251
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Irimia D, Sabathiel N, Ortner R, Poboroniuc M, Coon W, Allison BZ, Guger C. recoveriX: a new BCI-based technology for persons with stroke. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2017; 2016:1504-1507. [PMID: 28268612 DOI: 10.1109/embc.2016.7590995] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Brain-computer interface (BCI) systems have been used primarily to provide communication for persons with severe movement disabilities. This paper presents a new system that extends BCI technology to a new patient group: persons diagnosed with stroke. This system, called recoveriX, is designed to detect changes in motor imagery in real-time to help monitor compliance and provide closed-loop feedback during therapy. We describe recoveriX and present initial results from one patient.
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252
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Darvishi S, Ridding MC, Hordacre B, Abbott D, Baumert M. Investigating the impact of feedback update interval on the efficacy of restorative brain-computer interfaces. ROYAL SOCIETY OPEN SCIENCE 2017; 4:170660. [PMID: 28879007 PMCID: PMC5579123 DOI: 10.1098/rsos.170660] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/27/2017] [Accepted: 07/31/2017] [Indexed: 06/07/2023]
Abstract
Restorative brain-computer interfaces (BCIs) have been proposed to enhance stroke rehabilitation. Restorative BCIs are able to close the sensorimotor loop by rewarding motor imagery (MI) with sensory feedback. Despite the promising results from early studies, reaching clinically significant outcomes in a timely fashion is yet to be achieved. This lack of efficacy may be due to suboptimal feedback provision. To the best of our knowledge, the optimal feedback update interval (FUI) during MI remains unexplored. There is evidence that sensory feedback disinhibits the motor cortex. Thus, in this study, we explore how shorter than usual FUIs affect behavioural and neurophysiological measures following BCI training for stroke patients using a single-case proof-of-principle study design. The action research arm test was used as the primary behavioural measure and showed a clinically significant increase (36%) over the course of training. The neurophysiological measures including motor evoked potentials and maximum voluntary contraction showed distinctive changes in early and late phases of BCI training. Thus, this preliminary study may pave the way for running larger studies to further investigate the effect of FUI magnitude on the efficacy of restorative BCIs. It may also elucidate the role of early and late phases of motor learning along the course of BCI training.
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Affiliation(s)
- Sam Darvishi
- School of Electrical and Electronic Engineering, The University of Adelaide, Australia
| | | | - Brenton Hordacre
- The Robinson Research Institute, The University of Adelaide, Australia
- School of Health Sciences, University of South Australia, Australia
| | - Derek Abbott
- School of Electrical and Electronic Engineering, The University of Adelaide, Australia
| | - Mathias Baumert
- School of Electrical and Electronic Engineering, The University of Adelaide, Australia
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253
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Philips GR, Daly JJ, Príncipe JC. Topographical measures of functional connectivity as biomarkers for post-stroke motor recovery. J Neuroeng Rehabil 2017; 14:67. [PMID: 28683745 PMCID: PMC5501348 DOI: 10.1186/s12984-017-0277-3] [Citation(s) in RCA: 43] [Impact Index Per Article: 6.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2017] [Accepted: 06/20/2017] [Indexed: 01/19/2023] Open
Abstract
BACKGROUND Biomarkers derived from neural activity of the brain present a vital tool for the prediction and evaluation of post-stroke motor recovery, as well as for real-time biofeedback opportunities. METHODS In order to encapsulate recovery-related reorganization of brain networks into such biomarkers, we have utilized the generalized measure of association (GMA) and graph analyses, which include global and local efficiency, as well as hemispheric interdensity and intradensity. These methods were applied to electroencephalogram (EEG) data recorded during a study of 30 stroke survivors (21 male, mean age 57.9 years, mean stroke duration 22.4 months) undergoing 12 weeks of intensive therapeutic intervention. RESULTS We observed that decreases of the intradensity of the unaffected hemisphere are correlated (r s =-0.46;p<0.05) with functional recovery, as measured by the upper-extremity portion of the Fugl-Meyer Assessment (FMUE). In addition, high initial values of local efficiency predict greater improvement in FMUE (R 2=0.16;p<0.05). In a subset of 17 subjects possessing lesions of the cerebral cortex, reductions of global and local efficiency, as well as the intradensity of the unaffected hemisphere are found to be associated with functional improvement (r s =-0.60,-0.66,-0.75;p<0.05). Within the same subgroup, high initial values of global and local efficiency, are predictive of improved recovery (R 2=0.24,0.25;p<0.05). All significant findings were specific to the 12.5-25 Hz band. CONCLUSIONS These topological measures show promise for prognosis and evaluation of therapeutic outcomes, as well as potential application to BCI-enabled biofeedback.
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Affiliation(s)
- Gavin R. Philips
- Computational NeuroEngineering Laboratory, Department of Electrical and Computer Engineering, University of Florida, Gainesville, Florida, USA
| | - Janis J. Daly
- Department of Neurology, University of Florida, Gainesville, Florida, USA
- Malcolm Randall VA Medical Center, Gainesville, Florida, USA
| | - José C. Príncipe
- Computational NeuroEngineering Laboratory, Department of Electrical and Computer Engineering, University of Florida, Gainesville, Florida, USA
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254
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Ray AM, Lopez-Larraz E, Figueiredo TC, Birbaumer N, Ramos-Murguialday A. Movement-related brain oscillations vary with lesion location in severely paralyzed chronic stroke patients. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2017; 2017:1664-1667. [PMID: 29060204 DOI: 10.1109/embc.2017.8037160] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/07/2023]
Abstract
In the past few years, innovative upper-limb rehabilitation methods have been proposed for chronic stroke patients. These methods aim at functional motor rehabilitation using Brain-machine interfaces to constitute an alternate pathway from the brain to the muscles. Even in patients with absence of residual finger movements, recovery could be achieved. The extent to which these interventions are affected by individual lesion topology is yet to be understood. In this study EEG was measured in 30 chronic stroke patients during movement attempts of the paretic arm. We show that the magnitude of the event-related desynchronization was smaller in patients presenting lesions with involvement of the motor cortex. This could have important implications on the design of new rehabilitation schemes for these patients, which might benefit from carefully tailored interventions.
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255
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Min BK, Chavarriaga R, Millán JDR. Harnessing Prefrontal Cognitive Signals for Brain–Machine Interfaces. Trends Biotechnol 2017; 35:585-597. [DOI: 10.1016/j.tibtech.2017.03.008] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2016] [Revised: 03/13/2017] [Accepted: 03/14/2017] [Indexed: 12/27/2022]
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256
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Tonin L, Pitteri M, Leeb R, Zhang H, Menegatti E, Piccione F, Millán JDR. Behavioral and Cortical Effects during Attention Driven Brain-Computer Interface Operations in Spatial Neglect: A Feasibility Case Study. Front Hum Neurosci 2017; 11:336. [PMID: 28701939 PMCID: PMC5487481 DOI: 10.3389/fnhum.2017.00336] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2017] [Accepted: 06/12/2017] [Indexed: 11/13/2022] Open
Abstract
During the last years, several studies have suggested that Brain-Computer Interface (BCI) can play a critical role in the field of motor rehabilitation. In this case report, we aim to investigate the feasibility of a covert visuospatial attention (CVSA) driven BCI in three patients with left spatial neglect (SN). We hypothesize that such a BCI is able to detect attention task-specific brain patterns in SN patients and can induce significant changes in their abnormal cortical activity (α-power modulation, feature recruitment, and connectivity). The three patients were asked to control online a CVSA BCI by focusing their attention at different spatial locations, including their neglected (left) space. As primary outcome, results show a significant improvement of the reaction time in the neglected space between calibration and online modalities (p < 0.01) for the two out of three patients that had the slowest initial behavioral response. Such an evolution of reaction time negatively correlates (p < 0.05) with an increment of the Individual α-Power computed in the pre-cue interval. Furthermore, all patients exhibited a significant reduction of the inter-hemispheric imbalance (p < 0.05) over time in the parieto-occipital regions. Finally, analysis on the inter-hemispheric functional connectivity suggests an increment across modalities for regions in the affected (right) hemisphere and decrement for those in the healthy. Although preliminary, this feasibility study suggests a possible role of BCI in the therapeutic treatment of lateralized, attention-based visuospatial deficits.
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Affiliation(s)
- Luca Tonin
- Chair in Brain-Machine Interface, Center for Neuroprosthetics, École Polytechnique Fédérale de LausanneGeneva, Switzerland
| | - Marco Pitteri
- Neurology Section, Department of Neurosciences, Biomedicine and Movement Sciences, University of VeronaVerona, Italy
| | - Robert Leeb
- Chair in Brain-Machine Interface, Center for Neuroprosthetics, École Polytechnique Fédérale de LausanneGeneva, Switzerland
| | - Huaijian Zhang
- Chair in Brain-Machine Interface, Center for Neuroprosthetics, École Polytechnique Fédérale de LausanneGeneva, Switzerland
| | - Emanuele Menegatti
- Intelligent Autonomous Systems Laboratory, Department of Information Engineering, University of PadovaPadova, Italy
| | - Francesco Piccione
- Laboratory of Neuropsychology, IRCCS San Camillo Hospital FoundationVenice, Italy.,Laboratory of Neurophysiology, IRCCS San Camillo Hospital FoundationVenice, Italy
| | - José Del R Millán
- Chair in Brain-Machine Interface, Center for Neuroprosthetics, École Polytechnique Fédérale de LausanneGeneva, Switzerland
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257
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258
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Perronnet L, Lécuyer A, Mano M, Bannier E, Lotte F, Clerc M, Barillot C. Unimodal Versus Bimodal EEG-fMRI Neurofeedback of a Motor Imagery Task. Front Hum Neurosci 2017; 11:193. [PMID: 28473762 PMCID: PMC5397479 DOI: 10.3389/fnhum.2017.00193] [Citation(s) in RCA: 41] [Impact Index Per Article: 5.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2017] [Accepted: 04/03/2017] [Indexed: 11/30/2022] Open
Abstract
Neurofeedback is a promising tool for brain rehabilitation and peak performance training. Neurofeedback approaches usually rely on a single brain imaging modality such as EEG or fMRI. Combining these modalities for neurofeedback training could allow to provide richer information to the subject and could thus enable him/her to achieve faster and more specific self-regulation. Yet unimodal and multimodal neurofeedback have never been compared before. In the present work, we introduce a simultaneous EEG-fMRI experimental protocol in which participants performed a motor-imagery task in unimodal and bimodal NF conditions. With this protocol we were able to compare for the first time the effects of unimodal EEG-neurofeedback and fMRI-neurofeedback versus bimodal EEG-fMRI-neurofeedback by looking both at EEG and fMRI activations. We also propose a new feedback metaphor for bimodal EEG-fMRI-neurofeedback that integrates both EEG and fMRI signal in a single bi-dimensional feedback (a ball moving in 2D). Such a feedback is intended to relieve the cognitive load of the subject by presenting the bimodal neurofeedback task as a single regulation task instead of two. Additionally, this integrated feedback metaphor gives flexibility on defining a bimodal neurofeedback target. Participants were able to regulate activity in their motor regions in all NF conditions. Moreover, motor activations as revealed by offline fMRI analysis were stronger during EEG-fMRI-neurofeedback than during EEG-neurofeedback. This result suggests that EEG-fMRI-neurofeedback could be more specific or more engaging than EEG-neurofeedback. Our results also suggest that during EEG-fMRI-neurofeedback, participants tended to regulate more the modality that was harder to control. Taken together our results shed first light on the specific mechanisms of bimodal EEG-fMRI-neurofeedback and on its added-value as compared to unimodal EEG-neurofeedback and fMRI-neurofeedback.
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Affiliation(s)
- Lorraine Perronnet
- INRIA, VisAGeS Project TeamRennes, France.,Centre National de la Recherche Scientifique, IRISA, UMR 6074Rennes, France.,Institut National de la Santé et de la Recherche Médicale, U1228Rennes, France.,Université Rennes 1Rennes, France.,INRIA, Hybrid Project TeamRennes, France
| | - Anatole Lécuyer
- Centre National de la Recherche Scientifique, IRISA, UMR 6074Rennes, France.,INRIA, Hybrid Project TeamRennes, France
| | - Marsel Mano
- INRIA, VisAGeS Project TeamRennes, France.,Centre National de la Recherche Scientifique, IRISA, UMR 6074Rennes, France.,Institut National de la Santé et de la Recherche Médicale, U1228Rennes, France.,Université Rennes 1Rennes, France.,INRIA, Hybrid Project TeamRennes, France
| | - Elise Bannier
- INRIA, VisAGeS Project TeamRennes, France.,Centre National de la Recherche Scientifique, IRISA, UMR 6074Rennes, France.,Institut National de la Santé et de la Recherche Médicale, U1228Rennes, France.,Université Rennes 1Rennes, France.,CHU RennesRennes, France
| | - Fabien Lotte
- Inria, Potioc Project TeamTalence, France.,LaBRIBordeaux, France
| | - Maureen Clerc
- Inria, Athena Project TeamSophia Antipolis, France.,Université Côte d'AzurNice, France
| | - Christian Barillot
- INRIA, VisAGeS Project TeamRennes, France.,Centre National de la Recherche Scientifique, IRISA, UMR 6074Rennes, France.,Institut National de la Santé et de la Recherche Médicale, U1228Rennes, France.,Université Rennes 1Rennes, France
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259
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Abstract
Brain-computer interface (BCI) technology can restore communication and control to people who are severely paralyzed. There has been speculation that this technology might also be useful for a variety of diverse therapeutic applications. This survey considers possible ways that BCI technology can be applied to motor rehabilitation following stroke, Parkinson's disease, and psychiatric disorders. We consider potential neural signals as well as the design and goals of BCI-based therapeutic applications. These diverse applications all share a reliance on neuroimaging and signal processing technologies. At the same time, each of these potential applications presents a series of unique challenges.
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Affiliation(s)
| | - Janis Daly
- Brain Rehabilitation Research Program, McKnight Brain Institute, University of Florida, Gainesville, FL
| | - Chadwick Boulay
- The Ottawa Hospital Research Institute, Ottawa, Ontario, Canada
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260
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Ang KK, Guan C. EEG-Based Strategies to Detect Motor Imagery for Control and Rehabilitation. IEEE Trans Neural Syst Rehabil Eng 2017; 25:392-401. [DOI: 10.1109/tnsre.2016.2646763] [Citation(s) in RCA: 125] [Impact Index Per Article: 17.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
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261
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Application of artificial bee colony algorithm in feature optimization for motor imagery EEG classification. Neural Comput Appl 2017. [DOI: 10.1007/s00521-017-2950-7] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
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262
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Dettmers C, Braun N, Büsching I, Hassa T, Debener S, Liepert J. [Neurofeedback-based motor imagery training for rehabilitation after stroke]. DER NERVENARZT 2017; 87:1074-1081. [PMID: 27573884 DOI: 10.1007/s00115-016-0185-y] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Mental training, including motor observation and motor imagery, has awakened much academic interest. The presumed functional equivalence of motor imagery and motor execution has given hope that mental training could be used for motor rehabilitation after a stroke. Results obtained from randomized controlled trials have shown mixed results. Approximately half of the studies demonstrate positive effects of motor imagery training but the rest do not show an additional benefit. Possible reasons why motor imagery training has so far not become established as a robust therapeutic approach are discussed in detail. Moreover, more recent approaches, such as neurofeedback-based motor imagery or closed-loop systems are presented and the potential importance for motor learning and rehabilitation after a stroke is discussed.
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Affiliation(s)
- C Dettmers
- Kliniken Schmieder Konstanz, Eichhornstr.68, 78464, Konstanz, Deutschland.
| | - N Braun
- Abteilung für Neuropsychologie, Department für Psychologie, Fakultät VI - Medizin und Gesundheitswissenschaften, Universität Oldenburg, Oldenburg, Deutschland
| | - I Büsching
- Kliniken Schmieder Allensbach, Allensbach, Deutschland
| | - T Hassa
- Kliniken Schmieder Allensbach, Allensbach, Deutschland.,Lurija Institut, Konstanz, Deutschland
| | - S Debener
- Abteilung für Neuropsychologie, Department für Psychologie, Fakultät VI - Medizin und Gesundheitswissenschaften, Universität Oldenburg, Oldenburg, Deutschland
| | - J Liepert
- Kliniken Schmieder Allensbach, Allensbach, Deutschland
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263
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Bauer R, Gharabaghi A. Constraints and Adaptation of Closed-Loop Neuroprosthetics for Functional Restoration. Front Neurosci 2017; 11:111. [PMID: 28348511 PMCID: PMC5346545 DOI: 10.3389/fnins.2017.00111] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2016] [Accepted: 02/21/2017] [Indexed: 01/08/2023] Open
Abstract
Closed-loop neuroprosthetics aim to compensate for lost function, e.g., by controlling external devices such as prostheses or wheelchairs. Such assistive approaches seek to maximize speed and classification accuracy for high-dimensional control. More recent approaches use similar technology, but aim to restore lost motor function in the long term. To achieve this goal, restorative neuroprosthetics attempt to facilitate motor re-learning and to strengthen damaged and/or alternative neural connections on the basis of neurofeedback training within rehabilitative environments. Such a restorative approach requires reinforcement learning of self-modulated brain activity which is considered to be beneficial for functional rehabilitation, e.g., improvement of β-power modulation over sensorimotor areas for post-stroke movement restoration. Patients with motor impairments, however, may also have a compromised ability for motor task-related regulation of the targeted brain activity. This would affect the estimation of feature weights and hence the classification accuracy of the feedback device. This, in turn, can frustrate the patients and compromise their motor learning. Furthermore, the feedback training may even become erroneous when unconstrained classifier adaptation-which is often used in assistive approaches-is also applied in this rehabilitation context. In conclusion, the conceptual switch from assistance toward restoration necessitates a methodological paradigm shift from classification accuracy toward instructional efficiency. Furthermore, a constrained feature space, a priori regularized feature weights, and difficulty adaptation present key elements of restorative brain interfaces. These factors need, therefore, to be addressed within a therapeutic framework to facilitate reinforcement learning of brain self-regulation for restorative purposes.
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Affiliation(s)
- Robert Bauer
- Division of Functional and Restorative Neurosurgery, Centre for Integrative Neuroscience, Eberhard Karls University TuebingenTuebingen, Germany
| | - Alireza Gharabaghi
- Division of Functional and Restorative Neurosurgery, Centre for Integrative Neuroscience, Eberhard Karls University TuebingenTuebingen, Germany
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264
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Enriquez-Geppert S, Huster RJ, Herrmann CS. EEG-Neurofeedback as a Tool to Modulate Cognition and Behavior: A Review Tutorial. Front Hum Neurosci 2017; 11:51. [PMID: 28275344 PMCID: PMC5319996 DOI: 10.3389/fnhum.2017.00051] [Citation(s) in RCA: 114] [Impact Index Per Article: 16.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2016] [Accepted: 01/23/2017] [Indexed: 01/02/2023] Open
Abstract
Neurofeedback is attracting renewed interest as a method to self-regulate one’s own brain activity to directly alter the underlying neural mechanisms of cognition and behavior. It not only promises new avenues as a method for cognitive enhancement in healthy subjects, but also as a therapeutic tool. In the current article, we present a review tutorial discussing key aspects relevant to the development of electroencephalography (EEG) neurofeedback studies. In addition, the putative mechanisms underlying neurofeedback learning are considered. We highlight both aspects relevant for the practical application of neurofeedback as well as rather theoretical considerations related to the development of new generation protocols. Important characteristics regarding the set-up of a neurofeedback protocol are outlined in a step-by-step way. All these practical and theoretical considerations are illustrated based on a protocol and results of a frontal-midline theta up-regulation training for the improvement of executive functions. Not least, assessment criteria for the validation of neurofeedback studies as well as general guidelines for the evaluation of training efficacy are discussed.
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Affiliation(s)
- Stefanie Enriquez-Geppert
- Department of Clinical and Developmental Neuropsychology, Faculty of Behavioural and Social Sciences, University of Groningen Groningen, Netherlands
| | - René J Huster
- Department of Psychology, Faculty of Social Sciences, University of Oslo Oslo, Norway
| | - Christoph S Herrmann
- Experimental Psychology Laboratory, Department of Psychology, Faculty VI Medical and Health Sciences, University of Oldenburg Oldenburg, Germany
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265
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Darvishi S, Gharabaghi A, Boulay CB, Ridding MC, Abbott D, Baumert M. Proprioceptive Feedback Facilitates Motor Imagery-Related Operant Learning of Sensorimotor β-Band Modulation. Front Neurosci 2017; 11:60. [PMID: 28232788 PMCID: PMC5299002 DOI: 10.3389/fnins.2017.00060] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2016] [Accepted: 01/27/2017] [Indexed: 01/26/2023] Open
Abstract
Motor imagery (MI) activates the sensorimotor system independent of actual movements and might be facilitated by neurofeedback. Knowledge on the interaction between feedback modality and the involved frequency bands during MI-related brain self-regulation is still scarce. Previous studies compared the cortical activity during the MI task with concurrent feedback (MI with feedback condition) to cortical activity during the relaxation task where no feedback was provided (relaxation without feedback condition). The observed differences might, therefore, be related to either the task or the feedback. A proper comparison would necessitate studying a relaxation condition with feedback and a MI task condition without feedback as well. Right-handed healthy subjects performed two tasks, i.e., MI and relaxation, in alternating order. Each of the tasks (MI vs. relaxation) was studied with and without feedback. The respective event-driven oscillatory activity, i.e., sensorimotor desynchronization (during MI) or synchronization (during relaxation), was rewarded with contingent feedback. Importantly, feedback onset was delayed to study the task-related cortical activity in the absence of feedback provision during the delay period. The reward modality was alternated every 15 trials between proprioceptive and visual feedback. Proprioceptive input was superior to visual input to increase the range of task-related spectral perturbations in the α- and β-band, and was necessary to consistently achieve MI-related sensorimotor desynchronization (ERD) significantly below baseline. These effects occurred in task periods without feedback as well. The increased accuracy and duration of learned brain self-regulation achieved in the proprioceptive condition was specific to the β-band. MI-related operant learning of brain self-regulation is facilitated by proprioceptive feedback and mediated in the sensorimotor β-band.
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Affiliation(s)
- Sam Darvishi
- School of Electrical and Electronic Engineering, University of AdelaideAdelaide, SA, Australia; Division of Functional and Restorative Neurosurgery, and Centre for Integrative Neuroscience, Eberhard Karls University TuebingenTubingen, Germany
| | - Alireza Gharabaghi
- Division of Functional and Restorative Neurosurgery, and Centre for Integrative Neuroscience, Eberhard Karls University Tuebingen Tubingen, Germany
| | - Chadwick B Boulay
- The Ottawa Hospital Research Institute, University of Ottawa Ottawa, ON, Canada
| | - Michael C Ridding
- The Robinson Research Institute, University of Adelaide Adelaide, SA, Australia
| | - Derek Abbott
- School of Electrical and Electronic Engineering, University of Adelaide Adelaide, SA, Australia
| | - Mathias Baumert
- School of Electrical and Electronic Engineering, University of Adelaide Adelaide, SA, Australia
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266
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Henz D, Schöllhorn WI. EEG Brain Activity in Dynamic Health Qigong Training: Same Effects for Mental Practice and Physical Training? Front Psychol 2017; 8:154. [PMID: 28223957 PMCID: PMC5293832 DOI: 10.3389/fpsyg.2017.00154] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2016] [Accepted: 01/23/2017] [Indexed: 11/13/2022] Open
Abstract
In recent years, there has been significant uptake of meditation and related relaxation techniques, as a means of alleviating stress and fostering an attentive mind. Several electroencephalogram (EEG) studies have reported changes in spectral band frequencies during Qigong meditation indicating a relaxed state. Much less is reported on effects of brain activation patterns induced by Qigong techniques involving bodily movement. In this study, we tested whether (1) physical Qigong training alters EEG theta and alpha activation, and (2) mental practice induces the same effect as a physical Qigong training. Subjects performed the dynamic Health Qigong technique Wu Qin Xi (five animals) physically and by mental practice in a within-subjects design. Experimental conditions were randomized. Two 2-min (eyes-open, eyes-closed) EEG sequences under resting conditions were recorded before and immediately after each 15-min exercise. Analyses of variance were performed for spectral power density data. Increased alpha power was found in posterior regions in mental practice and physical training for eyes-open and eyes-closed conditions. Theta power was increased after mental practice in central areas in eyes-open conditions, decreased in fronto-central areas in eyes-closed conditions. Results suggest that mental, as well as physical Qigong training, increases alpha activity and therefore induces a relaxed state of mind. The observed differences in theta activity indicate different attentional processes in physical and mental Qigong training. No difference in theta activity was obtained in physical and mental Qigong training for eyes-open and eyes-closed resting state. In contrast, mental practice of Qigong entails a high degree of internalized attention that correlates with theta activity, and that is dependent on eyes-open and eyes-closed resting state.
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Affiliation(s)
- Diana Henz
- Institute of Sports Science, University of MainzMainz, Germany
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267
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McFarland DJ, Parvaz MA, Sarnacki WA, Goldstein RZ, Wolpaw JR. Prediction of subjective ratings of emotional pictures by EEG features. J Neural Eng 2017; 14:016009. [PMID: 27934776 PMCID: PMC5476954 DOI: 10.1088/1741-2552/14/1/016009] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
OBJECTIVE Emotion dysregulation is an important aspect of many psychiatric disorders. Brain-computer interface (BCI) technology could be a powerful new approach to facilitating therapeutic self-regulation of emotions. One possible BCI method would be to provide stimulus-specific feedback based on subject-specific electroencephalographic (EEG) responses to emotion-eliciting stimuli. APPROACH To assess the feasibility of this approach, we studied the relationships between emotional valence/arousal and three EEG features: amplitude of alpha activity over frontal cortex; amplitude of theta activity over frontal midline cortex; and the late positive potential over central and posterior mid-line areas. For each feature, we evaluated its ability to predict emotional valence/arousal on both an individual and a group basis. Twenty healthy participants (9 men, 11 women; ages 22-68) rated each of 192 pictures from the IAPS collection in terms of valence and arousal twice (96 pictures on each of 4 d over 2 weeks). EEG was collected simultaneously and used to develop models based on canonical correlation to predict subject-specific single-trial ratings. Separate models were evaluated for the three EEG features: frontal alpha activity; frontal midline theta; and the late positive potential. In each case, these features were used to simultaneously predict both the normed ratings and the subject-specific ratings. MAIN RESULTS Models using each of the three EEG features with data from individual subjects were generally successful at predicting subjective ratings on training data, but generalization to test data was less successful. Sparse models performed better than models without regularization. SIGNIFICANCE The results suggest that the frontal midline theta is a better candidate than frontal alpha activity or the late positive potential for use in a BCI-based paradigm designed to modify emotional reactions.
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Affiliation(s)
- Dennis J. McFarland
- National Center for Adaptive Neurotechnologies, Wadsworth Center, New York State Department of Health, Albany, New York 12201-0509
| | - Muhammad A. Parvaz
- Departments of Psychiatry and Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY 10029-6574
| | - William A. Sarnacki
- National Center for Adaptive Neurotechnologies, Wadsworth Center, New York State Department of Health, Albany, New York 12201-0509
| | - Rita Z. Goldstein
- Departments of Psychiatry and Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY 10029-6574
| | - Jonathan R. Wolpaw
- National Center for Adaptive Neurotechnologies, Wadsworth Center, New York State Department of Health, Albany, New York 12201-0509
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Huggins JE, Guger C, Ziat M, Zander TO, Taylor D, Tangermann M, Soria-Frisch A, Simeral J, Scherer R, Rupp R, Ruffini G, Robinson DKR, Ramsey NF, Nijholt A, Müller-Putz G, McFarland DJ, Mattia D, Lance BJ, Kindermans PJ, Iturrate I, Herff C, Gupta D, Do AH, Collinger JL, Chavarriaga R, Chase SM, Bleichner MG, Batista A, Anderson CW, Aarnoutse EJ. Workshops of the Sixth International Brain-Computer Interface Meeting: brain-computer interfaces past, present, and future. BRAIN-COMPUTER INTERFACES 2017; 4:3-36. [PMID: 29152523 PMCID: PMC5693371 DOI: 10.1080/2326263x.2016.1275488] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2023]
Abstract
The Sixth International Brain-Computer Interface (BCI) Meeting was held 30 May-3 June 2016 at the Asilomar Conference Grounds, Pacific Grove, California, USA. The conference included 28 workshops covering topics in BCI and brain-machine interface research. Topics included BCI for specific populations or applications, advancing BCI research through use of specific signals or technological advances, and translational and commercial issues to bring both implanted and non-invasive BCIs to market. BCI research is growing and expanding in the breadth of its applications, the depth of knowledge it can produce, and the practical benefit it can provide both for those with physical impairments and the general public. Here we provide summaries of each workshop, illustrating the breadth and depth of BCI research and highlighting important issues and calls for action to support future research and development.
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Affiliation(s)
- Jane E. Huggins
- Department of Physical Medicine and Rehabilitation, Department of Biomedical Engineering, University of Michigan, Ann Arbor, Michigan, USA
| | - Christoph Guger
- G.Tec Medical Engineering GmbH, Guger Technologies OG, Schiedlberg, Austria
| | - Mounia Ziat
- Psychology Department, Northern Michigan University, Marquette, MI, USA
| | - Thorsten O. Zander
- Team PhyPA, Biological Psychology and Neuroergonomics, Technical University of Berlin, Berlin, Germany
| | | | - Michael Tangermann
- Cluster of Excellence BrainLinks-BrainTools, University of Freiburg, Germany
| | | | - John Simeral
- Ctr. For Neurorestoration and Neurotechnology, Rehab. R&D Service, Dept. of VA Medical Center, School of Engineering, Brown University, Providence, RI, USA
| | - Reinhold Scherer
- Institute of Neural Engineering, BCI- Lab, Graz University of Technology, Graz, Austria
| | - Rüdiger Rupp
- Section Experimental Neurorehabilitation, Spinal Cord Injury Center, University Hospital in Heidelberg, Heidelberg, Germany
| | - Giulio Ruffini
- Neuroscience Business Unit, Starlab Barcelona SLU, Barcelona, Spain
- Neuroelectrics Inc., Boston, USA
| | - Douglas K. R. Robinson
- Institute: Laboratoire Interdisciplinaire Sciences Innovations Sociétés (LISIS), Université Paris-Est Marne-la-Vallée, MARNE-LA-VALLÉE, France
| | - Nick F. Ramsey
- Dept Neurology & Neurosurgery, Brain Center Rudolf Magnus, University Medical Center Utrecht, University of Utrecht, Utrecht, Netherlands
| | - Anton Nijholt
- Faculty EEMCS, Enschede, University of Twente, The Netherlands & Imagineering Institute, Iskandar, Malaysia
| | - Gernot Müller-Putz
- Institute of Neural Engineering, BCI- Lab, Graz University of Technology, Graz, Austria
| | - Dennis J. McFarland
- New York State Department of Health, National Center for Adaptive Neurotechnologies, Wadsworth Center, Albany, New York USA
| | - Donatella Mattia
- Clinical Neurophysiology, Fondazione Santa Lucia, Neuroelectrical Imaging and BCI Lab, IRCCS, Rome, Italy
| | - Brent J. Lance
- Human Research and Engineering Directorate, U.S. Army Research Laboratory, Aberdeen Proving Ground, Aberdeen, MD USA
| | | | - Iñaki Iturrate
- Defitech Chair in Brain–machine Interface (CNBI), Center for Neuroprosthetics, École Polytechnique Fédérale de Lausanne, EPFL-STI-CNBI, Campus Biotech H4, Geneva, Switzerland
| | - Christian Herff
- Cognitive Systems Lab, University of Bremen, Bremen, Germany
| | - Disha Gupta
- Brain Mind Research Inst, Weill Cornell Medical College, Early Brain Injury and Recovery Lab, Burke Medical Research Inst, White Plains, New York, USA
| | - An H. Do
- Department of Neurology, UC Irvine Brain Computer Interface Lab, University of California, Irvine, CA, USA
| | - Jennifer L. Collinger
- Department of Physical Medicine and Rehabilitation, Department of Veterans Affairs, VA Pittsburgh Healthcare System, University of Pittsburgh, Pittsburgh, PA, USA
| | - Ricardo Chavarriaga
- Defitech Chair in Brain–machine Interface (CNBI), Center for Neuroprosthetics, École Polytechnique Fédérale de Lausanne, EPFL-STI-CNBI, Campus Biotech H4, Geneva, Switzerland
| | - Steven M. Chase
- Center for the Neural Basis of Cognition and Department Biomedical Engineering, Carnegie Mellon University, Pittsburgh, PA, USA
| | - Martin G. Bleichner
- Neuropsychology Lab, Department of Psychology, European Medical School, Cluster of Excellence Hearing4all, University of Oldenburg, Oldenburg, Germany
| | - Aaron Batista
- Department of Bioengineering, Swanson School of Engineering, University of Pittsburgh, Pittsburgh, PA USA
| | - Charles W. Anderson
- Department of Computer Science, Colorado State University, Fort Collins, CO USA
| | - Erik J. Aarnoutse
- Brain Center Rudolf Magnus, Dept Neurology and Neurosurgery, University Medical Center Utrecht, Utrecht, The Netherlands
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269
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Friesen CL, Bardouille T, Neyedli HF, Boe SG. Combined Action Observation and Motor Imagery Neurofeedback for Modulation of Brain Activity. Front Hum Neurosci 2017; 10:692. [PMID: 28119594 PMCID: PMC5223402 DOI: 10.3389/fnhum.2016.00692] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2016] [Accepted: 12/26/2016] [Indexed: 12/27/2022] Open
Abstract
Motor imagery (MI) and action observation have proven to be efficacious adjuncts to traditional physiotherapy for enhancing motor recovery following stroke. Recently, researchers have used a combined approach called imagined imitation (II), where an individual watches a motor task being performed, while simultaneously imagining they are performing the movement. While neurofeedback (NFB) has been used extensively with MI to improve patients' ability to modulate sensorimotor activity and enhance motor recovery, the effectiveness of using NFB with II to modulate brain activity is unknown. This project tested the ability of participants to modulate sensorimotor activity during electroencephalography-based II-NFB of a complex, multi-part unilateral handshake, and whether this ability transferred to a subsequent bout of MI. Moreover, given the goal of translating findings from NFB research into practical applications, such as rehabilitation, the II-NFB system was designed with several user interface and user experience features, in an attempt to both drive user engagement and match the level of challenge to the abilities of the subjects. In particular, at easy difficulty levels the II-NFB system incentivized contralateral sensorimotor up-regulation (via event related desynchronization of the mu rhythm), while at higher difficulty levels the II-NFB system incentivized sensorimotor lateralization (i.e., both contralateral up-regulation and ipsilateral down-regulation). Thirty-two subjects, receiving real or sham NFB attended four sessions where they engaged in II-NFB training and subsequent MI. Results showed the NFB group demonstrated more bilateral sensorimotor activity during sessions 2–4 during II-NFB and subsequent MI, indicating mixed success for the implementation of this particular II-NFB system. Here we discuss our findings in the context of the design features included in the II-NFB system, highlighting limitations that should be considered in future designs.
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Affiliation(s)
- Christopher L Friesen
- Laboratory for Brain Recovery and Function, Dalhousie UniversityHalifax, NS, Canada; Department of Psychology and Neuroscience, Dalhousie UniversityHalifax, NS, Canada
| | - Timothy Bardouille
- Department of Psychology and Neuroscience, Dalhousie UniversityHalifax, NS, Canada; Biomedical Translational Imaging Centre, IWK Health CentreHalifax, NS, Canada; School of Physiotherapy, Dalhousie UniversityHalifax, NS, Canada
| | - Heather F Neyedli
- Department of Psychology and Neuroscience, Dalhousie UniversityHalifax, NS, Canada; School of Health and Human Performance, Dalhousie UniversityHalifax, NS, Canada
| | - Shaun G Boe
- Laboratory for Brain Recovery and Function, Dalhousie UniversityHalifax, NS, Canada; Department of Psychology and Neuroscience, Dalhousie UniversityHalifax, NS, Canada; School of Physiotherapy, Dalhousie UniversityHalifax, NS, Canada; School of Health and Human Performance, Dalhousie UniversityHalifax, NS, Canada
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270
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Sitaram R, Ros T, Stoeckel L, Haller S, Scharnowski F, Lewis-Peacock J, Weiskopf N, Blefari ML, Rana M, Oblak E, Birbaumer N, Sulzer J. Closed-loop brain training: the science of neurofeedback. Nat Rev Neurosci 2016; 18:86-100. [PMID: 28003656 DOI: 10.1038/nrn.2016.164] [Citation(s) in RCA: 527] [Impact Index Per Article: 65.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
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271
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Embodied neurofeedback with an anthropomorphic robotic hand. Sci Rep 2016; 6:37696. [PMID: 27869190 PMCID: PMC5116625 DOI: 10.1038/srep37696] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2016] [Accepted: 11/02/2016] [Indexed: 12/13/2022] Open
Abstract
Neurofeedback-guided motor imagery training (NF-MIT) has been suggested as a promising therapy for stroke-induced motor impairment. Whereas much NF-MIT research has aimed at signal processing optimization, the type of sensory feedback given to the participant has received less attention. Often the feedback signal is highly abstract and not inherently coupled to the mental act performed. In this study, we asked whether an embodied feedback signal is more efficient for neurofeedback operation than a non-embodiable feedback signal. Inspired by the rubber hand illusion, demonstrating that an artificial hand can be incorporated into one’s own body scheme, we used an anthropomorphic robotic hand to visually guide the participants’ motor imagery act and to deliver neurofeedback. Using two experimental manipulations, we investigated how a participant’s neurofeedback performance and subjective experience were influenced by the embodiability of the robotic hand, and by the neurofeedback signal’s validity. As pertains to embodiment, we found a promoting effect of robotic-hand embodiment in subjective, behavioral, electrophysiological and electrodermal measures. Regarding neurofeedback signal validity, we found some differences between real and sham neurofeedback in terms of subjective and electrodermal measures, but not in terms of behavioral and electrophysiological measures. This study motivates the further development of embodied feedback signals for NF-MIT.
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272
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EEG-Triggered Functional Electrical Stimulation Therapy for Restoring Upper Limb Function in Chronic Stroke with Severe Hemiplegia. Case Rep Neurol Med 2016; 2016:9146213. [PMID: 27882256 PMCID: PMC5110888 DOI: 10.1155/2016/9146213] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2016] [Revised: 05/27/2016] [Accepted: 10/04/2016] [Indexed: 11/18/2022] Open
Abstract
We report the therapeutic effects of integrating brain-computer interfacing technology and functional electrical stimulation therapy to restore upper limb reaching movements in a 64-year-old man with severe left hemiplegia following a hemorrhagic stroke he sustained six years prior to this study. He completed 40 90-minute sessions of functional electrical stimulation therapy using a custom-made neuroprosthesis that facilitated 5 different reaching movements. During each session, the participant attempted to reach with his paralyzed arm repeatedly. Stimulation for each of the movement phases (e.g., extending and retrieving the arm) was triggered when the power in the 18 Hz–28 Hz range (beta frequency range) of the participant's EEG activity, recorded with a single electrode, decreased below a predefined threshold. The function of the participant's arm showed a clinically significant improvement in the Fugl-Meyer Assessment Upper Extremity (FMA-UE) subscore (6 points) as well as moderate improvement in Functional Independence Measure Self-Care subscore (7 points). The changes in arm's function suggest that the combination of BCI technology and functional electrical stimulation therapy may restore voluntary motor function in individuals with chronic hemiplegia which results in severe upper limb deficit (FMA-UE ≤ 15), a population that does not benefit from current best-practice rehabilitation interventions.
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273
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Toppi J, Mattia D, Risetti M, Formisano R, Babiloni F, Astolfi L. Testing the Significance of Connectivity Networks: Comparison of Different Assessing Procedures. IEEE Trans Biomed Eng 2016; 63:2461-2473. [PMID: 27810793 DOI: 10.1109/tbme.2016.2621668] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
Despite the well-established use of partial directed coherence (PDC) to estimate interactions between brain signals, the assessment of its statistical significance still remains controversial. Commonly used approaches are based on the generation of empirical distributions of the null case, implying a considerable computational time, which may become a serious limitation in practical applications. Recently, rigorous asymptotic distributions for PDC were proposed. The aim of this work is to compare the performances of the asymptotic statistics with those of an empirical approach, in terms of both accuracy and computational time. METHODS Indices of performance were derived for the two approaches by a simulation study implementing different ground-truth networks under different levels of signal-to-noise ratio and amount of data available for the estimate. The two approaches were then applied to the resting-state EEG data acquired in a group of minimally conscious state and vegetative state/unresponsive wakefulness syndrome patients. RESULTS The performances of the asymptotic statistics in simulations matched those obtained by the empirical approach, with a considerable reduction of the computational time. Results of the application to real data showed that the asymptotic statistics led to the extraction of connectivity-based indices able to discriminate patients in different disorders of consciousness conditions and to correlate significantly with clinical scales. Such results were similar to those obtained by the empirical assessment, but with a considerable time economy. SIGNIFICANCE Asymptotic statistics provide an approach to the assessment of PDC significance with comparable performances with respect to the previously used empirical approaches but with a substantial advantage in terms of computational time.
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274
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Pichiorri F, Mrachacz-Kersting N, Molinari M, Kleih S, Kübler A, Mattia D. Brain-computer interface based motor and cognitive rehabilitation after stroke – state of the art, opportunity, and barriers: summary of the BCI Meeting 2016 in Asilomar. BRAIN-COMPUTER INTERFACES 2016. [DOI: 10.1080/2326263x.2016.1246328] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
Affiliation(s)
- Floriana Pichiorri
- Neuroelectrical Imaging and BCI lab, Fondazione Santa Lucia, Via Ardeatina, 306, 00179, Rome, Italy
| | - Natalie Mrachacz-Kersting
- Center for Sensory-Motor Interaction, Department of Health Science and Technology, Aalborg University, Fredrik Bajers Vej 7D3, 9220, Aalborg Ø, Denmark
| | - Marco Molinari
- Neuroelectrical Imaging and BCI lab, Fondazione Santa Lucia, Via Ardeatina, 306, 00179, Rome, Italy
| | - Sonja Kleih
- Institute of Psychology, University of Würzburg, Marcusstraße 9-11, 97070, Würzburg, Germany
| | - Andrea Kübler
- Institute of Psychology, University of Würzburg, Marcusstraße 9-11, 97070, Würzburg, Germany
| | - Donatella Mattia
- Neuroelectrical Imaging and BCI lab, Fondazione Santa Lucia, Via Ardeatina, 306, 00179, Rome, Italy
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275
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Simultaneous EEG-fNIRS reveals how age and feedback affect motor imagery signatures. Neurobiol Aging 2016; 49:183-197. [PMID: 27818001 DOI: 10.1016/j.neurobiolaging.2016.10.011] [Citation(s) in RCA: 32] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2015] [Revised: 10/07/2016] [Accepted: 10/09/2016] [Indexed: 12/18/2022]
Abstract
Stroke frequently results in motor impairment. Motor imagery (MI), the mental practice of movements, has been suggested as a promising complement to other therapeutic approaches facilitating motor rehabilitation. Of particular potential is the combination of MI with neurofeedback (NF). However, MI NF protocols have been largely optimized only in younger healthy adults, although strokes occur more frequently in older adults. The present study examined the influence of age on the neural correlates of MI supported by electroencephalogram (EEG)-based NF and on the neural correlates of motor execution. We adopted a multimodal neuroimaging framework focusing on EEG-derived event-related desynchronization (ERD%) and oxygenated (HbO) and deoxygenated hemoglobin (HbR) concentrations simultaneously acquired using functional near-infrared spectroscopy (fNIRS). ERD%, HbO concentration and HbR concentration were compared between younger (mean age: 24.4 years) and older healthy adults (mean age: 62.6 years). During MI, ERD% and HbR concentration were less lateralized in older adults than in younger adults. The lateralization-by-age interaction was not significant for movement execution. Moreover, EEG-based NF was related to an increase in task-specific activity when compared to the absence of feedback in both older and younger adults. Finally, significant modulation correlations were found between ERD% and hemodynamic measures despite the absence of significant amplitude correlations. Overall, the findings suggest a complex relationship between age and movement-related activity in electrophysiological and hemodynamic measures. Our results emphasize that the age of the actual end-user should be taken into account when designing neurorehabilitation protocols.
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276
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Bauer R, Vukelić M, Gharabaghi A. What is the optimal task difficulty for reinforcement learning of brain self-regulation? Clin Neurophysiol 2016; 127:3033-3041. [DOI: 10.1016/j.clinph.2016.06.016] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2016] [Revised: 06/10/2016] [Accepted: 06/19/2016] [Indexed: 11/28/2022]
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277
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Chaudhary U, Birbaumer N, Ramos-Murguialday A. Brain-computer interfaces for communication and rehabilitation. Nat Rev Neurol 2016; 12:513-25. [PMID: 27539560 DOI: 10.1038/nrneurol.2016.113] [Citation(s) in RCA: 330] [Impact Index Per Article: 41.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Brain-computer interfaces (BCIs) use brain activity to control external devices, thereby enabling severely disabled patients to interact with the environment. A variety of invasive and noninvasive techniques for controlling BCIs have been explored, most notably EEG, and more recently, near-infrared spectroscopy. Assistive BCIs are designed to enable paralyzed patients to communicate or control external robotic devices, such as prosthetics; rehabilitative BCIs are designed to facilitate recovery of neural function. In this Review, we provide an overview of the development of BCIs and the current technology available before discussing experimental and clinical studies of BCIs. We first consider the use of BCIs for communication in patients who are paralyzed, particularly those with locked-in syndrome or complete locked-in syndrome as a result of amyotrophic lateral sclerosis. We then discuss the use of BCIs for motor rehabilitation after severe stroke and spinal cord injury. We also describe the possible neurophysiological and learning mechanisms that underlie the clinical efficacy of BCIs.
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Affiliation(s)
- Ujwal Chaudhary
- Institute of Medical Psychology and Behavioural Neurobiology, University of Tübingen, Silcherstrasse 5, 72076 Tübingen, Germany
| | - Niels Birbaumer
- Institute of Medical Psychology and Behavioural Neurobiology, University of Tübingen, Silcherstrasse 5, 72076 Tübingen, Germany.,Wyss-Center for Bio- and Neuro-Engineering, Chenin de Mines 9, Ch 1202, Geneva, Switzerland
| | - Ander Ramos-Murguialday
- Institute of Medical Psychology and Behavioural Neurobiology, University of Tübingen, Silcherstrasse 5, 72076 Tübingen, Germany.,TECNALIA, Health Department, Neural Engineering Laboratory, San Sebastian, Paseo Mikeletegi 1, 20009 San Sebastian, Spain
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278
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Morone G, Paolucci S, Mattia D, Pichiorri F, Tramontano M, Iosa M. The 3Ts of the new millennium neurorehabilitation gym: therapy, technology, translationality. Expert Rev Med Devices 2016; 13:785-7. [PMID: 27466820 DOI: 10.1080/17434440.2016.1218275] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Affiliation(s)
- Giovanni Morone
- a Private Inpatient Unit , Fondazione Santa Lucia IRCCS , Rome , Italy.,b Clinical Laboratory of Experimental Neurorehabilitation , Fondazione Santa Lucia IRCCS , Rome , Italy
| | - Stefano Paolucci
- a Private Inpatient Unit , Fondazione Santa Lucia IRCCS , Rome , Italy.,b Clinical Laboratory of Experimental Neurorehabilitation , Fondazione Santa Lucia IRCCS , Rome , Italy
| | - Donatella Mattia
- c Neuroelectrical Imaging and Brain-Computer Interface Laboratory , Fondazione Santa Lucia IRCCS , Rome , Italy
| | - Floriana Pichiorri
- c Neuroelectrical Imaging and Brain-Computer Interface Laboratory , Fondazione Santa Lucia IRCCS , Rome , Italy
| | - Marco Tramontano
- d Department of Clinical and Behavioural Neurology , Santa Lucia Foundation IRCCS , Rome , Italy
| | - Marco Iosa
- a Private Inpatient Unit , Fondazione Santa Lucia IRCCS , Rome , Italy.,b Clinical Laboratory of Experimental Neurorehabilitation , Fondazione Santa Lucia IRCCS , Rome , Italy
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279
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Vourvopoulos A, Bermúdez I Badia S. Motor priming in virtual reality can augment motor-imagery training efficacy in restorative brain-computer interaction: a within-subject analysis. J Neuroeng Rehabil 2016; 13:69. [PMID: 27503007 PMCID: PMC4977849 DOI: 10.1186/s12984-016-0173-2] [Citation(s) in RCA: 54] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2015] [Accepted: 07/12/2016] [Indexed: 11/17/2022] Open
Abstract
Background The use of Brain–Computer Interface (BCI) technology in neurorehabilitation provides new strategies to overcome stroke-related motor limitations. Recent studies demonstrated the brain's capacity for functional and structural plasticity through BCI. However, it is not fully clear how we can take full advantage of the neurobiological mechanisms underlying recovery and how to maximize restoration through BCI. In this study we investigate the role of multimodal virtual reality (VR) simulations and motor priming (MP) in an upper limb motor-imagery BCI task in order to maximize the engagement of sensory-motor networks in a broad range of patients who can benefit from virtual rehabilitation training. Methods In order to investigate how different BCI paradigms impact brain activation, we designed 3 experimental conditions in a within-subject design, including an immersive Multimodal Virtual Reality with Motor Priming (VRMP) condition where users had to perform motor-execution before BCI training, an immersive Multimodal VR condition, and a control condition with standard 2D feedback. Further, these were also compared to overt motor-execution. Finally, a set of questionnaires were used to gather subjective data on Workload, Kinesthetic Imagery and Presence. Results Our findings show increased capacity to modulate and enhance brain activity patterns in all extracted EEG rhythms matching more closely those present during motor-execution and also a strong relationship between electrophysiological data and subjective experience. Conclusions Our data suggest that both VR and particularly MP can enhance the activation of brain patterns present during overt motor-execution. Further, we show changes in the interhemispheric EEG balance, which might play an important role in the promotion of neural activation and neuroplastic changes in stroke patients in a motor-imagery neurofeedback paradigm. In addition, electrophysiological correlates of psychophysiological responses provide us with valuable information about the motor and affective state of the user that has the potential to be used to predict MI-BCI training outcome based on user’s profile. Finally, we propose a BCI paradigm in VR, which gives the possibility of motor priming for patients with low level of motor control.
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Affiliation(s)
- Athanasios Vourvopoulos
- Faculdade das Ciências Exatas e da Engenharia, Universidade da Madeira, Campus Universitário da Penteada, 9020-105, Funchal, Portugal. .,Madeira Interactive Technologies Institute, Polo Científico e Tecnológico da Madeira, Caminho da Penteada, 9020-105, Funchal, Portugal.
| | - Sergi Bermúdez I Badia
- Faculdade das Ciências Exatas e da Engenharia, Universidade da Madeira, Campus Universitário da Penteada, 9020-105, Funchal, Portugal.,Madeira Interactive Technologies Institute, Polo Científico e Tecnológico da Madeira, Caminho da Penteada, 9020-105, Funchal, Portugal
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280
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Grimm F, Walter A, Spüler M, Naros G, Rosenstiel W, Gharabaghi A. Hybrid Neuroprosthesis for the Upper Limb: Combining Brain-Controlled Neuromuscular Stimulation with a Multi-Joint Arm Exoskeleton. Front Neurosci 2016; 10:367. [PMID: 27555805 PMCID: PMC4977295 DOI: 10.3389/fnins.2016.00367] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2016] [Accepted: 07/25/2016] [Indexed: 11/13/2022] Open
Abstract
Brain-machine interface-controlled (BMI) neurofeedback training aims to modulate cortical physiology and is applied during neurorehabilitation to increase the responsiveness of the brain to subsequent physiotherapy. In a parallel line of research, robotic exoskeletons are used in goal-oriented rehabilitation exercises for patients with severe motor impairment to extend their range of motion (ROM) and the intensity of training. Furthermore, neuromuscular electrical stimulation (NMES) is applied in neurologically impaired patients to restore muscle strength by closing the sensorimotor loop. In this proof-of-principle study, we explored an integrated approach for providing assistance as needed to amplify the task-related ROM and the movement-related brain modulation during rehabilitation exercises of severely impaired patients. For this purpose, we combined these three approaches (BMI, NMES, and exoskeleton) in an integrated neuroprosthesis and studied the feasibility of this device in seven severely affected chronic stroke patients who performed wrist flexion and extension exercises while receiving feedback via a virtual environment. They were assisted by a gravity-compensating, seven degree-of-freedom exoskeleton which was attached to the paretic arm. NMES was applied to the wrist extensor and flexor muscles during the exercises and was controlled by a hybrid BMI based on both sensorimotor cortical desynchronization (ERD) and electromyography (EMG) activity. The stimulation intensity was individualized for each targeted muscle and remained subthreshold, i.e., induced no overt support. The hybrid BMI controlled the stimulation significantly better than the offline analyzed ERD (p = 0.028) or EMG (p = 0.021) modality alone. Neuromuscular stimulation could be well integrated into the exoskeleton-based training and amplified both the task-related ROM (p = 0.009) and the movement-related brain modulation (p = 0.019). Combining a hybrid BMI with neuromuscular stimulation and antigravity assistance augments upper limb function and brain activity during rehabilitation exercises and may thus provide a novel restorative framework for severely affected stroke patients.
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Affiliation(s)
- Florian Grimm
- Division of Functional and Restorative Neurosurgery, Centre for Integrative Neuroscience, Eberhard Karls University Tuebingen Tuebingen, Germany
| | - Armin Walter
- Department of Computer Engineering, Wilhelm Schickard Institute for Computer Science, Eberhard Karls University Tuebingen Tuebingen, Germany
| | - Martin Spüler
- Department of Computer Engineering, Wilhelm Schickard Institute for Computer Science, Eberhard Karls University Tuebingen Tuebingen, Germany
| | - Georgios Naros
- Division of Functional and Restorative Neurosurgery, Centre for Integrative Neuroscience, Eberhard Karls University Tuebingen Tuebingen, Germany
| | - Wolfgang Rosenstiel
- Department of Computer Engineering, Wilhelm Schickard Institute for Computer Science, Eberhard Karls University Tuebingen Tuebingen, Germany
| | - Alireza Gharabaghi
- Division of Functional and Restorative Neurosurgery, Centre for Integrative Neuroscience, Eberhard Karls University Tuebingen Tuebingen, Germany
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281
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Chaudhary U, Birbaumer N, Ramos-Murguialday A. Brain-computer interfaces in the completely locked-in state and chronic stroke. PROGRESS IN BRAIN RESEARCH 2016; 228:131-61. [PMID: 27590968 DOI: 10.1016/bs.pbr.2016.04.019] [Citation(s) in RCA: 34] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Brain-computer interfaces (BCIs) use brain activity to control external devices, facilitating paralyzed patients to interact with the environment. In this chapter, we discuss the historical perspective of development of BCIs and the current advances of noninvasive BCIs for communication in patients with amyotrophic lateral sclerosis and for restoration of motor impairment after severe stroke. Distinct techniques have been explored to control a BCI in patient population especially electroencephalography (EEG) and more recently near-infrared spectroscopy (NIRS) because of their noninvasive nature and low cost. Previous studies demonstrated successful communication of patients with locked-in state (LIS) using EEG- and invasive electrocorticography-BCI and intracortical recordings when patients still showed residual eye control, but not with patients with complete LIS (ie, complete paralysis). Recently, a NIRS-BCI and classical conditioning procedure was introduced, allowing communication in patients in the complete locked-in state (CLIS). In severe chronic stroke without residual hand function first results indicate a possible superior motor rehabilitation to available treatment using BCI training. Here we present an overview of the available studies and recent results, which open new doors for communication, in the completely paralyzed and rehabilitation in severely affected stroke patients. We also reflect on and describe possible neuronal and learning mechanisms responsible for BCI control and perspective for future BMI research for communication in CLIS and stroke motor recovery.
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Affiliation(s)
- U Chaudhary
- Institute of Medical Psychology and Behavioral Neurobiology, University of Tübingen, Tübingen, Germany.
| | - N Birbaumer
- Institute of Medical Psychology and Behavioral Neurobiology, University of Tübingen, Tübingen, Germany; Wyss-Center for Bio- and Neuro-Engineering, Geneva, Switzerland
| | - A Ramos-Murguialday
- Institute of Medical Psychology and Behavioral Neurobiology, University of Tübingen, Tübingen, Germany; TECNALIA, San Sebastian, Spain.
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282
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Ushiba J, Soekadar SR. Brain-machine interfaces for rehabilitation of poststroke hemiplegia. PROGRESS IN BRAIN RESEARCH 2016; 228:163-83. [PMID: 27590969 DOI: 10.1016/bs.pbr.2016.04.020] [Citation(s) in RCA: 35] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
Noninvasive brain-machine interfaces (BMIs) are typically associated with neuroprosthetic applications or communication aids developed to assist in daily life after loss of motor function, eg, in severe paralysis. However, BMI technology has recently been found to be a powerful tool to promote neural plasticity facilitating motor recovery after brain damage, eg, due to stroke or trauma. In such BMI paradigms, motor cortical output and input are simultaneously activated, for instance by translating motor cortical activity associated with the attempt to move the paralyzed fingers into actual exoskeleton-driven finger movements, resulting in contingent visual and somatosensory feedback. Here, we describe the rationale and basic principles underlying such BMI motor rehabilitation paradigms and review recent studies that provide new insights into BMI-related neural plasticity and reorganization. Current challenges in clinical implementation and the broader use of BMI technology in stroke neurorehabilitation are discussed.
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Affiliation(s)
- J Ushiba
- Faculty of Science and Technology, Keio University, Kohoku-ku, Yokohama, Kanagawa, Japan.
| | - S R Soekadar
- Applied Neurotechnology Laboratory, University Hospital of Tübingen, Tübingen, Germany
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283
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Riccio A, Pichiorri F, Schettini F, Toppi J, Risetti M, Formisano R, Molinari M, Astolfi L, Cincotti F, Mattia D. Interfacing brain with computer to improve communication and rehabilitation after brain damage. PROGRESS IN BRAIN RESEARCH 2016; 228:357-87. [PMID: 27590975 DOI: 10.1016/bs.pbr.2016.04.018] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Communication and control of the external environment can be provided via brain-computer interfaces (BCIs) to replace a lost function in persons with severe diseases and little or no chance of recovery of motor abilities (ie, amyotrophic lateral sclerosis, brainstem stroke). BCIs allow to intentionally modulate brain activity, to train specific brain functions, and to control prosthetic devices, and thus, this technology can also improve the outcome of rehabilitation programs in persons who have suffered from a central nervous system injury (ie, stroke leading to motor or cognitive impairment). Overall, the BCI researcher is challenged to interact with people with severe disabilities and professionals in the field of neurorehabilitation. This implies a deep understanding of the disabled condition on the one hand, and it requires extensive knowledge on the physiology and function of the human brain on the other. For these reasons, a multidisciplinary approach and the continuous involvement of BCI users in the design, development, and testing of new systems are desirable. In this chapter, we will focus on noninvasive EEG-based systems and their clinical applications, highlighting crucial issues to foster BCI translation outside laboratories to eventually become a technology usable in real-life realm.
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Affiliation(s)
- A Riccio
- Neuroelectrical Imaging and Brain-Computer Interface Laboratory, Fondazione Santa Lucia IRCCS, Rome, Italy
| | - F Pichiorri
- Neuroelectrical Imaging and Brain-Computer Interface Laboratory, Fondazione Santa Lucia IRCCS, Rome, Italy; Sapienza University of Rome, Rome, Italy
| | - F Schettini
- Neuroelectrical Imaging and Brain-Computer Interface Laboratory, Fondazione Santa Lucia IRCCS, Rome, Italy
| | - J Toppi
- Neuroelectrical Imaging and Brain-Computer Interface Laboratory, Fondazione Santa Lucia IRCCS, Rome, Italy; Sapienza University of Rome, Rome, Italy
| | - M Risetti
- Neuroelectrical Imaging and Brain-Computer Interface Laboratory, Fondazione Santa Lucia IRCCS, Rome, Italy
| | - R Formisano
- Post-Coma Unit, Fondazione Santa Lucia IRCCS, Rome, Italy
| | - M Molinari
- Spinal Cord Unit, IRCCS Santa Lucia Foundation, Rome, Italy
| | - L Astolfi
- Neuroelectrical Imaging and Brain-Computer Interface Laboratory, Fondazione Santa Lucia IRCCS, Rome, Italy; Sapienza University of Rome, Rome, Italy
| | - F Cincotti
- Neuroelectrical Imaging and Brain-Computer Interface Laboratory, Fondazione Santa Lucia IRCCS, Rome, Italy; Sapienza University of Rome, Rome, Italy
| | - D Mattia
- Neuroelectrical Imaging and Brain-Computer Interface Laboratory, Fondazione Santa Lucia IRCCS, Rome, Italy.
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284
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Overground walking training with the i-Walker, a robotic servo-assistive device, enhances balance in patients with subacute stroke: a randomized controlled trial. J Neuroeng Rehabil 2016; 13:47. [PMID: 27225043 PMCID: PMC4880987 DOI: 10.1186/s12984-016-0155-4] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2015] [Accepted: 05/10/2016] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Patients affected by mild stroke benefit more from physiological overground walking training than walking-like training performed in place using specific devices. The aim of the study was to evaluate the effects of overground robotic walking training performed with the servo-assistive robotic rollator (i-Walker) on walking, balance, gait stability and falls in a community setting in patients with mild subacute stroke. METHODS Forty-four patients were randomly assigned to two different groups that received the same therapy in two daily 40-min sessions 5 days a week for 4 weeks. Twenty sessions of standard therapy were performed by both groups. In the other 20 sessions the subjects enrolled in the i-Walker-Group (iWG) performed with the i-Walker and the Control-Group patients (CG) performed the same amount of conventional walking oriented therapy. Clinical and instrumented gait assessments were made pre- and post-treatment. The follow-up observation consisted of recording the number of fallers in the community setting after 6 months. RESULTS Treatment effectiveness was higher in the iWG group in terms of balance improvement (Tinetti: 68.4 ± 27.6 % vs. 48.1 ± 33.9 %, p = 0.033) and 10-m and 6-min timed walking tests (significant interaction between group and time: F(1,40) = 14.252, p = 0.001; and F(1,40) = 7.883, p = 0.008, respectively). When measured, latero-lateral upper body accelerations were reduced in iWG (F = 4.727, p = 0.036), suggesting increased gait stability, which was supported by a reduced number of falls at home. CONCLUSIONS A robotic servo-assisted i-Walker improved walking performance and balance in patients affected by mild/moderate stroke, leading to increased gait stability and reduced falls in the community. TRIAL REGISTRATION This study was registered on anzctr.org.au (July 1, 2015; ACTRN12615000681550 ).
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285
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Meinel A, Castaño-Candamil S, Reis J, Tangermann M. Pre-Trial EEG-Based Single-Trial Motor Performance Prediction to Enhance Neuroergonomics for a Hand Force Task. Front Hum Neurosci 2016; 10:170. [PMID: 27199701 PMCID: PMC4843706 DOI: 10.3389/fnhum.2016.00170] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2015] [Accepted: 04/04/2016] [Indexed: 12/13/2022] Open
Abstract
We propose a framework for building electrophysiological predictors of single-trial motor performance variations, exemplified for SVIPT, a sequential isometric force control task suitable for hand motor rehabilitation after stroke. Electroencephalogram (EEG) data of 20 subjects with mean age of 53 years was recorded prior to and during 400 trials of SVIPT. They were executed within a single session with the non-dominant left hand, while receiving continuous visual feedback of the produced force trajectories. The behavioral data showed strong trial-by-trial performance variations for five clinically relevant metrics, which accounted for reaction time as well as for the smoothness and precision of the produced force trajectory. 18 out of 20 tested subjects remained after preprocessing and entered offline analysis. Source Power Comodulation (SPoC) was applied on EEG data of a short time interval prior to the start of each SVIPT trial. For 11 subjects, SPoC revealed robust oscillatory EEG subspace components, whose bandpower activity are predictive for the performance of the upcoming trial. Since SPoC may overfit to non-informative subspaces, we propose to apply three selection criteria accounting for the meaningfulness of the features. Across all subjects, the obtained components were spread along the frequency spectrum and showed a variety of spatial activity patterns. Those containing the highest level of predictive information resided in and close to the alpha band. Their spatial patterns resemble topologies reported for visual attention processes as well as those of imagined or executed hand motor tasks. In summary, we identified subject-specific single predictors that explain up to 36% of the performance fluctuations and may serve for enhancing neuroergonomics of motor rehabilitation scenarios.
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Affiliation(s)
- Andreas Meinel
- Brain State Decoding Lab, Cluster of Excellence BrainLinks-BrainTools, Department of Computer Science, Albert-Ludwigs-University Freiburg, Germany
| | - Sebastián Castaño-Candamil
- Brain State Decoding Lab, Cluster of Excellence BrainLinks-BrainTools, Department of Computer Science, Albert-Ludwigs-University Freiburg, Germany
| | - Janine Reis
- Department of Neurology, Albert-Ludwigs-University Freiburg, Germany
| | - Michael Tangermann
- Brain State Decoding Lab, Cluster of Excellence BrainLinks-BrainTools, Department of Computer Science, Albert-Ludwigs-University Freiburg, Germany
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286
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Luu TP, He Y, Brown S, Nakagame S, Contreras-Vidal JL. Gait adaptation to visual kinematic perturbations using a real-time closed-loop brain-computer interface to a virtual reality avatar. J Neural Eng 2016; 13:036006. [PMID: 27064824 DOI: 10.1088/1741-2560/13/3/036006] [Citation(s) in RCA: 66] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
OBJECTIVE The control of human bipedal locomotion is of great interest to the field of lower-body brain-computer interfaces (BCIs) for gait rehabilitation. While the feasibility of closed-loop BCI systems for the control of a lower body exoskeleton has been recently shown, multi-day closed-loop neural decoding of human gait in a BCI virtual reality (BCI-VR) environment has yet to be demonstrated. BCI-VR systems provide valuable alternatives for movement rehabilitation when wearable robots are not desirable due to medical conditions, cost, accessibility, usability, or patient preferences. APPROACH In this study, we propose a real-time closed-loop BCI that decodes lower limb joint angles from scalp electroencephalography (EEG) during treadmill walking to control a walking avatar in a virtual environment. Fluctuations in the amplitude of slow cortical potentials of EEG in the delta band (0.1-3 Hz) were used for prediction; thus, the EEG features correspond to time-domain amplitude modulated potentials in the delta band. Virtual kinematic perturbations resulting in asymmetric walking gait patterns of the avatar were also introduced to investigate gait adaptation using the closed-loop BCI-VR system over a period of eight days. MAIN RESULTS Our results demonstrate the feasibility of using a closed-loop BCI to learn to control a walking avatar under normal and altered visuomotor perturbations, which involved cortical adaptations. The average decoding accuracies (Pearson's r values) in real-time BCI across all subjects increased from (Hip: 0.18 ± 0.31; Knee: 0.23 ± 0.33; Ankle: 0.14 ± 0.22) on Day 1 to (Hip: 0.40 ± 0.24; Knee: 0.55 ± 0.20; Ankle: 0.29 ± 0.22) on Day 8. SIGNIFICANCE These findings have implications for the development of a real-time closed-loop EEG-based BCI-VR system for gait rehabilitation after stroke and for understanding cortical plasticity induced by a closed-loop BCI-VR system.
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287
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Naros G, Naros I, Grimm F, Ziemann U, Gharabaghi A. Reinforcement learning of self-regulated sensorimotor β-oscillations improves motor performance. Neuroimage 2016; 134:142-152. [PMID: 27046109 DOI: 10.1016/j.neuroimage.2016.03.016] [Citation(s) in RCA: 36] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2015] [Revised: 01/29/2016] [Accepted: 03/07/2016] [Indexed: 10/22/2022] Open
Abstract
Self-regulation of sensorimotor oscillations is currently researched in neurorehabilitation, e.g. for priming subsequent physiotherapy in stroke patients, and may be modulated by neurofeedback or transcranial brain stimulation. It has still to be demonstrated, however, whether and under which training conditions such brain self-regulation could also result in motor gains. Thirty-two right-handed, healthy subjects participated in a three-day intervention during which they performed 462 trials of kinesthetic motor-imagery while a brain-robot interface (BRI) turned event-related β-band desynchronization of the left sensorimotor cortex into the opening of the right hand by a robotic orthosis. Different training conditions were compared in a parallel-group design: (i) adaptive classifier thresholding and contingent feedback, (ii) adaptive classifier thresholding and non-contingent feedback, (iii) non-adaptive classifier thresholding and contingent feedback, and (iv) non-adaptive classifier thresholding and non-contingent feedback. We studied the task-related cortical physiology with electroencephalography and the behavioral performance in a subsequent isometric motor task. Contingent neurofeedback and adaptive classifier thresholding were critical for learning brain self-regulation which, in turn, led to behavioral gains after the intervention. The acquired skill for sustained sensorimotor β-desynchronization correlated significantly with subsequent motor improvement. Operant learning of brain self-regulation with a BRI may offer a therapeutic perspective for severely affected stroke patients lacking residual hand function.
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Affiliation(s)
- G Naros
- Division of Functional and Restorative Neurosurgery, University of Tuebingen, Germany.
| | - I Naros
- Division of Functional and Restorative Neurosurgery, University of Tuebingen, Germany; Centre for Integrative Neuroscience, University of Tuebingen, Germany
| | - F Grimm
- Division of Functional and Restorative Neurosurgery, University of Tuebingen, Germany; Centre for Integrative Neuroscience, University of Tuebingen, Germany
| | - U Ziemann
- Department of Neurology and Stroke, University of Tuebingen, Germany; Hertie Institute for Clinical Brain Research, University of Tuebingen, Germany
| | - A Gharabaghi
- Division of Functional and Restorative Neurosurgery, University of Tuebingen, Germany; Centre for Integrative Neuroscience, University of Tuebingen, Germany.
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288
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Moritz CT, Ruther P, Goering S, Stett A, Ball T, Burgard W, Chudler EH, Rao RPN. New Perspectives on Neuroengineering and Neurotechnologies: NSF-DFG Workshop Report. IEEE Trans Biomed Eng 2016; 63:1354-67. [PMID: 27008657 DOI: 10.1109/tbme.2016.2543662] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
GOAL To identify and overcome barriers to creating new neurotechnologies capable of restoring both motor and sensory function in individuals with neurological conditions. METHODS This report builds upon the outcomes of a joint workshop between the US National Science Foundation and the German Research Foundation on New Perspectives in Neuroengineering and Neurotechnology convened in Arlington, VA, USA, November 13-14, 2014. RESULTS The participants identified key technological challenges for recording and manipulating neural activity, decoding, and interpreting brain data in the presence of plasticity, and early considerations of ethical and social issues pertinent to the adoption of neurotechnologies. CONCLUSIONS The envisaged progress in neuroengineering requires tightly integrated hardware and signal processing efforts, advances in understanding of physiological adaptations to closed-loop interactions with neural devices, and an open dialog with stakeholders and potential end-users of neurotechnology. SIGNIFICANCE The development of new neurotechnologies (e.g., bidirectional brain-computer interfaces) could significantly improve the quality of life of people living with the effects of brain or spinal cord injury, or other neurodegenerative diseases. Focused efforts aimed at overcoming the remaining barriers at the electrode tissue interface, developing implantable hardware with on-board computation, and refining stimulation methods to precisely activate neural tissue will advance both our understanding of brain function and our ability to treat currently intractable disorders of the nervous system.
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289
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Hanakawa T. Organizing motor imageries. Neurosci Res 2016; 104:56-63. [DOI: 10.1016/j.neures.2015.11.003] [Citation(s) in RCA: 67] [Impact Index Per Article: 8.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2015] [Revised: 11/06/2015] [Accepted: 11/09/2015] [Indexed: 12/31/2022]
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290
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Barbosa S, Pires G, Nunes U. Toward a reliable gaze-independent hybrid BCI combining visual and natural auditory stimuli. J Neurosci Methods 2016; 261:47-61. [DOI: 10.1016/j.jneumeth.2015.11.026] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2015] [Revised: 11/26/2015] [Accepted: 11/26/2015] [Indexed: 12/13/2022]
Affiliation(s)
- Sara Barbosa
- Institute of Systems and Robotics, University of Coimbra, Coimbra, Portugal.
| | - Gabriel Pires
- Institute of Systems and Robotics, University of Coimbra, Coimbra, Portugal; Department of Engineering, Polytechnic Institute of Tomar, Tomar, Portugal.
| | - Urbano Nunes
- Institute of Systems and Robotics, University of Coimbra, Coimbra, Portugal; Department of Electrical and Computer Engineering, University of Coimbra, Coimbra, Portugal.
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291
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Hedlund M, Lindström B, Sojka P, Lundström R, Boraxbekk CJ. Is better preservation of eccentric strength after stroke due to altered prefrontal function? Neurocase 2016; 22:229-42. [PMID: 26750576 DOI: 10.1080/13554794.2015.1130232] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/17/2023]
Abstract
Ventrolateral prefrontal cortex (VLPFC) is part of a network that exerts inhibitory control over the motor cortex (MC). Recently, we demonstrated that VLPFC was more activated during imagined maximum eccentric than during imagined concentric contractions in healthy participants. This was accompanied with lower activation levels within motor regions during imagined eccentric contractions. The aim was to test a novel hypothesis of an involvement of VLPFC in contraction mode-specific modulation of force. Functional magnetic resonance imaging was used to examine differences in VLPFC and motor regions during the concentric and the eccentric phases of imagined maximum contractions in a selected sample of subjects with stroke (n = 4). The subjects were included as they exhibited disturbed modulation of force. The previously demonstrated pattern within VLPFC was evident only on the contralesional hemisphere. On the ipsilesional hemisphere, the recruitment in VLPFC was similar for both modes of contractions. The findings support a hypothesis of the involvement of VLPFC in contraction mode-specific modulation of maximum force production. A disturbance of this system might underlie the lack of contraction mode-specific modulation commonly found among stroke subjects, often expressed as an increased ratio between eccentric and concentric strength.
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Affiliation(s)
- Mattias Hedlund
- a Department of Community Medicine and Rehabilitation , Umeå University , Umeå , Sweden
| | - Britta Lindström
- a Department of Community Medicine and Rehabilitation , Umeå University , Umeå , Sweden
| | - Peter Sojka
- b Department of Health Sciences , Mid-Sweden University , Östersund , Sweden
| | - Ronnie Lundström
- c Department of Radiation Sciences, Biomedical Engineering , Umeå University , Umeå , Sweden
| | - Carl-Johan Boraxbekk
- d CEDAR, Center for Demographic and Aging Research , Umeå University , Umeå , Sweden.,e UFBI, Umeå Centre for Functional Brain Imaging , Umeå University , Umeå , Sweden
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292
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Brain–robot interface driven plasticity: Distributed modulation of corticospinal excitability. Neuroimage 2016; 125:522-532. [DOI: 10.1016/j.neuroimage.2015.09.074] [Citation(s) in RCA: 60] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2015] [Revised: 09/08/2015] [Accepted: 09/24/2015] [Indexed: 11/20/2022] Open
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293
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Mrachacz-Kersting N, Jiang N, Stevenson AJT, Niazi IK, Kostic V, Pavlovic A, Radovanovic S, Djuric-Jovicic M, Agosta F, Dremstrup K, Farina D. Efficient neuroplasticity induction in chronic stroke patients by an associative brain-computer interface. J Neurophysiol 2015; 115:1410-21. [PMID: 26719088 DOI: 10.1152/jn.00918.2015] [Citation(s) in RCA: 146] [Impact Index Per Article: 16.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2015] [Accepted: 12/18/2015] [Indexed: 01/12/2023] Open
Abstract
Brain-computer interfaces (BCIs) have the potential to improve functionality in chronic stoke patients when applied over a large number of sessions. Here we evaluated the effect and the underlying mechanisms of three BCI training sessions in a double-blind sham-controlled design. The applied BCI is based on Hebbian principles of associativity that hypothesize that neural assemblies activated in a correlated manner will strengthen synaptic connections. Twenty-two chronic stroke patients were divided into two training groups. Movement-related cortical potentials (MRCPs) were detected by electroencephalography during repetitions of foot dorsiflexion. Detection triggered a single electrical stimulation of the common peroneal nerve timed so that the resulting afferent volley arrived at the peak negative phase of the MRCP (BCIassociative group) or randomly (BCInonassociative group). Fugl-Meyer motor assessment (FM), 10-m walking speed, foot and hand tapping frequency, diffusion tensor imaging (DTI) data, and the excitability of the corticospinal tract to the target muscle [tibialis anterior (TA)] were quantified. The TA motor evoked potential (MEP) increased significantly after the BCIassociative intervention, but not for the BCInonassociative group. FM scores (0.8 ± 0.46 point difference, P = 0.01), foot (but not finger) tapping frequency, and 10-m walking speed improved significantly for the BCIassociative group, indicating clinically relevant improvements. Corticospinal tract integrity on DTI did not correlate with clinical or physiological changes. For the BCI as applied here, the precise coupling between the brain command and the afferent signal was imperative for the behavioral, clinical, and neurophysiological changes reported. This association may become the driving principle for the design of BCI rehabilitation in the future. Indeed, no available BCIs can match this degree of functional improvement with such a short intervention.
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Affiliation(s)
- Natalie Mrachacz-Kersting
- Center for Sensory-Motor Interaction (SMI), Department of Health Science and Technology, Aalborg University, Aalborg, Denmark;
| | - Ning Jiang
- Department of Neurorehabilitation Engineering, Bernstein Focus Neurotechnology Göttingen, Bernstein Center for Computational Neuroscience, University Medical Center Göttingen, Georg-August University, Göttingen, Germany
| | - Andrew James Thomas Stevenson
- Center for Sensory-Motor Interaction (SMI), Department of Health Science and Technology, Aalborg University, Aalborg, Denmark
| | - Imran Khan Niazi
- Center for Sensory-Motor Interaction (SMI), Department of Health Science and Technology, Aalborg University, Aalborg, Denmark
| | - Vladimir Kostic
- Neurology Clinic, Clinical Center of Serbia, Faculty of Medicine, University of Belgrade, Belgrade, Serbia
| | - Aleksandra Pavlovic
- Neurology Clinic, Clinical Center of Serbia, Faculty of Medicine, University of Belgrade, Belgrade, Serbia
| | - Sasa Radovanovic
- Neurology Clinic, Clinical Center of Serbia, Faculty of Medicine, University of Belgrade, Belgrade, Serbia
| | | | - Federica Agosta
- Neuroimaging Research Unit, Division of Neuroscience, Institute of Experimental Neurology, San Raffaele Scientific Institute, Vita-Salute San Raffaele University, Milan, Italy
| | - Kim Dremstrup
- Center for Sensory-Motor Interaction (SMI), Department of Health Science and Technology, Aalborg University, Aalborg, Denmark
| | - Dario Farina
- Department of Neurorehabilitation Engineering, Bernstein Focus Neurotechnology Göttingen, Bernstein Center for Computational Neuroscience, University Medical Center Göttingen, Georg-August University, Göttingen, Germany
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294
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From disorders of consciousness to early neurorehabilitation using assistive technologies in patients with severe brain damage. Curr Opin Neurol 2015; 28:587-94. [DOI: 10.1097/wco.0000000000000264] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/17/2023]
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295
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Brauchle D, Vukelić M, Bauer R, Gharabaghi A. Brain state-dependent robotic reaching movement with a multi-joint arm exoskeleton: combining brain-machine interfacing and robotic rehabilitation. Front Hum Neurosci 2015; 9:564. [PMID: 26528168 PMCID: PMC4607784 DOI: 10.3389/fnhum.2015.00564] [Citation(s) in RCA: 43] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2014] [Accepted: 09/25/2015] [Indexed: 11/13/2022] Open
Abstract
While robot-assisted arm and hand training after stroke allows for intensive task-oriented practice, it has provided only limited additional benefit over dose-matched physiotherapy up to now. These rehabilitation devices are possibly too supportive during the exercises. Neurophysiological signals might be one way of avoiding slacking and providing robotic support only when the brain is particularly responsive to peripheral input. We tested the feasibility of three-dimensional robotic assistance for reaching movements with a multi-joint exoskeleton during motor imagery (MI)-related desynchronization of sensorimotor oscillations in the β-band. We also registered task-related network changes of cortical functional connectivity by electroencephalography via the imaginary part of the coherence function. Healthy subjects and stroke survivors showed similar patterns—but different aptitudes—of controlling the robotic movement. All participants in this pilot study with nine healthy subjects and two stroke patients achieved their maximum performance during the early stages of the task. Robotic control was significantly higher and less variable when proprioceptive feedback was provided in addition to visual feedback, i.e., when the orthosis was actually attached to the subject’s arm during the task. A distributed cortical network of task-related coherent activity in the θ-band showed significant differences between healthy subjects and stroke patients as well as between early and late periods of the task. Brain-robot interfaces (BRIs) may successfully link three-dimensional robotic training to the participants’ efforts and allow for task-oriented practice of activities of daily living with a physiologically controlled multi-joint exoskeleton. Changes of cortical physiology during the task might also help to make subject-specific adjustments of task difficulty and guide adjunct interventions to facilitate motor learning for functional restoration, a proposal that warrants further investigation in a larger cohort of stroke patients.
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Affiliation(s)
- Daniel Brauchle
- Division of Functional and Restorative Neurosurgery and Division of Translational Neurosurgery, Department of Neurosurgery, Eberhard Karls University Tuebingen Tübingen, Germany ; Neuroprosthetics Research Group, Werner Reichardt Centre for Integrative Neuroscience, Eberhard Karls University Tuebingen Tübingen, Germany
| | - Mathias Vukelić
- Division of Functional and Restorative Neurosurgery and Division of Translational Neurosurgery, Department of Neurosurgery, Eberhard Karls University Tuebingen Tübingen, Germany ; Neuroprosthetics Research Group, Werner Reichardt Centre for Integrative Neuroscience, Eberhard Karls University Tuebingen Tübingen, Germany
| | - Robert Bauer
- Division of Functional and Restorative Neurosurgery and Division of Translational Neurosurgery, Department of Neurosurgery, Eberhard Karls University Tuebingen Tübingen, Germany ; Neuroprosthetics Research Group, Werner Reichardt Centre for Integrative Neuroscience, Eberhard Karls University Tuebingen Tübingen, Germany
| | - Alireza Gharabaghi
- Division of Functional and Restorative Neurosurgery and Division of Translational Neurosurgery, Department of Neurosurgery, Eberhard Karls University Tuebingen Tübingen, Germany ; Neuroprosthetics Research Group, Werner Reichardt Centre for Integrative Neuroscience, Eberhard Karls University Tuebingen Tübingen, Germany
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Edelman BJ, Johnson N, Sohrabpour A, Tong S, Thakor N, He B. Systems Neuroengineering: Understanding and Interacting with the Brain. ENGINEERING (BEIJING, CHINA) 2015; 1:292-308. [PMID: 34336364 PMCID: PMC8323844 DOI: 10.15302/j-eng-2015078] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/15/2023]
Abstract
In this paper, we review the current state-of-the-art techniques used for understanding the inner workings of the brain at a systems level. The neural activity that governs our everyday lives involves an intricate coordination of many processes that can be attributed to a variety of brain regions. On the surface, many of these functions can appear to be controlled by specific anatomical structures; however, in reality, numerous dynamic networks within the brain contribute to its function through an interconnected web of neuronal and synaptic pathways. The brain, in its healthy or pathological state, can therefore be best understood by taking a systems-level approach. While numerous neuroengineering technologies exist, we focus here on three major thrusts in the field of systems neuroengineering: neuroimaging, neural interfacing, and neuromodulation. Neuroimaging enables us to delineate the structural and functional organization of the brain, which is key in understanding how the neural system functions in both normal and disease states. Based on such knowledge, devices can be used either to communicate with the neural system, as in neural interface systems, or to modulate brain activity, as in neuromodulation systems. The consideration of these three fields is key to the development and application of neuro-devices. Feedback-based neuro-devices require the ability to sense neural activity (via a neuroimaging modality) through a neural interface (invasive or noninvasive) and ultimately to select a set of stimulation parameters in order to alter neural function via a neuromodulation modality. Systems neuroengineering refers to the use of engineering tools and technologies to image, decode, and modulate the brain in order to comprehend its functions and to repair its dysfunction. Interactions between these fields will help to shape the future of systems neuroengineering-to develop neurotechniques for enhancing the understanding of whole-brain function and dysfunction, and the management of neurological and mental disorders.
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Affiliation(s)
- Bradley J. Edelman
- Department of Biomedical Engineering, University of Minnesota, Minneapolis, MN 55455, USA
| | - Nessa Johnson
- Department of Biomedical Engineering, University of Minnesota, Minneapolis, MN 55455, USA
| | - Abbas Sohrabpour
- Department of Biomedical Engineering, University of Minnesota, Minneapolis, MN 55455, USA
| | - Shanbao Tong
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200030, China
| | - Nitish Thakor
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21205, USA
- SINAPSE Institute, National University of Singapore, Singapore
| | - Bin He
- Department of Biomedical Engineering, University of Minnesota, Minneapolis, MN 55455, USA
- Institute for Engineering in Medicine, University of Minnesota, Minneapolis, MN 55455, USA
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297
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Matsubara M, Kayanuma H, Ono Y, Omatsu S, Tominaga T. Determination of appropriate imagery task to discriminate ERD of "Pinch" and "Hold" movements in healthy participants and stroke patients. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2015; 2015:4598-4601. [PMID: 26737318 DOI: 10.1109/embc.2015.7319418] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/05/2023]
Abstract
Using video clips of hand movement to facilitate motor imagery and evoke strong ERD, we investigated the appropriate imagery task to discriminate ERD of "Pinch" and "Hold" movements in healthy controls and stroke patients. Participants watched 4 types of video clips that were a combination of 2 hand actions (pinch a small metal ball ("Pinch") or hold a tennis ball ("Hold")) and 2 object properties (ball rolls into the hand ("Move" condition) or stay still close to the hand ("Stop" condition)). The mean decrease in the mu band power from the baseline was 47.0±8.8% and 45.9±5.5% in healthy controls and patients, respectively, showing that all video clips evoked stable ERD. However, the relationship in ERD power between these hand actions varied across participants and object properties. We therefore calculated the absolute value of difference in ERD power between two hand actions (adERD) to further investigate the appropriate object property of the video clip stimuli. The mean adERD across participants and object properties was 22.3±6.0% in healthy controls and 33.9±6.6% in patients, showing a good contrast between the ERD powers between 2 hand actions. The adERD was significantly larger in Move condition than Stop condition at electrode C6 in healthy participants for left hand imagery. Similar tendency was also found at electrode C3 in right paralyzed patients. The post-experiment questionnaire demonstrated that the ease in hand motor imagery was greater in Move condition compared to Stop condition in all participant groups. These results suggest that the video clip stimuli with Move object properties are appropriate to induce stable ERD for classification of Pinch and Hold movements.
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298
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Naros G, Gharabaghi A. Reinforcement learning of self-regulated β-oscillations for motor restoration in chronic stroke. Front Hum Neurosci 2015; 9:391. [PMID: 26190995 PMCID: PMC4490244 DOI: 10.3389/fnhum.2015.00391] [Citation(s) in RCA: 54] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2015] [Accepted: 06/19/2015] [Indexed: 11/21/2022] Open
Abstract
Neurofeedback training of Motor imagery (MI)-related brain-states with brain-computer/brain-machine interfaces (BCI/BMI) is currently being explored as an experimental intervention prior to standard physiotherapy to improve the motor outcome of stroke rehabilitation. The use of BCI/BMI technology increases the adherence to MI training more efficiently than interventions with sham or no feedback. Moreover, pilot studies suggest that such a priming intervention before physiotherapy might—like some brain stimulation techniques—increase the responsiveness of the brain to the subsequent physiotherapy, thereby improving the general clinical outcome. However, there is little evidence up to now that these BCI/BMI-based interventions have achieved operate conditioning of specific brain states that facilitate task-specific functional gains beyond the practice of primed physiotherapy. In this context, we argue that BCI/BMI technology provides a valuable neurofeedback tool for rehabilitation but needs to aim at physiological features relevant for the targeted behavioral gain. Moreover, this therapeutic intervention has to be informed by concepts of reinforcement learning to develop its full potential. Such a refined neurofeedback approach would need to address the following issues: (1) Defining a physiological feedback target specific to the intended behavioral gain, e.g., β-band oscillations for cortico-muscular communication. This targeted brain state could well be different from the brain state optimal for the neurofeedback task, e.g., α-band oscillations for differentiating MI from rest; (2) Selecting a BCI/BMI classification and thresholding approach on the basis of learning principles, i.e., balancing challenge and reward of the neurofeedback task instead of maximizing the classification accuracy of the difficulty level device; and (3) Adjusting the difficulty level in the course of the training period to account for the cognitive load and the learning experience of the participant. Here, we propose a comprehensive neurofeedback strategy for motor restoration after stroke that addresses these aspects, and provide evidence for the feasibility of the suggested approach by demonstrating that dynamic threshold adaptation based on reinforcement learning may lead to frequency-specific operant conditioning of β-band oscillations paralleled by task-specific motor improvement; a proposal that requires investigation in a larger cohort of stroke patients.
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Affiliation(s)
- Georgios Naros
- Division of Functional and Restorative Neurosurgery and Division of Translational Neurosurgery, Department of Neurosurgery, Eberhard Karls University Tuebingen Tuebingen, Germany ; Neuroprosthetics Research Group, Werner Reichardt Centre for Integrative Neuroscience, Eberhard Karls University Tuebingen Tuebingen, Germany
| | - Alireza Gharabaghi
- Division of Functional and Restorative Neurosurgery and Division of Translational Neurosurgery, Department of Neurosurgery, Eberhard Karls University Tuebingen Tuebingen, Germany ; Neuroprosthetics Research Group, Werner Reichardt Centre for Integrative Neuroscience, Eberhard Karls University Tuebingen Tuebingen, Germany
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299
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Pereira M, Sobolewski A, Millan JDR. Modulation of the inter-hemispheric asymmetry of motor-related brain activity using brain-computer interfaces. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2015; 2015:2319-2322. [PMID: 26736757 DOI: 10.1109/embc.2015.7318857] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/05/2023]
Abstract
Non-invasive brain stimulation has shown promising results in neurorehabilitation for motor-impaired stroke patients, by rebalancing the relative involvement of each hemisphere in movement generation. Similarly, brain-computer interfaces have been used to successfully facilitate movement-related brain activity spared by the infarct. We propose to merge both approaches by using BCI to train stroke patients to rebalance their motor-related brain activity during motor tasks, through the use of online feedback. In this pilot study, we report results showing that some healthy subjects were able to learn to spontaneously up- and/or down-regulate their ipsilateral brain activity during a single session.
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