistic control of brain-computer interfaces.
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Affiliation(s)
- Seitaro Iwama
- School of Fundamental Science and Technology, Graduate School of Keio University, Kanagawa, Japan
| | - Shohei Tsuchimoto
- School of Fundamental Science and Technology, Graduate School of Keio University, Kanagawa, Japan; Center of Assistive Robotics and Rehabilitation for Longevity and Good Health, National Center for Geriatrics and Gerontology, Aichi, Japan
| | - Masaaki Hayashi
- School of Fundamental Science and Technology, Graduate School of Keio University, Kanagawa, Japan
| | - Nobuaki Mizuguchi
- Center of Assistive Robotics and Rehabilitation for Longevity and Good Health, National Center for Geriatrics and Gerontology, Aichi, Japan; Department of Biosciences and informatics, Faculty of Science and Technology, Keio University, 3-14-1 Hiyoshi, Kouhoku-ku, Yokohama, Kanagawa 223-8522, Japan
| | - Junichi Ushiba
- Department of Biosciences and informatics, Faculty of Science and Technology, Keio University, 3-14-1 Hiyoshi, Kouhoku-ku, Yokohama, Kanagawa 223-8522, Japan.
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210
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Yuan K, Wang X, Chen C, Lau CCY, Chu WCW, Tong RKY. Interhemispheric Functional Reorganization and its Structural Base After BCI-Guided Upper-Limb Training in Chronic Stroke. IEEE Trans Neural Syst Rehabil Eng 2020; 28:2525-2536. [PMID: 32997632 DOI: 10.1109/tnsre.2020.3027955] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Brain-computer interface (BCI)-guided robot-assisted upper-limb training has been increasingly applied to stroke rehabilitation. However, the induced long-term neuroplasticity modulation still needs to be further characterized. This study investigated the functional reorganization and its structural base after BCI-guided robot-assisted training using resting-state fMRI, task-based fMRI, and diffusion tensor imaging (DTI) data. The clinical improvement and the neurological changes before, immediately after, and six months after 20-session BCI-guided robot hand training were explored in 14 chronic stroke subjects. The structural base of the induced functional reorganization and motor improvement were also investigated using DTI. Repeated measure ANOVA indicated long-term motor improvement was found (F[2, 26] = 6.367, p = 0.006). Significantly modulated functional connectivity (FC) was observed between ipsilesional motor regions (M1 and SMA) and some contralesional areas (SMA, PMd, SPL) in the seed-based analysis. Modulated FC with ipsilesional M1 was significantly correlated with motor function improvement (r = 0.6455, p = 0.0276). Besides, increased interhemispheric FC among the sensorimotor area from resting-state data and increased laterality index from task-based data together indicated the re-balance of the two hemispheres during the recovery. Multiple linear regression models suggested that both motor function improvement and the functional change between ipsilesional M1 and contralesional premotor area were significantly associated with the ipsilesional corticospinal tract integrity. The results in the current study provided solid support for stroke recovery mechanism in terms of interhemispheric interaction and its structural substrates, which could further enhance the understanding of BCI training in stroke rehabilitation. This study was registered at https://clinicaltrials.gov (NCT02323061).
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211
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Milosevic M, Marquez-Chin C, Masani K, Hirata M, Nomura T, Popovic MR, Nakazawa K. Why brain-controlled neuroprosthetics matter: mechanisms underlying electrical stimulation of muscles and nerves in rehabilitation. Biomed Eng Online 2020; 19:81. [PMID: 33148270 PMCID: PMC7641791 DOI: 10.1186/s12938-020-00824-w] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2020] [Accepted: 10/10/2020] [Indexed: 12/11/2022] Open
Abstract
Delivering short trains of electric pulses to the muscles and nerves can elicit action potentials resulting in muscle contractions. When the stimulations are sequenced to generate functional movements, such as grasping or walking, the application is referred to as functional electrical stimulation (FES). Implications of the motor and sensory recruitment of muscles using FES go beyond simple contraction of muscles. Evidence suggests that FES can induce short- and long-term neurophysiological changes in the central nervous system by varying the stimulation parameters and delivery methods. By taking advantage of this, FES has been used to restore voluntary movement in individuals with neurological injuries with a technique called FES therapy (FEST). However, long-lasting cortical re-organization (neuroplasticity) depends on the ability to synchronize the descending (voluntary) commands and the successful execution of the intended task using a FES. Brain-computer interface (BCI) technologies offer a way to synchronize cortical commands and movements generated by FES, which can be advantageous for inducing neuroplasticity. Therefore, the aim of this review paper is to discuss the neurophysiological mechanisms of electrical stimulation of muscles and nerves and how BCI-controlled FES can be used in rehabilitation to improve motor function.
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Affiliation(s)
- Matija Milosevic
- Graduate School of Engineering Science, Department of Mechanical Science and Bioengineering, Osaka University, 1-3 Machikaneyama-cho, Toyonaka, Osaka, 560-8531, Japan.
| | - Cesar Marquez-Chin
- Institute of Biomedical Engineering, University of Toronto, 164 College Street, Toronto, ON, M5S 3G9, Canada
- KITE Research Institute, Toronto Rehabilitation Institute - University Health Network, 520 Sutherland Drive, Toronto, ON, M4G 3V9, Canada
- CRANIA, University Health Network & University of Toronto, 550 University Avenue, Toronto, ON, M5G 2A2, Canada
| | - Kei Masani
- Institute of Biomedical Engineering, University of Toronto, 164 College Street, Toronto, ON, M5S 3G9, Canada
- KITE Research Institute, Toronto Rehabilitation Institute - University Health Network, 520 Sutherland Drive, Toronto, ON, M4G 3V9, Canada
- CRANIA, University Health Network & University of Toronto, 550 University Avenue, Toronto, ON, M5G 2A2, Canada
| | - Masayuki Hirata
- Department of Neurological Diagnosis and Restoration, Graduate School of Medicine, Osaka University, 2-2 Yamadaoka, Suita, Osaka, 565-0871, Japan
| | - Taishin Nomura
- Graduate School of Engineering Science, Department of Mechanical Science and Bioengineering, Osaka University, 1-3 Machikaneyama-cho, Toyonaka, Osaka, 560-8531, Japan
| | - Milos R Popovic
- Institute of Biomedical Engineering, University of Toronto, 164 College Street, Toronto, ON, M5S 3G9, Canada
- KITE Research Institute, Toronto Rehabilitation Institute - University Health Network, 520 Sutherland Drive, Toronto, ON, M4G 3V9, Canada
- CRANIA, University Health Network & University of Toronto, 550 University Avenue, Toronto, ON, M5G 2A2, Canada
| | - Kimitaka Nakazawa
- Department of Life Sciences, Graduate School of Arts and Sciences, University of Tokyo, 3-8-1 Komaba, Meguro, Tokyo, 153-8902, Japan
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Kruse A, Suica Z, Taeymans J, Schuster-Amft C. Effect of brain-computer interface training based on non-invasive electroencephalography using motor imagery on functional recovery after stroke - a systematic review and meta-analysis. BMC Neurol 2020; 20:385. [PMID: 33092554 PMCID: PMC7584076 DOI: 10.1186/s12883-020-01960-5] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2020] [Accepted: 10/14/2020] [Indexed: 01/25/2023] Open
Abstract
Background Training with brain-computer interface (BCI) technology in the rehabilitation of patients after a stroke is rapidly developing. Numerous RCT investigated the effects of BCI training (BCIT) on recovery of motor and brain function in patients after stroke. Methods A systematic literature search was performed in Medline, IEEE Xplore Digital Library, Cochrane library, and Embase in July 2018 and was repeated in March 2019. RCT or controlled clinical trials that included BCIT for improving motor and brain recovery in patients after a stroke were identified. Data were meta-analysed using the random-effects model. Standardized mean difference (SMD) with 95% confidence (95%CI) and 95% prediction interval (95%PI) were calculated. A meta-regression was performed to evaluate the effects of covariates on the pooled effect-size. Results In total, 14 studies, including 362 patients after ischemic and hemorrhagic stroke (cortical, subcortical, 121 females; mean age 53.0+/− 5.8; mean time since stroke onset 15.7+/− 18.2 months) were included. Main motor recovery outcome measure used was the Fugl-Meyer Assessment. Quantitative analysis showed that a BCI training compared to conventional therapy alone in patients after stroke was effective with an SMD of 0.39 (95%CI: 0.17 to 0.62; 95%PI of 0.13 to 0.66) for motor function recovery of the upper extremity. An SMD of 0.41 (95%CI: − 0.29 to 1.12) for motor function recovery of the lower extremity was found. BCI training enhanced brain function recovery with an SMD of 1.11 (95%CI: 0.64 to 1.59; 95%PI ranging from 0.33 to 1.89). Covariates such as training duration, impairment level of the upper extremity, and the combination of both did not show significant effects on the overall pooled estimate. Conclusion This meta-analysis showed evidence that BCI training added to conventional therapy may enhance motor functioning of the upper extremity and brain function recovery in patients after a stroke. We recommend a standardised evaluation of motor imagery ability of included patients and the assessment of brain function recovery should consider neuropsychological aspects (attention, concentration). Further influencing factors on motor recovery due to BCI technology might consider factors such as age, lesion type and location, quality of performance of motor imagery, or neuropsychological aspects. Trial Registration PROSPERO registration: CRD42018105832. Supplementary information Supplementary information accompanies this paper at 10.1186/s12883-020-01960-5.
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Affiliation(s)
- Antje Kruse
- Department of Health Professions, Bern University Applied Science, Schwarztorstrasse 48, 3007, Bern, Switzerland.,Private Practice, Baslerstrasse 60, 4102, Binningen, Switzerland
| | - Zorica Suica
- Research Department, Reha Rheinfelden, Salinenstrasse 98, 4310, Rheinfelden, Switzerland
| | - Jan Taeymans
- Department of Health Professions, Bern University Applied Science, Schwarztorstrasse 48, 3007, Bern, Switzerland.,Vrije Universiteit Brussel, Pleinlaan 2, 1050, Brussels, Belgium
| | - Corina Schuster-Amft
- Research Department, Reha Rheinfelden, Salinenstrasse 98, 4310, Rheinfelden, Switzerland. .,Department of Engineering and Information Technology, Pestalozzistrasse 20, 3401, Burgdorf, Switzerland. .,Department of Sport, Exercise and Health, University of Basel, Birsstrasse 320 B, 4052, Basel, Switzerland.
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213
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Sebastián-Romagosa M, Cho W, Ortner R, Murovec N, Von Oertzen T, Kamada K, Allison BZ, Guger C. Brain Computer Interface Treatment for Motor Rehabilitation of Upper Extremity of Stroke Patients-A Feasibility Study. Front Neurosci 2020; 14:591435. [PMID: 33192277 PMCID: PMC7640937 DOI: 10.3389/fnins.2020.591435] [Citation(s) in RCA: 46] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2020] [Accepted: 09/10/2020] [Indexed: 12/21/2022] Open
Abstract
Introduction Numerous recent publications have explored Brain Computer Interfaces (BCI) systems as rehabilitation tools to help subacute and chronic stroke patients recover upper extremity movement. Recent work has shown that BCI therapy can lead to better outcomes than conventional therapy. BCI combined with other techniques such as Functional Electrical Stimulation (FES) and Virtual Reality (VR) allows to the user restore the neurological function by inducing the neural plasticity through improved real-time detection of motor imagery (MI) as patients perform therapy tasks. Methods Fifty-one stroke patients with upper extremity hemiparesis were recruited for this study. All participants performed 25 sessions with the MI BCI and assessment visits to track the functional changes before and after the therapy. Results The results of this study demonstrated a significant increase in the motor function of the paretic arm assessed by Fugl-Meyer Assessment (FMA-UE), ΔFMA-UE = 4.68 points, P < 0.001, reduction of the spasticity in the wrist and fingers assessed by Modified Ashworth Scale (MAS), ΔMAS-wrist = -0.72 points (SD = 0.83), P < 0.001, ΔMAS-fingers = -0.63 points (SD = 0.82), P < 0.001. Other significant improvements in the grasp ability were detected in the healthy hand. All these functional improvements achieved during the BCI therapy persisted 6 months after the therapy ended. Results also showed that patients with Motor Imagery accuracy (MI) above 80% increase 3.16 points more in the FMA than patients below this threshold (95% CI; [1.47–6.62], P = 0.003). The functional improvement was not related with the stroke severity or with the stroke stage. Conclusion The BCI treatment used here was effective in promoting long lasting functional improvements in the upper extremity in stroke survivors with severe, moderate and mild impairment. This functional improvement can be explained by improved neuroplasticity in the central nervous system.
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Affiliation(s)
| | - Woosang Cho
- g.tec Medical Engineering GmbH, Schiedlberg, Austria.,Institute of Medical Psychology and Behavioral Neurobiology, University of Tübingen, Tübingen, Germany.,International Max Planck Research School for Neural & Behavioral Sciences, Tübingen, Germany
| | - Rupert Ortner
- g.tec Medical Engineering Spain SL, Barcelona, Spain
| | - Nensi Murovec
- g.tec Medical Engineering GmbH, Schiedlberg, Austria
| | - Tim Von Oertzen
- Department of Neurology 1, Kepler Universitätsklinik, Linz, Austria
| | | | - Brendan Z Allison
- Department of Cognitive Science, University of California, San Diego, San Diego, CA, United States
| | - Christoph Guger
- g.tec Medical Engineering Spain SL, Barcelona, Spain.,g.tec Medical Engineering GmbH, Schiedlberg, Austria
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214
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Guggenberger R, Raco V, Gharabaghi A. State-Dependent Gain Modulation of Spinal Motor Output. Front Bioeng Biotechnol 2020; 8:523866. [PMID: 33117775 PMCID: PMC7561675 DOI: 10.3389/fbioe.2020.523866] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2019] [Accepted: 09/17/2020] [Indexed: 01/04/2023] Open
Abstract
Afferent somatosensory information plays a crucial role in modulating efferent motor output. A better understanding of this sensorimotor interplay may inform the design of neurorehabilitation interfaces. Current neurotechnological approaches that address motor restoration after trauma or stroke combine motor imagery (MI) and contingent somatosensory feedback, e.g., via peripheral stimulation, to induce corticospinal reorganization. These interventions may, however, change the motor output already at the spinal level dependent on alterations of the afferent input. Neuromuscular electrical stimulation (NMES) was combined with measurements of wrist deflection using a kinematic glove during either MI or rest. We investigated 360 NMES bursts to the right forearm of 12 healthy subjects at two frequencies (30 and 100 Hz) in random order. For each frequency, stimulation was assessed at nine intensities. Measuring the induced wrist deflection across different intensities allowed us to estimate the input-output curve (IOC) of the spinal motor output. MI decreased the slope of the IOC independent of the stimulation frequency. NMES with 100 Hz vs. 30 Hz decreased the threshold of the IOC. Human-machine interfaces for neurorehabilitation that combine MI and NMES need to consider bidirectional communication and may utilize the gain modulation of spinal circuitries by applying low-intensity, high-frequency stimulation.
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Affiliation(s)
- Robert Guggenberger
- Institute for Neuromodulation and Neurotechnology, Department of Neurosurgery and Neurotechnology, University of Tüebingen, Tüebingen, Germany
| | - Valerio Raco
- Institute for Neuromodulation and Neurotechnology, Department of Neurosurgery and Neurotechnology, University of Tüebingen, Tüebingen, Germany
| | - Alireza Gharabaghi
- Institute for Neuromodulation and Neurotechnology, Department of Neurosurgery and Neurotechnology, University of Tüebingen, Tüebingen, Germany
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215
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Draaisma LR, Wessel MJ, Hummel FC. Neurotechnologies as tools for cognitive rehabilitation in stroke patients. Expert Rev Neurother 2020; 20:1249-1261. [DOI: 10.1080/14737175.2020.1820324] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
Affiliation(s)
- Laurijn R. Draaisma
- Defitech Chair of Clinical Neuroengineering, Center for Neuroprosthetics (CNP) and Brain Mind Institute (BMI), Swiss Federal Institute of Technology (EPFL), Geneva, Switzerland
- Defitech Chair of Clinical Neuroengineering, Clinique Romande de Réadaptation, Center for Neuroprosthetics (CNP) and Brain Mind Institute (BMI, Swiss Federal Institute of Technology (EPFL Valais), Sion, Switzerland
| | - Maximilian J. Wessel
- Defitech Chair of Clinical Neuroengineering, Center for Neuroprosthetics (CNP) and Brain Mind Institute (BMI), Swiss Federal Institute of Technology (EPFL), Geneva, Switzerland
- Defitech Chair of Clinical Neuroengineering, Clinique Romande de Réadaptation, Center for Neuroprosthetics (CNP) and Brain Mind Institute (BMI, Swiss Federal Institute of Technology (EPFL Valais), Sion, Switzerland
| | - Friedhelm C. Hummel
- Defitech Chair of Clinical Neuroengineering, Center for Neuroprosthetics (CNP) and Brain Mind Institute (BMI), Swiss Federal Institute of Technology (EPFL), Geneva, Switzerland
- Defitech Chair of Clinical Neuroengineering, Clinique Romande de Réadaptation, Center for Neuroprosthetics (CNP) and Brain Mind Institute (BMI, Swiss Federal Institute of Technology (EPFL Valais), Sion, Switzerland
- Clinical Neuroscience, University of Geneva Medical School, Geneva, Switzerland
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216
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Krueger J, Reichert C, Dürschmid S, Krauth R, Vogt S, Huchtemann T, Lindquist S, Lamprecht J, Sailer M, Heinze HJ, Hinrichs H, Sweeney-Reed CM. Rehabilitation nach Schlaganfall: Durch Gehirn-Computer-Schnittstelle
vermittelte funktionelle Elektrostimulation. KLIN NEUROPHYSIOL 2020. [DOI: 10.1055/a-1205-7467] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
Abstract
ZusammenfassungEine Gehirn-Computer-Schnittstelle (BCI) in der Rehabilitation von
Schlaganfallpatienten ermöglicht die Steuerung einer funktionellen
Elektrostimulation (FES), um eine Muskelkontraktion in der gelähmten
Extremität zum Zeitpunkt der Bewegungsintention durch Erkennung
entsprechender Hirnsignale auszulösen. Es wird angenommen, dass eine
genaue zeitliche Kohärenz zwischen Bewegungsintention und visuellem
sowie propriozeptivem Feedback, ausgelöst durch eine reale Bewegung,
neuroplastische Prozesse begünstigen und eine funktionelle
Verbesserung der Parese bewirken kann. In dieser systematischen
Übersichtsarbeit zu randomisierten kontrollierten Studien wurden die
Datenbanken Pubmed, Scopus und Web of Science durchsucht und von 516
berücksichtigten Publikationen 13 ausgewählt, die auf 7
Studienpopulationen basierten. Ein direkter Vergleich der Studien ist durch
Unterschiede im Studiendesign erschwert. Fünf Studien berichten von
einer verbesserten motorischen Funktion in der BCI-FES-Gruppe, davon zeigen
3 signifikante Unterschiede zwischen der BCI-FES- und der
Kontrollgruppe.
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Affiliation(s)
- Johanna Krueger
- Neurokybernetik und Rehabilitation, Klinik für Neurologie,
Otto-von-Guericke Universität, Magdeburg
- Krankenhaus Barmherziger Brüder Regensburg
| | - Christoph Reichert
- Abteilung Verhaltensneurologie, Leibniz Institut für
Neurobiologie (LIN), Magdeburg
| | - Stefan Dürschmid
- Abteilung Verhaltensneurologie, Leibniz Institut für
Neurobiologie (LIN), Magdeburg
| | - Richard Krauth
- Neurokybernetik und Rehabilitation, Klinik für Neurologie,
Otto-von-Guericke Universität, Magdeburg
| | - Susanne Vogt
- Klinik für Neurologie, Otto-von-Guericke Universität,
Magdeburg
| | | | | | - Juliane Lamprecht
- MEDIAN Klinik NRZ Magdeburg, MEDIAN Klinik Flechtingen
- An-Institut für Neurorehabilitation, Otto-von-Guericke
Universität, Magdeburg
| | - Michael Sailer
- MEDIAN Klinik NRZ Magdeburg, MEDIAN Klinik Flechtingen
- An-Institut für Neurorehabilitation, Otto-von-Guericke
Universität, Magdeburg
| | - Hans-Jochen Heinze
- Abteilung Verhaltensneurologie, Leibniz Institut für
Neurobiologie (LIN), Magdeburg
- Klinik für Neurologie, Otto-von-Guericke Universität,
Magdeburg
- Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE),
Magdeburg
| | - Hermann Hinrichs
- Abteilung Verhaltensneurologie, Leibniz Institut für
Neurobiologie (LIN), Magdeburg
- Klinik für Neurologie, Otto-von-Guericke Universität,
Magdeburg
- Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE),
Magdeburg
- Center for Behavioral Brain Sciences (CBBS), Magdeburg
- Forschungscampus STIMULATE, Magdeburg
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217
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Chou CH, Wang T, Sun X, Niu CM, Hao M, Xie Q, Lan N. Automated functional electrical stimulation training system for upper-limb function recovery in poststroke patients. Med Eng Phys 2020; 84:174-183. [PMID: 32977916 DOI: 10.1016/j.medengphy.2020.09.001] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2020] [Revised: 08/24/2020] [Accepted: 09/02/2020] [Indexed: 11/24/2022]
Abstract
BACKGROUND This paper describes the design and test of an automated functional electrical stimulation (FES) system for poststroke rehabilitation training. The aim of automated FES is to synchronize electrically induced movements to assist residual movements of patients. METHODS In the design of the FES system, an accelerometry module detected movement initiation and movement performed by post-stroke patients. The desired movement was displayed in visual game module. Synergy-based FES patterns were formulated using a normal pattern of muscle synergies from a healthy subject. Experiment 1 evaluated how different levels of trigger threshold or timing affected the variability of compound movements for forward reaching (FR) and lateral reaching (LR). Experiment 2 explored the effect of FES duration on compound movements. RESULTS Synchronizing FES-assisted movements with residual voluntary movements produced more consistent compound movements. Matching the duration of synergy-based FES to that of patients could assist slower movements of patients with reduced RMS errors. CONCLUSIONS Evidence indicated that synchronization and matching duration with residual voluntary movements of patients could improve the consistency of FES assisted movements. Automated FES training can reduce the burden of therapists to monitor the training process, which may encourage patients to complete the training.
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Affiliation(s)
- Chih-Hong Chou
- Laboratory of Neurorehabilitaiton Engineering, School of Biomedical Engineering, Shanghai Jiao Tong University, China
| | - Tong Wang
- Laboratory of Neurorehabilitaiton Engineering, School of Biomedical Engineering, Shanghai Jiao Tong University, China
| | - Xiaopei Sun
- Department of Rehabilitation Medicine, Ruijin Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Chuanxin M Niu
- Laboratory of Neurorehabilitaiton Engineering, School of Biomedical Engineering, Shanghai Jiao Tong University, China; Department of Rehabilitation Medicine, Ruijin Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Manzhao Hao
- Laboratory of Neurorehabilitaiton Engineering, School of Biomedical Engineering, Shanghai Jiao Tong University, China
| | - Qing Xie
- Department of Rehabilitation Medicine, Ruijin Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China.
| | - Ning Lan
- Laboratory of Neurorehabilitaiton Engineering, School of Biomedical Engineering, Shanghai Jiao Tong University, China.
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218
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Pillette L, Lotte F, N'Kaoua B, Joseph PA, Jeunet C, Glize B. Why we should systematically assess, control and report somatosensory impairments in BCI-based motor rehabilitation after stroke studies. Neuroimage Clin 2020; 28:102417. [PMID: 33039972 PMCID: PMC7551360 DOI: 10.1016/j.nicl.2020.102417] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2020] [Revised: 08/22/2020] [Accepted: 09/03/2020] [Indexed: 11/25/2022]
Abstract
The neuronal loss resulting from stroke forces 80% of the patients to undergo motor rehabilitation, for which Brain-Computer Interfaces (BCIs) and NeuroFeedback (NF) can be used. During the rehabilitation, when patients attempt or imagine performing a movement, BCIs/NF provide them with a synchronized sensory (e.g., tactile) feedback based on their sensorimotor-related brain activity that aims at fostering brain plasticity and motor recovery. The co-activation of ascending (i.e., somatosensory) and descending (i.e., motor) networks indeed enables significant functional motor improvement, together with significant sensorimotor-related neurophysiological changes. Somatosensory abilities are essential for patients to perceive the feedback provided by the BCI system. Thus, somatosensory impairments may significantly alter the efficiency of BCI-based motor rehabilitation. In order to precisely understand and assess the impact of somatosensory impairments, we first review the literature on post-stroke BCI-based motor rehabilitation (14 randomized clinical trials). We show that despite the central role that somatosensory abilities play on BCI-based motor rehabilitation post-stroke, the latter are rarely reported and used as inclusion/exclusion criteria in the literature on the matter. We then argue that somatosensory abilities have repeatedly been shown to influence the motor rehabilitation outcome, in general. This stresses the importance of also considering them and reporting them in the literature in BCI-based rehabilitation after stroke, especially since half of post-stroke patients suffer from somatosensory impairments. We argue that somatosensory abilities should systematically be assessed, controlled and reported if we want to precisely assess the influence they have on BCI efficiency. Not doing so could result in the misinterpretation of reported results, while doing so could improve (1) our understanding of the mechanisms underlying motor recovery (2) our ability to adapt the therapy to the patients' impairments and (3) our comprehension of the between-subject and between-study variability of therapeutic outcomes mentioned in the literature.
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Affiliation(s)
- Léa Pillette
- Inria, 200 av.de la Vieille Tour, 33400 Talence, France; LaBRI (Univ.Bordeaux, CNRS, Bordeaux-INP), 351, cours de la Libération, 33405 Talence, France.
| | - Fabien Lotte
- Inria, 200 av.de la Vieille Tour, 33400 Talence, France; LaBRI (Univ.Bordeaux, CNRS, Bordeaux-INP), 351, cours de la Libération, 33405 Talence, France.
| | - Bernard N'Kaoua
- Handicap, Activity, Cognition, Health, Inserm/University of Bordeaux, 146 rue Léo Saignat, 33076 Bordeaux cedex, France.
| | - Pierre-Alain Joseph
- Handicap, Activity, Cognition, Health, Inserm/University of Bordeaux, 146 rue Léo Saignat, 33076 Bordeaux cedex, France; Service MPR Pôle de Neurosciences Cliniques CHU, University of Bordeaux, Place Amélie Raba-Léon, 33000 Bordeaux cedex, France.
| | - Camille Jeunet
- CLLE (CNRS, Univ.Toulouse Jean Jaurès), 5 Allées Antonio Machado, 31058 Toulouse cedex 9, France.
| | - Bertrand Glize
- Handicap, Activity, Cognition, Health, Inserm/University of Bordeaux, 146 rue Léo Saignat, 33076 Bordeaux cedex, France; Service MPR Pôle de Neurosciences Cliniques CHU, University of Bordeaux, Place Amélie Raba-Léon, 33000 Bordeaux cedex, France.
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219
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220
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Chen S, Cao L, Shu X, Wang H, Ding L, Wang SH, Jia J. Longitudinal Electroencephalography Analysis in Subacute Stroke Patients During Intervention of Brain-Computer Interface With Exoskeleton Feedback. Front Neurosci 2020; 14:809. [PMID: 32922254 PMCID: PMC7457033 DOI: 10.3389/fnins.2020.00809] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2020] [Accepted: 07/10/2020] [Indexed: 11/13/2022] Open
Abstract
Background Brain-computer interface (BCI) has been regarded as a newly developing intervention in promoting motor recovery in stroke survivors. Several studies have been performed in chronic stroke to explore its clinical and subclinical efficacy. However, evidence in subacute stroke was poor, and the longitudinal sensorimotor rhythm changes in subacute stroke after BCI with exoskeleton feedback were still unclear. Materials and Methods Fourteen stroke patients in subacute stage were recruited and randomly allocated to BCI group (n = 7) and the control group (n = 7). Brain-computer interface training with exoskeleton feedback was applied in the BCI group three times a week for 4 weeks. The Fugl-Meyer Assessment of Upper Extremity (FMA-UE) scale was used to assess motor function improvement. Brain-computer interface performance was calculated across the 12-time interventions. Sensorimotor rhythm changes were explored by event-related desynchronization (ERD) changes and topographies. Results After 1 month BCI intervention, both the BCI group (p = 0.032) and the control group (p = 0.048) improved in FMA-UE scores. The BCI group (12.77%) showed larger percentage of improvement than the control group (7.14%), and more patients obtained good motor recovery in the BCI group (57.1%) than did the control group (28.6%). Patients with good recovery showed relatively higher online BCI performance, which were greater than 70%. And they showed a continuous improvement in offline BCI performance and obtained a highest value in the last six sessions of interventions during BCI training. However, patients with poor recovery reached a platform in the first six sessions of interventions and did not improve any more or even showed a decrease. In sensorimotor rhythm, patients with good recovery showed an enhanced ERD along with time change. Topographies showed that the ipsilesional hemisphere presented stronger activations after BCI intervention. Conclusion Brain-computer interface training with exoskeleton feedback was feasible in subacute stroke patients. Brain-computer interface performance can be an index to evaluate the efficacy of BCI intervention. Patients who presented increasingly stronger or continuously strong activations (ERD) may obtain better motor recovery.
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Affiliation(s)
- Shugeng Chen
- Department of Rehabilitation Medicine, Huashan Hospital, Fudan University, Shanghai, China
| | - Lei Cao
- Department of Computer Science and Technology, Shanghai Maritime University, Shanghai, China
| | - Xiaokang Shu
- School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Hewei Wang
- Department of Rehabilitation Medicine, Huashan Hospital, Fudan University, Shanghai, China
| | - Li Ding
- Department of Rehabilitation Medicine, Huashan Hospital, Fudan University, Shanghai, China
| | - Shui-Hua Wang
- School of Architecture Building and Civil Engineering, Loughborough University, Loughborough, United Kingdom.,School of Mathematics and Actuarial Science, University of Leicester, Leicester, United Kingdom
| | - Jie Jia
- Department of Rehabilitation Medicine, Huashan Hospital, Fudan University, Shanghai, China.,National Clinical Research Center for Aging and Medicine, Huashan Hospital, Fudan University, Shanghai, China
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221
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Riquelme-Ros JV, Rodríguez-Bermúdez G, Rodríguez-Rodríguez I, Rodríguez JV, Molina-García-Pardo JM. On the Better Performance of Pianists with Motor Imagery-Based Brain-Computer Interface Systems. SENSORS 2020; 20:s20164452. [PMID: 32785025 PMCID: PMC7472325 DOI: 10.3390/s20164452] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/02/2020] [Revised: 08/06/2020] [Accepted: 08/07/2020] [Indexed: 11/16/2022]
Abstract
Motor imagery (MI)-based brain-computer interface (BCI) systems detect electrical brain activity patterns through electroencephalogram (EEG) signals to forecast user intention while performing movement imagination tasks. As the microscopic details of individuals' brains are directly shaped by their rich experiences, musicians can develop certain neurological characteristics, such as improved brain plasticity, following extensive musical training. Specifically, the advanced bimanual motor coordination that pianists exhibit means that they may interact more effectively with BCI systems than their non-musically trained counterparts; this could lead to personalized BCI strategies according to the users' previously detected skills. This work assessed the performance of pianists as they interacted with an MI-based BCI system and compared it with that of a control group. The Common Spatial Patterns (CSP) and Linear Discriminant Analysis (LDA) machine learning algorithms were applied to the EEG signals for feature extraction and classification, respectively. The results revealed that the pianists achieved a higher level of BCI control by means of MI during the final trial (74.69%) compared to the control group (63.13%). The outcome indicates that musical training could enhance the performance of individuals using BCI systems.
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Affiliation(s)
| | - Germán Rodríguez-Bermúdez
- University Center of Defense, San Javier Air Force Base, Ministerio de Defensa-Universidad Politécnica de Cartagena, E30720 Santiago de la Ribera, Spain;
| | - Ignacio Rodríguez-Rodríguez
- Departamento de Ingeniería de Comunicaciones, ATIC Research Group, Universidad de Málaga, E29071 Málaga, Spain;
| | - José-Víctor Rodríguez
- Departamento de Tecnologías de la Información y las Comunicaciones, Universidad Politécnica de Cartagena, E30202 Cartagena, Spain;
- Correspondence:
| | - José-María Molina-García-Pardo
- Departamento de Tecnologías de la Información y las Comunicaciones, Universidad Politécnica de Cartagena, E30202 Cartagena, Spain;
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Guggenberger R, Heringhaus M, Gharabaghi A. Brain-Machine Neurofeedback: Robotics or Electrical Stimulation? Front Bioeng Biotechnol 2020; 8:639. [PMID: 32733860 PMCID: PMC7358603 DOI: 10.3389/fbioe.2020.00639] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2019] [Accepted: 05/26/2020] [Indexed: 12/19/2022] Open
Abstract
Neurotechnology such as brain-machine interfaces (BMI) are currently being investigated as training devices for neurorehabilitation, when active movements are no longer possible. When the hand is paralyzed following a stroke for example, a robotic orthosis, functional electrical stimulation (FES) or their combination may provide movement assistance; i.e., the corresponding sensory and proprioceptive neurofeedback is given contingent to the movement intention or imagination, thereby closing the sensorimotor loop. Controlling these devices may be challenging or even frustrating. Direct comparisons between these two feedback modalities (robotics vs. FES) with regard to the workload they pose for the user are, however, missing. Twenty healthy subjects controlled a BMI by kinesthetic motor imagery of finger extension. Motor imagery-related sensorimotor desynchronization in the EEG beta frequency-band (17–21 Hz) was turned into passive opening of the contralateral hand by a robotic orthosis or FES in a randomized, cross-over block design. Mental demand, physical demand, temporal demand, performance, effort, and frustration level were captured with the NASA Task Load Index (NASA-TLX) questionnaire by comparing these workload components to each other (weights), evaluating them individually (ratings), and estimating the respective combinations (adjusted workload ratings). The findings were compared to the task-related aspects of active hand movement with EMG feedback. Furthermore, both feedback modalities were compared with regard to their BMI performance. Robotic and FES feedback had similar workloads when weighting and rating the different components. For both robotics and FES, mental demand was the most relevant component, and higher than during active movement with EMG feedback. The FES task led to significantly more physical (p = 0.0368) and less temporal demand (p = 0.0403) than the robotic task in the adjusted workload ratings. Notably, the FES task showed a physical demand 2.67 times closer to the EMG task, but a mental demand 6.79 times closer to the robotic task. On average, significantly more onsets were reached during the robotic as compared to the FES task (17.22 onsets, SD = 3.02 vs. 16.46, SD = 2.94 out of 20 opportunities; p = 0.016), even though there were no significant differences between the BMI classification accuracies of the conditions (p = 0.806; CI = −0.027 to −0.034). These findings may inform the design of neurorehabilitation interfaces toward human-centered hardware for a more natural bidirectional interaction and acceptance by the user.
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Affiliation(s)
- Robert Guggenberger
- Institute for Neuromodulation and Neurotechnology, Department of Neurosurgery and Neurotechnology, University of Tübingen, Tübingen, Germany
| | - Monika Heringhaus
- Institute for Neuromodulation and Neurotechnology, Department of Neurosurgery and Neurotechnology, University of Tübingen, Tübingen, Germany
| | - Alireza Gharabaghi
- Institute for Neuromodulation and Neurotechnology, Department of Neurosurgery and Neurotechnology, University of Tübingen, Tübingen, Germany
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223
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Sebastián-Romagosa M, Udina E, Ortner R, Dinarès-Ferran J, Cho W, Murovec N, Matencio-Peralba C, Sieghartsleitner S, Allison BZ, Guger C. EEG Biomarkers Related With the Functional State of Stroke Patients. Front Neurosci 2020; 14:582. [PMID: 32733182 PMCID: PMC7358582 DOI: 10.3389/fnins.2020.00582] [Citation(s) in RCA: 32] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2020] [Accepted: 05/12/2020] [Indexed: 12/20/2022] Open
Abstract
Introduction Recent studies explored promising new quantitative methods to analyze electroencephalography (EEG) signals. This paper analyzes the correlation of two EEG parameters, Brain Symmetry Index (BSI) and Laterality Coefficient (LC), with established functional scales for the stroke assessment. Methods Thirty-two healthy subjects and thirty-six stroke patients with upper extremity hemiparesis were recruited for this study. The stroke patients where subdivided in three groups according to the stroke location: Cortical, Subcortical, and Cortical + Subcortical. The participants performed assessment visits to record the EEG in the resting state and perform functional tests using rehabilitation scales. Then, stroke patients performed 25 sessions using a motor-imagery based Brain Computer Interface system (BCI). BSI was calculated with the EEG data in resting state and LC was calculated with the Event-Related Synchronization maps. Results The results of this study demonstrated significant differences in the BSI between the healthy group and Subcortical group (P = 0.001), and also between the healthy and Cortical+Subcortical group (P = 0.019). No significant differences were found between the healthy group and the Cortical group (P = 0.505). Furthermore, the BSI analysis in the healthy group based on gender showed statistical differences (P = 0.027). In the stroke group, the correlation between the BSI and the functional state of the upper extremity assessed by Fugl-Meyer Assessment (FMA) was also significant, ρ = −0.430 and P = 0.046. The correlation between the BSI and the FMA-Lower extremity was not significant (ρ = −0.063, P = 0.852). Similarly, the LC calculated in the alpha band has significative correlation with FMA of upper extremity (ρ = −0.623 and P < 0.001) and FMA of lower extremity (ρ = −0.509 and P = 0.026). Other important significant correlations between LC and functional scales were observed. In addition, the patients showed an improvement in the FMA-upper extremity after the BCI therapy (ΔFMA = 1 median [IQR: 0–8], P = 0.002). Conclusion The quantitative EEG tools used here may help support our understanding of stroke and how the brain changes during rehabilitation therapy. These tools can help identify changes in EEG biomarkers and parameters during therapy that might lead to improved therapy methods and functional prognoses.
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Affiliation(s)
- Marc Sebastián-Romagosa
- Department of Physiology, Universitat Autònoma de Barcelona, Barcelona, Spain.,g.tec Medical Engineering Spain SL, Barcelona, Spain
| | - Esther Udina
- Department of Physiology, Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Rupert Ortner
- g.tec Medical Engineering Spain SL, Barcelona, Spain
| | - Josep Dinarès-Ferran
- g.tec Medical Engineering Spain SL, Barcelona, Spain.,Data and Signal Processing Research Group, Department of Engineering, University of Vic - Central University of Catalonia, Vic, Spain
| | - Woosang Cho
- g.tec Medical Engineering GmbH, Schiedlberg, Austria
| | - Nensi Murovec
- g.tec Medical Engineering GmbH, Schiedlberg, Austria
| | | | | | - Brendan Z Allison
- Department of Cognitive Science, University of California at San Diego, La Jolla, CA, United States
| | - Christoph Guger
- g.tec Medical Engineering Spain SL, Barcelona, Spain.,g.tec Medical Engineering GmbH, Schiedlberg, Austria
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224
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Jochumsen M, Niazi IK. Detection and classification of single-trial movement-related cortical potentials associated with functional lower limb movements. J Neural Eng 2020; 17:035009. [DOI: 10.1088/1741-2552/ab9a99] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
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225
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The Promotoer, a brain-computer interface-assisted intervention to promote upper limb functional motor recovery after stroke: a study protocol for a randomized controlled trial to test early and long-term efficacy and to identify determinants of response. BMC Neurol 2020; 20:254. [PMID: 32593293 PMCID: PMC7320550 DOI: 10.1186/s12883-020-01826-w] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2020] [Accepted: 06/10/2020] [Indexed: 01/21/2023] Open
Abstract
BACKGROUND Stroke is a leading cause of long-term disability. Cost-effective post-stroke rehabilitation programs for upper limb are critically needed. Brain-Computer Interfaces (BCIs) which enable the modulation of Electroencephalography (EEG) sensorimotor rhythms are promising tools to promote post-stroke recovery of upper limb motor function. The "Promotoer" study intends to boost the application of the EEG-based BCIs in clinical practice providing evidence for a short/long-term efficacy in enhancing post-stroke hand functional motor recovery and quantifiable indices of the participants response to a BCI-based intervention. To these aims, a longitudinal study will be performed in which subacute stroke participants will undergo a hand motor imagery (MI) training assisted by the Promotoer system, an EEG-based BCI system fully compliant with rehabilitation requirements. METHODS This longitudinal 2-arm randomized controlled superiority trial will include 48 first ever, unilateral, subacute stroke participants, randomly assigned to 2 intervention groups: the BCI-assisted hand MI training and a hand MI training not supported by BCI. Both interventions are delivered (3 weekly session; 6 weeks) as add-on regimen to standard intensive rehabilitation. A multidimensional assessment will be performed at: randomization/pre-intervention, 48 h post-intervention, and at 1, 3 and 6 month/s after end of intervention. Primary outcome measure is the Fugl-Meyer Assessment (FMA, upper extremity) at 48 h post-intervention. Secondary outcome measures include: the upper extremity FMA at follow-up, the Modified Ashworth Scale, the Numeric Rating Scale for pain, the Action Research Arm Test, the National Institute of Health Stroke Scale, the Manual Muscle Test, all collected at the different timepoints as well as neurophysiological and neuroimaging measures. DISCUSSION We expect the BCI-based rewarding of hand MI practice to promote long-lasting retention of the early induced improvement in hand motor outcome and also, this clinical improvement to be sustained by a long-lasting neuroplasticity changes harnessed by the BCI-based intervention. Furthermore, the longitudinal multidimensional assessment will address the selection of those stroke participants who best benefit of a BCI-assisted therapy, consistently advancing the transfer of BCIs to a best clinical practice. TRIAL REGISTRATION Name of registry: BCI-assisted MI Intervention in Subacute Stroke (Promotoer). TRIAL REGISTRATION NUMBER NCT04353297 ; registration date on the ClinicalTrial.gov platform: April, 15/2020.
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226
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Khan MA, Das R, Iversen HK, Puthusserypady S. Review on motor imagery based BCI systems for upper limb post-stroke neurorehabilitation: From designing to application. Comput Biol Med 2020; 123:103843. [PMID: 32768038 DOI: 10.1016/j.compbiomed.2020.103843] [Citation(s) in RCA: 66] [Impact Index Per Article: 16.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2020] [Revised: 05/18/2020] [Accepted: 06/02/2020] [Indexed: 12/21/2022]
Abstract
Strokes are a growing cause of mortality and many stroke survivors suffer from motor impairment as well as other types of disabilities in their daily life activities. To treat these sequelae, motor imagery (MI) based brain-computer interface (BCI) systems have shown potential to serve as an effective neurorehabilitation tool for post-stroke rehabilitation therapy. In this review, different MI-BCI based strategies, including "Functional Electric Stimulation, Robotics Assistance and Hybrid Virtual Reality based Models," have been comprehensively reported for upper-limb neurorehabilitation. Each of these approaches have been presented to illustrate the in-depth advantages and challenges of the respective BCI systems. Additionally, the current state-of-the-art and main concerns regarding BCI based post-stroke neurorehabilitation devices have also been discussed. Finally, recommendations for future developments have been proposed while discussing the BCI neurorehabilitation systems.
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Affiliation(s)
- Muhammad Ahmed Khan
- Department of Health Technology, Technical University of Denmark, 2800, Kgs. Lyngby, Denmark.
| | - Rig Das
- Department of Health Technology, Technical University of Denmark, 2800, Kgs. Lyngby, Denmark
| | - Helle K Iversen
- Department of Neurology, University of Copenhagen, Rigshospitalet, 2600, Glostrup, Denmark
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227
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Coscia M, Wessel MJ, Chaudary U, Millán JDR, Micera S, Guggisberg A, Vuadens P, Donoghue J, Birbaumer N, Hummel FC. Neurotechnology-aided interventions for upper limb motor rehabilitation in severe chronic stroke. Brain 2020; 142:2182-2197. [PMID: 31257411 PMCID: PMC6658861 DOI: 10.1093/brain/awz181] [Citation(s) in RCA: 105] [Impact Index Per Article: 26.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2018] [Revised: 03/14/2019] [Accepted: 05/12/2019] [Indexed: 01/27/2023] Open
Abstract
Upper limb motor deficits in severe stroke survivors often remain unresolved over extended time periods. Novel neurotechnologies have the potential to significantly support upper limb motor restoration in severely impaired stroke individuals. Here, we review recent controlled clinical studies and reviews focusing on the mechanisms of action and effectiveness of single and combined technology-aided interventions for upper limb motor rehabilitation after stroke, including robotics, muscular electrical stimulation, brain stimulation and brain computer/machine interfaces. We aim at identifying possible guidance for the optimal use of these new technologies to enhance upper limb motor recovery especially in severe chronic stroke patients. We found that the current literature does not provide enough evidence to support strict guidelines, because of the variability of the procedures for each intervention and of the heterogeneity of the stroke population. The present results confirm that neurotechnology-aided upper limb rehabilitation is promising for severe chronic stroke patients, but the combination of interventions often lacks understanding of single intervention mechanisms of action, which may not reflect the summation of single intervention’s effectiveness. Stroke rehabilitation is a long and complex process, and one single intervention administrated in a short time interval cannot have a large impact for motor recovery, especially in severely impaired patients. To design personalized interventions combining or proposing different interventions in sequence, it is necessary to have an excellent understanding of the mechanisms determining the effectiveness of a single treatment in this heterogeneous population of stroke patients. We encourage the identification of objective biomarkers for stroke recovery for patients’ stratification and to tailor treatments. Furthermore, the advantage of longitudinal personalized trial designs compared to classical double-blind placebo-controlled clinical trials as the basis for precise personalized stroke rehabilitation medicine is discussed. Finally, we also promote the necessary conceptual change from ‘one-suits-all’ treatments within in-patient clinical rehabilitation set-ups towards personalized home-based treatment strategies, by adopting novel technologies merging rehabilitation and motor assistance, including implantable ones.
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Affiliation(s)
- Martina Coscia
- Wyss Center for Bio and Neuroengineering, Chemin des Mines 9, 1202 Geneva, Switzerland
| | - Maximilian J Wessel
- Defitech Chair in Clinical Neuroengineering, Center for Neuroprosthetics (CNP) and Brain Mind Institute (BMI), School of Life Sciences, Swiss Federal Institute of Technology (EPFL), 1202 Geneva, Switzerland.,Defitech Chair in Clinical Neuroengineering, Center for Neuroprosthetics (CNP) and Brain Mind Institute (BMI), School of Life Sciences, Swiss Federal Institute of Technology (EPFL Valais), Clinique Romande de Réadaptation, 1951 Sion, Switzerland
| | - Ujwal Chaudary
- Wyss Center for Bio and Neuroengineering, Chemin des Mines 9, 1202 Geneva, Switzerland
| | - José Del R Millán
- Defitech Chair in Brain-Machine Interface, Center for Neuroprosthetics, School of Engineering, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, 1015, Switzerland
| | - Silvestro Micera
- Bertarelli Foundation Chair in Translational Neuroengineering, Center for Neuroprosthetics and Institute of Bioengineering, School of Engineering, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, 1015, Switzerland.,Translational Neural Engineering Area, The Biorobotics Institute, Scuola Superiore Sant'Anna, Pisa, 56025, Italy
| | - Adrian Guggisberg
- Clinical Neuroscience, University of Geneva Medical School, 1202 Geneva, Switzerland
| | | | - John Donoghue
- Wyss Center for Bio and Neuroengineering, Chemin des Mines 9, 1202 Geneva, Switzerland.,Department of Neuroscience, Brown University, Providence, RI 02906, USA
| | - Niels Birbaumer
- Wyss Center for Bio and Neuroengineering, Chemin des Mines 9, 1202 Geneva, Switzerland.,Institute of Medical Psychology and Behavioral Neurobiology, University Tuebingen, Germany
| | - Friedhelm C Hummel
- Defitech Chair in Clinical Neuroengineering, Center for Neuroprosthetics (CNP) and Brain Mind Institute (BMI), School of Life Sciences, Swiss Federal Institute of Technology (EPFL), 1202 Geneva, Switzerland.,Defitech Chair in Clinical Neuroengineering, Center for Neuroprosthetics (CNP) and Brain Mind Institute (BMI), School of Life Sciences, Swiss Federal Institute of Technology (EPFL Valais), Clinique Romande de Réadaptation, 1951 Sion, Switzerland.,Clinical Neuroscience, University of Geneva Medical School, 1202 Geneva, Switzerland
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228
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Rieke JD, Matarasso AK, Yusufali MM, Ravindran A, Alcantara J, White KD, Daly JJ. Development of a combined, sequential real-time fMRI and fNIRS neurofeedback system to enhance motor learning after stroke. J Neurosci Methods 2020; 341:108719. [PMID: 32439425 DOI: 10.1016/j.jneumeth.2020.108719] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2019] [Revised: 03/30/2020] [Accepted: 03/30/2020] [Indexed: 10/24/2022]
Abstract
BACKGROUND After stroke, wrist extension dyscoordination precludes functional arm/hand. We developed a more spatially precise brain signal for use in brain computer interface (BCI's) for stroke survivors. NEW METHOD Combination BCI protocol of real-time functional magnetic resonance imaging (rt-fMRI) sequentially followed by functional near infrared spectroscopy (rt-fNIRS) neurofeedback, interleaved with motor learning sessions without neural feedback. Custom Matlab and Python code was developed to provide rt-fNIRS-based feedback to the chronic stroke survivor, system user. RESULTS The user achieved a maximum of 71 % brain signal accuracy during rt-fNIRS neural training; progressive focus of brain activation across rt-fMRI neural training; increasing trend of brain signal amplitude during wrist extension across rt-fNIRS training; and clinically significant recovery of arm coordination and active wrist extension. COMPARISON WITH EXISTING METHODS Neurorehabilitation, peripherally directed, shows limited efficacy, as do EEG-based BCIs, for motor recovery of moderate/severely impaired stroke survivors. EEG-based BCIs are based on electrophysiological signal; whereas, rt-fMRI and rt-fNIRS are based on neurovascular signal. CONCLUSION The system functioned well during user testing. Methods are detailed for others' use. The system user successfully engaged rt-fMRI and rt-fNIRS neurofeedback systems, modulated brain signal during rt-fMRI and rt-fNIRS training, according to volume of brain activation and intensity of signal, respectively, and clinically significantly improved limb coordination and active wrist extension. fNIRS use in this case demonstrates a feasible/practical BCI system for further study with regard to use in chronic stroke rehab, and fMRI worked in concept, but cost and some patient-use issues make it less feasible for clinical practice.
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Affiliation(s)
- Jake D Rieke
- Brain Rehabilitation Research Center (BRRC), Malcom Randall VA Medical Center (VA), 1600 SW Archer Rd, Gainesville, FL, 32608, USA; Department of Biomedical Engineering (BME), NEB Building, University of Florida, Gainesville, FL, 32608, USA
| | - Avi K Matarasso
- Brain Rehabilitation Research Center (BRRC), Malcom Randall VA Medical Center (VA), 1600 SW Archer Rd, Gainesville, FL, 32608, USA; Dept of Chemical Engineering, NEB Building, UF, Gainesville, FL, 32608, USA
| | - M Minhal Yusufali
- Brain Rehabilitation Research Center (BRRC), Malcom Randall VA Medical Center (VA), 1600 SW Archer Rd, Gainesville, FL, 32608, USA; Department of Biomedical Engineering (BME), NEB Building, University of Florida, Gainesville, FL, 32608, USA
| | - Aniruddh Ravindran
- Brain Rehabilitation Research Center (BRRC), Malcom Randall VA Medical Center (VA), 1600 SW Archer Rd, Gainesville, FL, 32608, USA; Department of Biomedical Engineering (BME), NEB Building, University of Florida, Gainesville, FL, 32608, USA
| | - Jose Alcantara
- Brain Rehabilitation Research Center (BRRC), Malcom Randall VA Medical Center (VA), 1600 SW Archer Rd, Gainesville, FL, 32608, USA; Department of Biomedical Engineering (BME), NEB Building, University of Florida, Gainesville, FL, 32608, USA
| | - Keith D White
- Brain Rehabilitation Research Center (BRRC), Malcom Randall VA Medical Center (VA), 1600 SW Archer Rd, Gainesville, FL, 32608, USA
| | - Janis J Daly
- Brain Rehabilitation Research Center (BRRC), Malcom Randall VA Medical Center (VA), 1600 SW Archer Rd, Gainesville, FL, 32608, USA; Dept of Neurology, College of Medicine, UF, Gainesville, FL, 32608, USA.
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229
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Jochumsen M, Knoche H, Kjaer TW, Dinesen B, Kidmose P. EEG Headset Evaluation for Detection of Single-Trial Movement Intention for Brain-Computer Interfaces. SENSORS 2020; 20:s20102804. [PMID: 32423133 PMCID: PMC7287803 DOI: 10.3390/s20102804] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/01/2020] [Revised: 05/10/2020] [Accepted: 05/13/2020] [Indexed: 01/26/2023]
Abstract
Brain-computer interfaces (BCIs) can be used in neurorehabilitation; however, the literature about transferring the technology to rehabilitation clinics is limited. A key component of a BCI is the headset, for which several options are available. The aim of this study was to test four commercially available headsets' ability to record and classify movement intentions (movement-related cortical potentials-MRCPs). Twelve healthy participants performed 100 movements, while continuous EEG was recorded from the headsets on two different days to establish the reliability of the measures: classification accuracies of single-trials, number of rejected epochs, and signal-to-noise ratio. MRCPs could be recorded with the headsets covering the motor cortex, and they obtained the best classification accuracies (73%-77%). The reliability was moderate to good for the best headset (a gel-based headset covering the motor cortex). The results demonstrate that, among the evaluated headsets, reliable recordings of MRCPs require channels located close to the motor cortex and potentially a gel-based headset.
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Affiliation(s)
- Mads Jochumsen
- Department of Health Science and Technology, Aalborg University, 9220 Aalborg, Denmark;
- Correspondence:
| | - Hendrik Knoche
- Department of Architecture, Design and Media Technology, Aalborg University, 9000 Aalborg, Denmark;
| | - Troels Wesenberg Kjaer
- Department of Neurology, Zealand University Hospital, Roskilde, Denmark. Department of Clinical Medicine, University of Copenhagen, 2200 Copenhagen, Denmark;
| | - Birthe Dinesen
- Department of Health Science and Technology, Aalborg University, 9220 Aalborg, Denmark;
| | - Preben Kidmose
- Department of Engineering—Electrical and Computer Engineering, Aarhus University, 8200 Aarhus, Denmark;
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230
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Farrokhi B, Erfanian A. A state-based probabilistic method for decoding hand position during movement from ECoG signals in non-human primate. J Neural Eng 2020; 17:026042. [PMID: 32224511 DOI: 10.1088/1741-2552/ab848b] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
OBJECTIVE In this study, we proposed a state-based probabilistic method for decoding hand positions during unilateral and bilateral movements using the ECoG signals recorded from the brain of Rhesus monkey. APPROACH A customized electrode array was implanted subdurally in the right hemisphere of the brain covering from the primary motor cortex to the frontal cortex. Three different experimental paradigms were considered: ipsilateral, contralateral, and bilateral movements. During unilateral movement, the monkey was trained to get food with one hand, while during bilateral movement, the monkey used its left and right hands alternately to get food. To estimate the hand positions, a state-based probabilistic method was introduced which was based on the conditional probability of the hand movement state (i.e. idle, right hand movement, and left hand movement) and the conditional expectation of the hand position for each state. Moreover, a hybrid feature extraction method based on linear discriminant analysis and partial least squares (PLS) was introduced. MAIN RESULTS The proposed method could successfully decode the hand positions during ipsilateral, contralateral, and bilateral movements and significantly improved the decoding performance compared to the conventional Kalman and PLS regression methods [Formula: see text]. The proposed hybrid feature extraction method was found to outperform both the PLS and PCA methods [Formula: see text]. Investigating the kinematic information of each frequency band shows that more informative frequency bands were [Formula: see text] (15-30 Hz) and [Formula: see text](50-100 Hz) for ipsilateral and [Formula: see text] and [Formula: see text] (100-200 Hz) for contralateral movements. It is observed that ipsilateral movement was decoded better than contralateral movement for [Formula: see text] (5-15 Hz) and [Formula: see text] bands, while contralateral movements was decoded better for [Formula: see text] (30-200 Hz) and hfECoG (200-400 Hz) bands. SIGNIFICANCE Accurate decoding the bilateral movement using the ECoG recorded from one brain hemisphere is an important issue toward real-life applications of the brain-machine interface technologies.
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Affiliation(s)
- Behraz Farrokhi
- Department of Biomedical Engineering, School of Electrical Engineering, Iran University of Science and Technology (IUST), Iran Neural Technology Research Centre, Tehran, Iran
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231
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Bai Z, Fong KNK, Zhang JJ, Chan J, Ting KH. Immediate and long-term effects of BCI-based rehabilitation of the upper extremity after stroke: a systematic review and meta-analysis. J Neuroeng Rehabil 2020; 17:57. [PMID: 32334608 PMCID: PMC7183617 DOI: 10.1186/s12984-020-00686-2] [Citation(s) in RCA: 76] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2019] [Accepted: 04/07/2020] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND A substantial number of clinical studies have demonstrated the functional recovery induced by the use of brain-computer interface (BCI) technology in patients after stroke. The objective of this review is to evaluate the effect sizes of clinical studies investigating the use of BCIs in restoring upper extremity function after stroke and the potentiating effect of transcranial direct current stimulation (tDCS) on BCI training for motor recovery. METHODS The databases (PubMed, Medline, EMBASE, CINAHL, CENTRAL, PsycINFO, and PEDro) were systematically searched for eligible single-group or clinical controlled studies regarding the effects of BCIs in hemiparetic upper extremity recovery after stroke. Single-group studies were qualitatively described, but only controlled-trial studies were included in the meta-analysis. The PEDro scale was used to assess the methodological quality of the controlled studies. A meta-analysis of upper extremity function was performed by pooling the standardized mean difference (SMD). Subgroup meta-analyses regarding the use of external devices in combination with the application of BCIs were also carried out. We summarized the neural mechanism of the use of BCIs on stroke. RESULTS A total of 1015 records were screened. Eighteen single-group studies and 15 controlled studies were included. The studies showed that BCIs seem to be safe for patients with stroke. The single-group studies consistently showed a trend that suggested BCIs were effective in improving upper extremity function. The meta-analysis (of 12 studies) showed a medium effect size favoring BCIs for improving upper extremity function after intervention (SMD = 0.42; 95% CI = 0.18-0.66; I2 = 48%; P < 0.001; fixed-effects model), while the long-term effect (five studies) was not significant (SMD = 0.12; 95% CI = - 0.28 - 0.52; I2 = 0%; P = 0.540; fixed-effects model). A subgroup meta-analysis indicated that using functional electrical stimulation as the external device in BCI training was more effective than using other devices (P = 0.010). Using movement attempts as the trigger task in BCI training appears to be more effective than using motor imagery (P = 0.070). The use of tDCS (two studies) could not further facilitate the effects of BCI training to restore upper extremity motor function (SMD = - 0.30; 95% CI = - 0.96 - 0.36; I2 = 0%; P = 0.370; fixed-effects model). CONCLUSION The use of BCIs has significant immediate effects on the improvement of hemiparetic upper extremity function in patients after stroke, but the limited number of studies does not support its long-term effects. BCIs combined with functional electrical stimulation may be a better combination for functional recovery than other kinds of neural feedback. The mechanism for functional recovery may be attributed to the activation of the ipsilesional premotor and sensorimotor cortical network.
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Affiliation(s)
- Zhongfei Bai
- Department of Rehabilitation Sciences, The Hong Kong Polytechnic University, Kowloon, Hong Kong SAR.,Department of Occupational Therapy, Shanghai YangZhi Rehabilitation Hospital (Shanghai Sunshine Rehabilitation Center), Shanghai, China.,Department of Rehabilitation Sciences, Tongji University School of Medicine, Shanghai, China
| | - Kenneth N K Fong
- Department of Rehabilitation Sciences, The Hong Kong Polytechnic University, Kowloon, Hong Kong SAR.
| | - Jack Jiaqi Zhang
- Department of Rehabilitation Sciences, The Hong Kong Polytechnic University, Kowloon, Hong Kong SAR
| | - Josephine Chan
- School of Occupational Therapy, Institute of Health Sciences, Texas Woman's University, Houston Center, USA
| | - K H Ting
- University Research Facility in Behavioral and Systems Neuroscience, The Hong Kong Polytechnic University, Kowloon, Hong Kong SAR
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232
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Liu SQ, Zhang JC, Zhu R. A Wearable Human Motion Tracking Device Using Micro Flow Sensor Incorporating a Micro Accelerometer. IEEE Trans Biomed Eng 2020; 67:940-948. [DOI: 10.1109/tbme.2019.2924689] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
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233
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Micera S, Caleo M, Chisari C, Hummel FC, Pedrocchi A. Advanced Neurotechnologies for the Restoration of Motor Function. Neuron 2020; 105:604-620. [PMID: 32078796 DOI: 10.1016/j.neuron.2020.01.039] [Citation(s) in RCA: 60] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2019] [Revised: 11/15/2019] [Accepted: 01/27/2020] [Indexed: 01/23/2023]
Abstract
Stroke is one of the leading causes of long-term disability. Advanced technological solutions ("neurotechnologies") exploiting robotic systems and electrodes that stimulate the nervous system can increase the efficacy of stroke rehabilitation. Recent studies on these approaches have shown promising results. However, a paradigm shift in the development of new approaches must be made to significantly improve the clinical outcomes of neurotechnologies compared with those of traditional therapies. An "evolutionary" change can occur only by understanding in great detail the basic mechanisms of natural stroke recovery and technology-assisted neurorehabilitation. In this review, we first describe the results achieved by existing neurotechnologies and highlight their current limitations. In parallel, we summarize the data available on the mechanisms of recovery from electrophysiological, behavioral, and anatomical studies in humans and rodent models. Finally, we propose new approaches for the effective use of neurotechnologies in stroke survivors, as well as in people with other neurological disorders.
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Affiliation(s)
- Silvestro Micera
- The Biorobotics Institute and Department of Excellence in Robotics & AI, Scuola Superiore Sant'Anna, Pisa, Italy; Bertarelli Foundation Chair in Translational Neuroengineering, Centre for Neuroprosthetics and Institute of Bioengineering, School of Engineering, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland.
| | - Matteo Caleo
- Department of Biomedical Sciences, University of Padova, Padova, Italy; Institute of Neuroscience, National Research Council (CNR), Pisa, Italy
| | - Carmelo Chisari
- Neurorehabilitation Section, Department of Translational Research and New Technologies in Medicine and Surgery, University of Pisa, Pisa, Italy
| | - Friedhelm C Hummel
- Defitech Chair of Clinical Neuroengineering, Center for Neuroprosthetics (CNP) and Brain Mind Institute (BMI), École Polytechnique Fédérale de Lausanne (EPFL), 1202 Geneva, Switzerland; Defitech Chair of Clinical Neuroengineering, Center for Neuroprosthetics (CNP) and Brain Mind Institute (BMI), École Polytechnique Fédérale de Lausanne (EPFL Valais), Clinique Romande de Réadaptation, 1951 Sion, Switzerland; Clinical Neuroscience, University of Geneva Medical School, 1202 Geneva, Switzerland
| | - Alessandra Pedrocchi
- Neuroengineering and Medical Robotics Laboratory NearLab, Department of Electronics, Information and Bioengineering, Politecnico di Milano, 20133 Milan, Italy
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234
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Bacomics: a comprehensive cross area originating in the studies of various brain-apparatus conversations. Cogn Neurodyn 2020; 14:425-442. [PMID: 32655708 DOI: 10.1007/s11571-020-09577-7] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2019] [Revised: 02/17/2020] [Accepted: 03/05/2020] [Indexed: 12/20/2022] Open
Abstract
The brain is the most important organ of the human body, and the conversations between the brain and an apparatus can not only reveal a normally functioning or a dysfunctional brain but also can modulate the brain. Here, the apparatus may be a nonbiological instrument, such as a computer, and the consequent brain-computer interface is now a very popular research area with various applications. The apparatus may also be a biological organ or system, such as the gut and muscle, and their efficient conversations with the brain are vital for a healthy life. Are there any common bases that bind these different scenarios? Here, we propose a new comprehensive cross area: Bacomics, which comes from brain-apparatus conversations (BAC) + omics. We take Bacomics to cover at least three situations: (1) The brain is normal, but the conversation channel is disabled, as in amyotrophic lateral sclerosis. The task is to reconstruct or open up new channels to reactivate the brain function. (2) The brain is in disorder, such as in Parkinson's disease, and the work is to utilize existing or open up new channels to intervene, repair and modulate the brain by medications or stimulation. (3) Both the brain and channels are in order, and the goal is to enhance coordinated development between the brain and apparatus. In this paper, we elaborate the connotation of BAC into three aspects according to the information flow: the issue of output to the outside (BAC-1), the issue of input to the brain (BAC-2) and the issue of unity of brain and apparatus (BAC-3). More importantly, there are no less than five principles that may be taken as the cornerstones of Bacomics, such as feedforward and feedback control, brain plasticity, harmony, the unity of opposites and systems principles. Clearly, Bacomics integrates these seemingly disparate domains, but more importantly, opens a much wider door for the research and development of the brain, and the principles further provide the general framework in which to realize or optimize these various conversations.
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Iturrate I, Chavarriaga R, Millán JDR. General principles of machine learning for brain-computer interfacing. HANDBOOK OF CLINICAL NEUROLOGY 2020; 168:311-328. [PMID: 32164862 DOI: 10.1016/b978-0-444-63934-9.00023-8] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
Abstract
Brain-computer interfaces (BCIs) are systems that translate brain activity patterns into commands that can be executed by an artificial device. This enables the possibility of controlling devices such as a prosthetic arm or exoskeleton, a wheelchair, typewriting applications, or games directly by modulating our brain activity. For this purpose, BCI systems rely on signal processing and machine learning algorithms to decode the brain activity. This chapter provides an overview of the main steps required to do such a process, including signal preprocessing, feature extraction and selection, and decoding. Given the large amount of possible methods that can be used for these processes, a comprehensive review of them is beyond the scope of this chapter, and it is focused instead on the general principles that should be taken into account, as well as discussing good practices on how these methods should be applied and evaluated for proper design of reliable BCI systems.
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Affiliation(s)
- Iñaki Iturrate
- Center for Neuroprosthetics, École Polytechnique Fédérale de Lausanne, Geneva, Switzerland
| | - Ricardo Chavarriaga
- Center for Neuroprosthetics, École Polytechnique Fédérale de Lausanne, Geneva, Switzerland; Institute of Applied Information Technology (InIT), Zurich University of Applied Sciences ZHAW, Winterthur, Switzerland.
| | - José Del R Millán
- Department of Electrical and Computer Engineering, The University of Texas at Austin, Austin, TX, United States; Department of Neurology, The University of Texas at Austin, Austin, TX, United States
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Abstract
Electroencephalography (EEG) is the non-invasive measurement of the brain's electric fields. Electrodes placed on the scalp record voltage potentials resulting from current flow in and around neurons. EEG is nearly a century old: this long history has afforded EEG a rich and diverse spectrum of applications. On the one hand, foundations of EEG in clinical diagnostics have dovetailed more recently into brain-triggered neurorehabilitation treatments. On the other hand, EEG has not only been a workhorse for providing brain correlates of constructs in the field of experimental psychology, but has also been used as a true neuroimaging method with more recent extensions in translational as well as computational neuroscience. The versatility and accessibility of the technique, in combination with advances in signal processing, allow for this 'old dog' to still deliver new tricks and innovations.
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Affiliation(s)
- Andrea Biasiucci
- The Laboratory for Investigative Neurophysiology (The LINE), Department of Radiology, University Hospital Center and University of Lausanne (CHUV), Lausanne, Switzerland
| | - Benedetta Franceschiello
- The Laboratory for Investigative Neurophysiology (The LINE), Department of Radiology, University Hospital Center and University of Lausanne (CHUV), Lausanne, Switzerland; Ophthalmology Department, University of Lausanne, Fondation Asile des Aveugles, Lausanne, Switzerland
| | - Micah M Murray
- The Laboratory for Investigative Neurophysiology (The LINE), Department of Radiology, University Hospital Center and University of Lausanne (CHUV), Lausanne, Switzerland; Ophthalmology Department, University of Lausanne, Fondation Asile des Aveugles, Lausanne, Switzerland; EEG Brain Mapping Core, Center for Biomedical Imaging (CIBM) of Lausanne and Geneva, Lausanne, Switzerland; Department of Hearing and Speech Sciences, Vanderbilt University Medical Center, Nashville, TN 37232, USA.
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Juliano JM, Spicer RP, Vourvopoulos A, Lefebvre S, Jann K, Ard T, Santarnecchi E, Krum DM, Liew SL. Embodiment Is Related to Better Performance on a Brain-Computer Interface in Immersive Virtual Reality: A Pilot Study. SENSORS (BASEL, SWITZERLAND) 2020; 20:E1204. [PMID: 32098317 PMCID: PMC7070491 DOI: 10.3390/s20041204] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/21/2019] [Revised: 02/19/2020] [Accepted: 02/19/2020] [Indexed: 01/25/2023]
Abstract
Electroencephalography (EEG)-based brain-computer interfaces (BCIs) for motor rehabilitation aim to "close the loop" between attempted motor commands and sensory feedback by providing supplemental information when individuals successfully achieve specific brain patterns. Existing EEG-based BCIs use various displays to provide feedback, ranging from displays considered more immersive (e.g., head-mounted display virtual reality (HMD-VR)) to displays considered less immersive (e.g., computer screens). However, it is not clear whether more immersive displays improve neurofeedback performance and whether there are individual performance differences in HMD-VR versus screen-based neurofeedback. In this pilot study, we compared neurofeedback performance in HMD-VR versus a computer screen in 12 healthy individuals and examined whether individual differences on two measures (i.e., presence, embodiment) were related to neurofeedback performance in either environment. We found that, while participants' performance on the BCI was similar between display conditions, the participants' reported levels of embodiment were significantly different. Specifically, participants experienced higher levels of embodiment in HMD-VR compared to a computer screen. We further found that reported levels of embodiment positively correlated with neurofeedback performance only in HMD-VR. Overall, these preliminary results suggest that embodiment may relate to better performance on EEG-based BCIs and that HMD-VR may increase embodiment compared to computer screens.
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Affiliation(s)
- Julia M. Juliano
- Neural Plasticity and Neurorehabilitation Laboratory, Neuroscience Graduate Program, University of Southern California, Los Angeles, CA 90089, USA;
| | - Ryan P. Spicer
- Institute for Creative Technologies, University of Southern California, Playa Vista, CA 90094, USA; (R.P.S.); (D.M.K.)
| | - Athanasios Vourvopoulos
- Neural Plasticity and Neurorehabilitation Laboratory, Division of Occupational Science and Occupational Therapy, University of Southern California, Los Angeles, CA 90089, USA; (A.V.); (S.L.)
| | - Stephanie Lefebvre
- Neural Plasticity and Neurorehabilitation Laboratory, Division of Occupational Science and Occupational Therapy, University of Southern California, Los Angeles, CA 90089, USA; (A.V.); (S.L.)
| | - Kay Jann
- USC Stevens Neuroimaging and Informatics Institute, Department of Neurology, University of Southern California, Los Angeles, CA 90033, USA; (K.J.); (T.A.)
| | - Tyler Ard
- USC Stevens Neuroimaging and Informatics Institute, Department of Neurology, University of Southern California, Los Angeles, CA 90033, USA; (K.J.); (T.A.)
| | - Emiliano Santarnecchi
- Berenson-Allen Center for Non-Invasive Brain Stimulation and Division of Cognitive Neurology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA 02215, USA;
| | - David M. Krum
- Institute for Creative Technologies, University of Southern California, Playa Vista, CA 90094, USA; (R.P.S.); (D.M.K.)
| | - Sook-Lei Liew
- Neural Plasticity and Neurorehabilitation Laboratory, Division of Occupational Science and Occupational Therapy, University of Southern California, Los Angeles, CA 90089, USA; (A.V.); (S.L.)
- USC Stevens Neuroimaging and Informatics Institute, Department of Neurology, University of Southern California, Los Angeles, CA 90033, USA; (K.J.); (T.A.)
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Jochumsen M, Knoche H, Kidmose P, Kjær TW, Dinesen BI. Evaluation of EEG Headset Mounting for Brain-Computer Interface-Based Stroke Rehabilitation by Patients, Therapists, and Relatives. Front Hum Neurosci 2020; 14:13. [PMID: 32116602 PMCID: PMC7033449 DOI: 10.3389/fnhum.2020.00013] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2019] [Accepted: 01/13/2020] [Indexed: 12/11/2022] Open
Abstract
Brain-computer interfaces (BCIs) have successfully been used for motor recovery training in stroke patients. However, the setup of BCI systems is complex and may be divided into (1) mounting the headset and (2) calibration of the BCI. One of the major problems is mounting the headset for recording brain activity in a stroke rehabilitation context, and usability testing of this is limited. In this study, the aim was to compare the translational aspects of mounting five different commercially available headsets from a user perspective and investigate the design considerations associated with technology transfer to rehabilitation clinics and home use. No EEG signals were recorded, so the effectiveness of the systems have not been evaluated. Three out of five headsets covered the motor cortex which is needed to pick up movement intentions of attempted movements. The other two were as control and reference for potential design considerations. As primary stakeholders, nine stroke patients, eight therapists and two relatives participated; the stroke patients mounted the headsets themselves. The setup time was recorded, and participants filled in questionnaires related to comfort, aesthetics, setup complexity, overall satisfaction, and general design considerations. The patients had difficulties in mounting all headsets except for a headband with a dry electrode located on the forehead (control). The therapists and relatives were able to mount all headsets. The fastest headset to mount was the headband, and the most preferred headsets were the headband and a behind-ear headset (control). The most preferred headset that covered the motor cortex used water-based electrodes. The patients reported that it was important that they could mount the headset themselves for them to use it every day at home. These results have implications for design considerations for the development of BCI systems to be used in rehabilitation clinics and in the patient’s home.
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Affiliation(s)
- Mads Jochumsen
- Department of Health Science and Technology, Aalborg University, Aalborg, Denmark
| | - Hendrik Knoche
- Department of Architecture, Design and Media Technology, Aalborg University, Aalborg, Denmark
| | - Preben Kidmose
- Department of Engineering - Bioelectrical Instrumentation and Signal Processing, Aarhus University, Aarhus, Denmark
| | | | - Birthe Irene Dinesen
- Laboratory of Welfare Technologies, Telehealth and Telerehabilitation, Department of Health Science and Technology, Aalborg University, Aalborg, Denmark
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Wu Q, Yue Z, Ge Y, Ma D, Yin H, Zhao H, Liu G, Wang J, Dou W, Pan Y. Brain Functional Networks Study of Subacute Stroke Patients With Upper Limb Dysfunction After Comprehensive Rehabilitation Including BCI Training. Front Neurol 2020; 10:1419. [PMID: 32082238 PMCID: PMC7000923 DOI: 10.3389/fneur.2019.01419] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2019] [Accepted: 12/30/2019] [Indexed: 12/21/2022] Open
Abstract
Brain computer interface (BCI)-based training is promising for the treatment of stroke patients with upper limb (UL) paralysis. However, most stroke patients receive comprehensive treatment that not only includes BCI, but also routine training. The purpose of this study was to investigate the topological alterations in brain functional networks following comprehensive treatment, including BCI training, in the subacute stage of stroke. Twenty-five hospitalized subacute stroke patients with moderate to severe UL paralysis were assigned to one of two groups: 4-week comprehensive treatment, including routine and BCI training (BCI group, BG, n = 14) and 4-week routine training without BCI support (control group, CG, n = 11). Functional UL assessments were performed before and after training, including, Fugl-Meyer Assessment-UL (FMA-UL), Action Research Arm Test (ARAT), and Wolf Motor Function Test (WMFT). Neuroimaging assessment of functional connectivity (FC) in the BG was performed by resting state functional magnetic resonance imaging. After training, as compared with baseline, all clinical assessments (FMA-UL, ARAT, and WMFT) improved significantly (p < 0.05) in both groups. Meanwhile, better functional improvements were observed in FMA-UL (p < 0.05), ARAT (p < 0.05), and WMFT (p < 0.05) in the BG. Meanwhile, FC of the BG increased across the whole brain, including the temporal, parietal, and occipital lobes and subcortical regions. More importantly, increased inter-hemispheric FC between the somatosensory association cortex and putamen was strongly positively associated with UL motor function after training. Our findings demonstrate that comprehensive rehabilitation, including BCI training, can enhance UL motor function better than routine training for subacute stroke patients. The reorganization of brain functional networks topology in subacute stroke patients allows for increased coordination between the multi-sensory and motor-related cortex and the extrapyramidal system. Future long-term, longitudinal, controlled neuroimaging studies are needed to assess the effectiveness of BCI training as an approach to promote brain plasticity during the subacute stage of stroke.
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Affiliation(s)
- Qiong Wu
- Department of Rehabilitation Medicine, Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua University, Beijing, China
| | - Zan Yue
- Institute of Robotics and Intelligent Systems, School of Mechanical Engineering, Xi'an Jiaotong University, Xi'an, China
| | - Yunxiang Ge
- Department of Electronic Engineering, Tsinghua University, Beijing, China
| | - Di Ma
- Department of Rehabilitation Medicine, Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua University, Beijing, China
| | - Hang Yin
- Department of Rehabilitation Medicine, Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua University, Beijing, China
| | - Hongliang Zhao
- Department of Radiology, Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua University, Beijing, China
| | - Gang Liu
- Institute of Robotics and Intelligent Systems, School of Mechanical Engineering, Xi'an Jiaotong University, Xi'an, China
| | - Jing Wang
- Institute of Robotics and Intelligent Systems, School of Mechanical Engineering, Xi'an Jiaotong University, Xi'an, China
| | - Weibei Dou
- Department of Electronic Engineering, Tsinghua University, Beijing, China.,Beijing National Research Center for Information Science and Technology, Beijing, China
| | - Yu Pan
- Department of Rehabilitation Medicine, Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua University, Beijing, China
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Malekshahi A, Malekshahi R, Czornik M, Dax J, Wolpert S, Bauer H, Braun C, Birbaumer N. Real-time monitoring and regulating auditory cortex alpha activity in patients with chronic tinnitus. J Neural Eng 2020; 17:016032. [PMID: 31726439 DOI: 10.1088/1741-2552/ab57d5] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
OBJECTIVE Low levels of alpha activity (8-13Hz) mirror a state of enhanced responsiveness, whereas high levels of alpha are a state of reduced responsiveness. Tinnitus is accompanied by reduction of alpha activity in the perisylvian regions compared to normal hearing controls. This reduction might be a key mechanism in the chain of reactions leading to tinnitus. We devised a novel spatial filter as an on-line source monitoring method, which can be used to control alpha activity in the primary auditory cortex. In addition, we designed an innovative experimental procedure to enable suppression of visual and somatosensory alpha, facilitating auditory alpha control during alpha neurofeedback. APPROACH An amplitude-modulated auditory stimulation with 40 Hz modulation frequency and 1000 Hz carrier frequency specifically activates the primary auditory cortex. The topography of 40 Hz oscillation depicts the activity of the auditory cortices. We used this map as a spatial filter, which passes the activity originating from the auditory cortex. To suppress superposition of auditory alpha by somatosensory and visual alpha, we used a continuous tactile jaw-stimulation and visual stimulation protocol to suppress somatosensory alpha of regions adjacent to the auditory cortex and visual alpha for local regulation of auditory alpha activity only. MAIN RESULTS This novel spatial filter for online detection of auditory alpha activity and the usage of multi-sensory stimulation facilitate the appearance of alpha activity from the auditory cortex at the sensor level. SIGNIFICANCE The proposed procedure can be used in an EEG-neurofeedback-treatment approach allowing online auditory alpha self-regulation training in patients with chronic tinnitus.
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Affiliation(s)
- Azim Malekshahi
- Institute of Medical Psychology and Behavioral Neurobiology, University of Tübingen, Tübingen, Germany. These authors contributed equally. Author to whom any correspondence should be addressed
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Zhang L, Chen L, Wang Z, Liu S, Wang M, Chen S, Ming D. Study on brain computer interface combined tactile enhancement and time-varying features .. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2020; 2019:3042-3045. [PMID: 31946529 DOI: 10.1109/embc.2019.8856609] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
Neuroplasticity plays an important role in the recovery of injured nervous system. Both motor imagery (MI) and functional electrical stimulation (FES) can promote plasticity by activating the sensorimotor cortex. Specifically, MI as control strategy to activate FES in a brain computer interface (BCI) is a promising approach for motor functions recovery. This study demonstrated the efficiency of somatosensory input provided by electrical stimulation (ES) on cortical activation during MI. And the performance of classifiers with time-varying electroencephalography (EEG) features also be probed. We inspected the cortical activation by EEG for three experiment conditions, i.e. ES during MI, MI and ES. And the classification accuracy of three conditions were discussed respectively. Results showed that the ES during MI could induce stronger cortical activation than the other two conditions, and the classifier with time-varying EEG features had a higher classification accuracy. The results demonstrated that MI-based BCI combined MI and ES which fulfills two properties of somatosensory input and time-varying features is an available approach for motor neural rehabilitation.
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242
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Pichiorri F, Mattia D. Brain-computer interfaces in neurologic rehabilitation practice. HANDBOOK OF CLINICAL NEUROLOGY 2020; 168:101-116. [PMID: 32164846 DOI: 10.1016/b978-0-444-63934-9.00009-3] [Citation(s) in RCA: 25] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
The brain-computer interfaces (BCIs) for neurologic rehabilitation are based on the assumption that by retraining the brain to specific activities, an ultimate improvement of function can be expected. In this chapter, we review the present status, key determinants, and future directions of the clinical use of BCI in neurorehabilitation. The recent advancements in noninvasive BCIs as a therapeutic tool to promote functional motor recovery by inducing neuroplasticity are described, focusing on stroke as it represents the major cause of long-term motor disability. The relevance of recent findings on BCI use in spinal cord injury beyond the control of neuroprosthetic devices to restore motor function is briefly discussed. In a dedicated section, we examine the potential role of BCI technology in the domain of cognitive function recovery by instantiating BCIs in the long history of neurofeedback and some emerging BCI paradigms to address cognitive rehabilitation are highlighted. Despite the knowledge acquired over the last decade and the growing number of studies providing evidence for clinical efficacy of BCI in motor rehabilitation, an exhaustive deployment of this technology in clinical practice is still on its way. The pipeline to translate BCI to clinical practice in neurorehabilitation is the subject of this chapter.
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Affiliation(s)
- Floriana Pichiorri
- Neuroelectrical Imaging and Brain Computer Interface Laboratory, Fondazione Santa Lucia IRCCS, Rome, Italy
| | - Donatella Mattia
- Neuroelectrical Imaging and Brain Computer Interface Laboratory, Fondazione Santa Lucia IRCCS, Rome, Italy.
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243
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Abstract
In the past 10 years, brain-computer interfaces (BCIs) for controlling assistive devices have seen tremendous progress with respect to reliability and learnability, and numerous exemplary applications were demonstrated to be controllable by a BCI. Yet, BCI-controlled applications are hardly used for patients with neurologic or neurodegenerative disease. Such patient groups are considered potential end-users of BCI, specifically for replacing or improving lost function. We argue that BCI research and development still faces a translational gap, i.e., the knowledge of how to bring BCIs from the laboratory to the field is insufficient. BCI-controlled applications lack usability and accessibility; both constitute two sides of one coin, which is the key to use in daily life and to prevent nonuse. To increase usability, we suggest rigorously adopting the user-centered design in applied BCI research and development. To provide accessibility, assistive technology (AT) experts, providers, and other stakeholders have to be included in the user-centered process. BCI experts have to ensure the transfer of knowledge to AT professionals, and listen to the needs of primary, secondary, and tertiary end-users of BCI technology. Addressing both, usability and accessibility, in applied BCI research and development will bridge the translational gap and ensure that the needs of clinical end-users are heard, understood, addressed, and fulfilled.
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Affiliation(s)
- Andrea Kübler
- Institute of Psychology, University of Würzburg, Würzburg, Germany
| | - Femke Nijboer
- Faculty of Electrical Engineering, Mathematics and Computer Science, University of Twente, Enschede, The Netherlands
| | - Sonja Kleih
- Institute of Psychology, University of Würzburg, Würzburg, Germany
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Leeb R, Pérez-Marcos D. Brain-computer interfaces and virtual reality for neurorehabilitation. HANDBOOK OF CLINICAL NEUROLOGY 2020; 168:183-197. [PMID: 32164852 DOI: 10.1016/b978-0-444-63934-9.00014-7] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
Brain-computer interfaces (BCIs) and virtual reality (VR) are two technologic advances that are changing our way of interacting with the world. BCIs can be used to influence and can serve as a control mechanism in navigation tasks, communication, or other assistive functions. VR can create ad hoc interactive scenarios that involve all our senses, stimulate the brain in a multisensory fashion, and increase the motivation and fun with game-like environments. VR and motion tracking enable natural human-computer interaction at cognitive and physical levels. This includes both brain and body in the design of meaningful VR experiences; these cases in which participants feel naturally present could help augment the benefits of BCIs for assistive and neurorehabilitation applications for the relearning of motor and cognitive skills. VR technology is now available at the consumer level thanks to the proliferation of affordable head-mounted displays (HMDs). Merging both technologies into simplified, practical devices may help democratize these technologies.
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Huggins JE, Guger C, Aarnoutse E, Allison B, Anderson CW, Bedrick S, Besio W, Chavarriaga R, Collinger JL, Do AH, Herff C, Hohmann M, Kinsella M, Lee K, Lotte F, Müller-Putz G, Nijholt A, Pels E, Peters B, Putze F, Rupp R, Schalk G, Scott S, Tangermann M, Tubig P, Zander T. Workshops of the Seventh International Brain-Computer Interface Meeting: Not Getting Lost in Translation. BRAIN-COMPUTER INTERFACES 2019; 6:71-101. [PMID: 33033729 PMCID: PMC7539697 DOI: 10.1080/2326263x.2019.1697163] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2019] [Accepted: 10/30/2019] [Indexed: 12/11/2022]
Abstract
The Seventh International Brain-Computer Interface (BCI) Meeting was held May 21-25th, 2018 at the Asilomar Conference Grounds, Pacific Grove, California, United States. The interactive nature of this conference was embodied by 25 workshops covering topics in BCI (also called brain-machine interface) research. Workshops covered foundational topics such as hardware development and signal analysis algorithms, new and imaginative topics such as BCI for virtual reality and multi-brain BCIs, and translational topics such as clinical applications and ethical assumptions of BCI development. BCI research is expanding in the diversity of applications and populations for whom those applications are being developed. BCI applications are moving toward clinical readiness as researchers struggle with the practical considerations to make sure that BCI translational efforts will be successful. This paper summarizes each workshop, providing an overview of the topic of discussion, references for additional information, and identifying future issues for research and development that resulted from the interactions and discussion at the workshop.
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Affiliation(s)
- Jane E Huggins
- Department of Physical Medicine and Rehabilitation, Department of Biomedical Engineering, Neuroscience Graduate Program, University of Michigan, Ann Arbor, Michigan, United States, 325 East Eisenhower, Room 3017; Ann Arbor, Michigan 48108-5744
| | - Christoph Guger
- g.tec medical engineering GmbH/Guger Technologies OG, Austria, Sierningstrasse 14, 4521 Schiedlberg, Austria
| | - Erik Aarnoutse
- UMC Utrecht Brain Center, Department of Neurology & Neurosurgery, University Medical Center Utrecht, Heidelberglaan 100, 3584 CX Utrecht, The Netherlands
| | - Brendan Allison
- Dept. of Cognitive Science, Mail Code 0515, University of California at San Diego, La Jolla, United States
| | - Charles W Anderson
- Department of Computer Science, Molecular, Cellular and Integrative Neurosience Program, Colorado State University, Fort Collins, CO 80523
| | - Steven Bedrick
- Center for Spoken Language Understanding, Oregon Health & Science University, Portland, OR 97239
| | - Walter Besio
- Department of Electrical, Computer, & Biomedical Engineering and Interdisciplinary Neuroscience Program, University of Rhode Island, Kingston, Rhode Island, USA, CREmedical Corp. Kingston, Rhode Island, USA
| | - Ricardo Chavarriaga
- Defitech Chair in Brain-Machine Interface (CNBI), Center for Neuroprosthetics, Ecole Polytechnique Fédérale de Lausanne - EPFL, Switzerland
| | - Jennifer L Collinger
- University of Pittsburgh, Department of Physical Medicine and Rehabilitation, VA Pittsburgh Healthcare System, Department of Veterans Affairs, 3520 5th Ave, Pittsburgh, PA, 15213
| | - An H Do
- UC Irvine Brain Computer Interface Lab, Department of Neurology, University of California, Irvine
| | - Christian Herff
- School of Mental Health and Neuroscience, Maastricht University, Maastricht, The Netherlands
| | - Matthias Hohmann
- Max Planck Institute for Intelligent Systems, Department for Empirical Inference, Max-Planck-Ring 4, 72074 Tübingen, Germany
| | - Michelle Kinsella
- Oregon Health & Science University, Institute on Development & Disability, 707 SW Gaines St, #1290, Portland, OR 97239
| | - Kyuhwa Lee
- Swiss Federal Institute of Technology in Lausanne-EPFL
| | - Fabien Lotte
- Inria Bordeaux Sud-Ouest, LaBRI (Univ. Bordeaux/CNRS/Bordeaux INP), 200 avenue de la vieille tour, 33405, Talence Cedex, France
| | | | - Anton Nijholt
- Faculty EEMCS, University of Twente, Enschede, The Netherlands
| | - Elmar Pels
- UMC Utrecht Brain Center, Department of Neurology & Neurosurgery, University Medical Center Utrecht, Heidelberglaan 100, 3584 CX Utrecht, The Netherlands
| | - Betts Peters
- Oregon Health & Science University, Institute on Development & Disability, 707 SW Gaines St, #1290, Portland, OR 97239
| | - Felix Putze
- University of Bremen, Germany, Cognitive Systems Lab, University of Bremen, Enrique-Schmidt-Straße 5 (Cartesium), 28359 Bremen
| | - Rüdiger Rupp
- Spinal Cord Injury Center, Heidelberg University Hospital
| | - Gerwin Schalk
- National Center for Adaptive Neurotechnologies, Wadsworth Center, NYS Dept. of Health, Dept. of Neurology, Albany Medical College, Dept. of Biomed. Sci., State Univ. of New York at Albany, Center for Medical Sciences 2003, 150 New Scotland Avenue, Albany, New York 12208
| | - Stephanie Scott
- Department of Media Communications, Colorado State University, Fort Collins, CO 80523
| | - Michael Tangermann
- Brain State Decoding Lab, Cluster of Excellence BrainLinks-BrainTools, Computer Science Dept., University of Freiburg, Germany, Autonomous Intelligent Systems Lab, Computer Science Dept., University of Freiburg, Germany
| | - Paul Tubig
- Department of Philosophy, Center for Neurotechnology, University of Washington, Savery Hall, Room 361, Seattle, WA 98195
| | - Thorsten Zander
- Team PhyPA, Biological Psychology and Neuroergonomics, Technische Universität Berlin, Berlin, Germany, 7 Zander Laboratories B.V., Amsterdam, The Netherlands
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Pizzolato C, Saxby DJ, Palipana D, Diamond LE, Barrett RS, Teng YD, Lloyd DG. Neuromusculoskeletal Modeling-Based Prostheses for Recovery After Spinal Cord Injury. Front Neurorobot 2019; 13:97. [PMID: 31849634 PMCID: PMC6900959 DOI: 10.3389/fnbot.2019.00097] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2019] [Accepted: 11/05/2019] [Indexed: 01/12/2023] Open
Abstract
Concurrent stimulation and reinforcement of motor and sensory pathways has been proposed as an effective approach to restoring function after developmental or acquired neurotrauma. This can be achieved by applying multimodal rehabilitation regimens, such as thought-controlled exoskeletons or epidural electrical stimulation to recover motor pattern generation in individuals with spinal cord injury (SCI). However, the human neuromusculoskeletal (NMS) system has often been oversimplified in designing rehabilitative and assistive devices. As a result, the neuromechanics of the muscles is seldom considered when modeling the relationship between electrical stimulation, mechanical assistance from exoskeletons, and final joint movement. A powerful way to enhance current neurorehabilitation is to develop the next generation prostheses incorporating personalized NMS models of patients. This strategy will enable an individual voluntary interfacing with multiple electromechanical rehabilitation devices targeting key afferent and efferent systems for functional improvement. This narrative review discusses how real-time NMS models can be integrated with finite element (FE) of musculoskeletal tissues and interface multiple assistive and robotic devices with individuals with SCI to promote neural restoration. In particular, the utility of NMS models for optimizing muscle stimulation patterns, tracking functional improvement, monitoring safety, and providing augmented feedback during exercise-based rehabilitation are discussed.
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Affiliation(s)
- Claudio Pizzolato
- School of Allied Health Sciences, Griffith University, Gold Coast, QLD, Australia.,Griffith Centre for Biomedical and Rehabilitation Engineering, Menzies Health Institute Queensland, Griffith University, Gold Coast, QLD, Australia
| | - David J Saxby
- School of Allied Health Sciences, Griffith University, Gold Coast, QLD, Australia.,Griffith Centre for Biomedical and Rehabilitation Engineering, Menzies Health Institute Queensland, Griffith University, Gold Coast, QLD, Australia
| | - Dinesh Palipana
- Griffith Centre for Biomedical and Rehabilitation Engineering, Menzies Health Institute Queensland, Griffith University, Gold Coast, QLD, Australia.,The Hopkins Centre, Menzies Health Institute Queensland, Griffith University, Gold Coast, QLD, Australia.,Gold Coast Hospital and Health Service, Gold Coast, QLD, Australia.,School of Medicine, Griffith University, Gold Coast, QLD, Australia
| | - Laura E Diamond
- School of Allied Health Sciences, Griffith University, Gold Coast, QLD, Australia.,Griffith Centre for Biomedical and Rehabilitation Engineering, Menzies Health Institute Queensland, Griffith University, Gold Coast, QLD, Australia
| | - Rod S Barrett
- School of Allied Health Sciences, Griffith University, Gold Coast, QLD, Australia.,Griffith Centre for Biomedical and Rehabilitation Engineering, Menzies Health Institute Queensland, Griffith University, Gold Coast, QLD, Australia
| | - Yang D Teng
- Department of Physical Medicine and Rehabilitation, Spaulding Rehabilitation Hospital, Harvard Medical School, Charlestown, MA, United States.,Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States
| | - David G Lloyd
- School of Allied Health Sciences, Griffith University, Gold Coast, QLD, Australia.,Griffith Centre for Biomedical and Rehabilitation Engineering, Menzies Health Institute Queensland, Griffith University, Gold Coast, QLD, Australia
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248
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Wang Z, Zhou Y, Chen L, Gu B, Liu S, Xu M, Qi H, He F, Ming D. A BCI based visual-haptic neurofeedback training improves cortical activations and classification performance during motor imagery. J Neural Eng 2019; 16:066012. [DOI: 10.1088/1741-2552/ab377d] [Citation(s) in RCA: 28] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
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249
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Abstract
The development of robotic devices for rehabilitation is a fast-growing field. Nowadays, thanks to novel technologies that have improved robots’ capabilities and offered more cost-effective solutions, robotic devices are increasingly being employed during clinical practice, with the goal of boosting patients’ recovery. Robotic rehabilitation is also widely used in the context of neurological disorders, where it is often provided in a variety of different fashions, depending on the specific function to be restored. Indeed, the effect of robot-aided neurorehabilitation can be maximized when used in combination with a proper training regimen (based on motor control paradigms) or with non-invasive brain machine interfaces. Therapy-induced changes in neural activity and behavioral performance, which may suggest underlying changes in neural plasticity, can be quantified by multimodal assessments of both sensorimotor performance and brain/muscular activity pre/post or during intervention. Here, we provide an overview of the most common robotic devices for upper and lower limb rehabilitation and we describe the aforementioned neurorehabilitation scenarios. We also review assessment techniques for the evaluation of robotic therapy. Additional exploitation of these research areas will highlight the crucial contribution of rehabilitation robotics for promoting recovery and answering questions about reorganization of brain functions in response to disease.
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250
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Williams JJ, Watson AM, Vazquez AL, Schwartz AB. Viral-Mediated Optogenetic Stimulation of Peripheral Motor Nerves in Non-human Primates. Front Neurosci 2019; 13:759. [PMID: 31417342 PMCID: PMC6684788 DOI: 10.3389/fnins.2019.00759] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2018] [Accepted: 07/08/2019] [Indexed: 11/13/2022] Open
Abstract
Objective: Reanimation of muscles paralyzed by disease states such as spinal cord injury remains a highly sought therapeutic goal of neuroprosthetic research. Optogenetic stimulation of peripheral motor nerves expressing light-sensitive opsins is a promising approach to muscle reanimation that may overcome several drawbacks of traditional methods such as functional electrical stimulation (FES). However, the utility of these methods has only been demonstrated in rodents to date, while translation to clinical practice will likely first require demonstration and refinement of these gene therapy techniques in non-human primates. Approach: Three rhesus macaques were injected intramuscularly with either one or both of two optogenetic constructs (AAV6-hSyn-ChR2-eYFP and/or AAV6-hSyn-Chronos-eYFP) to transduce opsin expression in the corresponding nerves. Neuromuscular junctions were targeted for virus delivery using an electrical stimulating injection technique. Functional opsin expression was periodically evaluated up to 13 weeks post-injection by optically stimulating targeted nerves with a 472 nm fiber-coupled laser while recording electromyographic (EMG) responses. Main Results: One monkey demonstrated functional expression of ChR2 at 8 weeks post-injection in each of two injected muscles, while the second monkey briefly exhibited contractions coupled to optical stimulation in a muscle injected with the Chronos construct at 10 weeks. A third monkey injected only in one muscle with the ChR2 construct showed strong optically coupled contractions at 5 ½ weeks which then disappeared by 9 weeks. EMG responses to optical stimulation of ChR2-transduced nerves demonstrated graded recruitment relative to both stimulus pulse-width and light intensity, and followed stimulus trains up to 16 Hz. In addition, the EMG response to prolonged stimulation showed delayed fatigue over several minutes. Significance: These results demonstrate the feasibility of viral transduction of peripheral motor nerves for functional optical stimulation of motor activity in non-human primates, a variable timeline of opsin expression in a animal model closer to humans, and fundamental EMG response characteristics to optical nerve stimulation. Together, they represent an important step in translating these optogenetic techniques as a clinically viable gene therapy.
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Affiliation(s)
- Jordan J. Williams
- Department of Neurobiology, Systems Neuroscience Institute, University of Pittsburgh, Pittsburgh, PA, United States
| | - Alan M. Watson
- Center for Biologic Imaging, University of Pittsburgh, Pittsburgh, PA, United States
- Department of Cell Biology, University of Pittsburgh, Pittsburgh, PA, United States
| | - Alberto L. Vazquez
- Department of Radiology, University of Pittsburgh, Pittsburgh, PA, United States
- Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA, United States
| | - Andrew B. Schwartz
- Department of Neurobiology, Systems Neuroscience Institute, University of Pittsburgh, Pittsburgh, PA, United States
- Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA, United States
- Department of Neurobiology, University of Pittsburgh, Pittsburgh, PA, United States
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