101
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Bulea TC, Prasad S, Kilicarslan A, Contreras-Vidal JL. Sitting and standing intention can be decoded from scalp EEG recorded prior to movement execution. Front Neurosci 2014; 8:376. [PMID: 25505377 PMCID: PMC4243562 DOI: 10.3389/fnins.2014.00376] [Citation(s) in RCA: 78] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2014] [Accepted: 11/04/2014] [Indexed: 12/18/2022] Open
Abstract
Low frequency signals recorded from non-invasive electroencephalography (EEG), in particular movement-related cortical potentials (MRPs), are associated with preparation and execution of movement and thus present a target for use in brain-machine interfaces. We investigated the ability to decode movement intent from delta-band (0.1-4 Hz) EEG recorded immediately before movement execution in healthy volunteers. We used data from epochs starting 1.5 s before movement onset to classify future movements into one of three classes: stand-up, sit-down, or quiet. We assessed classification accuracy in both externally triggered and self-paced paradigms. Movement onset was determined from electromyography (EMG) recordings synchronized with EEG signals. We employed an artifact subspace reconstruction (ASR) algorithm to eliminate high amplitude noise before building our time-embedded EEG features. We applied local Fisher's discriminant analysis to reduce the dimensionality of our spatio-temporal features and subsequently used a Gaussian mixture model classifier for our three class problem. Our results demonstrate significantly better than chance classification accuracy (chance level = 33.3%) for the self-initiated (78.0 ± 2.6%) and triggered (74.7 ± 5.7%) paradigms. Surprisingly, we found no significant difference in classification accuracy between the self-paced and cued paradigms when using the full set of non-peripheral electrodes. However, accuracy was significantly increased for self-paced movements when only electrodes over the primary motor area were used. Overall, this study demonstrates that delta-band EEG recorded immediately before movement carries discriminative information regarding movement type. Our results suggest that EEG-based classifiers could improve lower-limb neuroprostheses and neurorehabilitation techniques by providing earlier detection of movement intent, which could be used in robot-assisted strategies for motor training and recovery of function.
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Affiliation(s)
- Thomas C Bulea
- Functional and Applied Biomechanics Section, Rehabilitation Medicine Department, National Institutes of Health Bethesda, MD, USA ; Laboratory for Non-invasive Brain-Machine Interface Systems, Department of Electrical and Computer Engineering, University of Houston Houston, TX, USA
| | - Saurabh Prasad
- Laboratory for Non-invasive Brain-Machine Interface Systems, Department of Electrical and Computer Engineering, University of Houston Houston, TX, USA
| | - Atilla Kilicarslan
- Laboratory for Non-invasive Brain-Machine Interface Systems, Department of Electrical and Computer Engineering, University of Houston Houston, TX, USA
| | - Jose L Contreras-Vidal
- Laboratory for Non-invasive Brain-Machine Interface Systems, Department of Electrical and Computer Engineering, University of Houston Houston, TX, USA
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102
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Jaeger L, Marchal-Crespo L, Wolf P, Riener R, Michels L, Kollias S. Brain activation associated with active and passive lower limb stepping. Front Hum Neurosci 2014; 8:828. [PMID: 25389396 PMCID: PMC4211402 DOI: 10.3389/fnhum.2014.00828] [Citation(s) in RCA: 49] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2014] [Accepted: 09/29/2014] [Indexed: 11/14/2022] Open
Abstract
Reports about standardized and repeatable experimental procedures investigating supraspinal activation in patients with gait disorders are scarce in current neuro-imaging literature. Well-designed and executed tasks are important to gain insight into the effects of gait-rehabilitation on sensorimotor centers of the brain. The present study aims to demonstrate the feasibility of a novel imaging paradigm, combining the magnetic resonance (MR)-compatible stepping robot (MARCOS) with sparse sampling functional magnetic resonance imaging (fMRI) to measure task-related BOLD signal changes and to delineate the supraspinal contribution specific to active and passive stepping. Twenty-four healthy participants underwent fMRI during active and passive, periodic, bilateral, multi-joint, lower limb flexion and extension akin to human gait. Active and passive stepping engaged several cortical and subcortical areas of the sensorimotor network, with higher relative activation of those areas during active movement. Our results indicate that the combination of MARCOS and sparse sampling fMRI is feasible for the detection of lower limb motor related supraspinal activation. Activation of the anterior cingulate and medial frontal areas suggests motor response inhibition during passive movement in healthy participants. Our results are of relevance for understanding the neural mechanisms underlying gait in the healthy.
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Affiliation(s)
- Lukas Jaeger
- Sensory-Motor Systems Lab, Department of Health Sciences and Technology, Eidgenössische Technische Hochschule Zürich Zürich, Switzerland ; Medical Faculty, University of Zurich Zurich, Switzerland ; Clinic of Neuroradiology, University Hospital of Zurich Zurich, Switzerland
| | - Laura Marchal-Crespo
- Sensory-Motor Systems Lab, Department of Health Sciences and Technology, Eidgenössische Technische Hochschule Zürich Zürich, Switzerland ; Medical Faculty, University of Zurich Zurich, Switzerland
| | - Peter Wolf
- Sensory-Motor Systems Lab, Department of Health Sciences and Technology, Eidgenössische Technische Hochschule Zürich Zürich, Switzerland ; Medical Faculty, University of Zurich Zurich, Switzerland
| | - Robert Riener
- Sensory-Motor Systems Lab, Department of Health Sciences and Technology, Eidgenössische Technische Hochschule Zürich Zürich, Switzerland ; Medical Faculty, University of Zurich Zurich, Switzerland
| | - Lars Michels
- Clinic of Neuroradiology, University Hospital of Zurich Zurich, Switzerland ; Center of MR-Research, University Children's Hospital Zurich, Switzerland
| | - Spyros Kollias
- Clinic of Neuroradiology, University Hospital of Zurich Zurich, Switzerland
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103
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Balasubramanian CK, Clark DJ, Fox EJ. Walking adaptability after a stroke and its assessment in clinical settings. Stroke Res Treat 2014; 2014:591013. [PMID: 25254140 PMCID: PMC4164852 DOI: 10.1155/2014/591013] [Citation(s) in RCA: 69] [Impact Index Per Article: 6.9] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2014] [Accepted: 06/06/2014] [Indexed: 11/17/2022] Open
Abstract
Control of walking has been described by a tripartite model consisting of stepping, equilibrium, and adaptability. This review focuses on walking adaptability, which is defined as the ability to modify walking to meet task goals and environmental demands. Walking adaptability is crucial to safe ambulation in the home and community environments and is often severely compromised after a stroke. Yet quantification of walking adaptability after stroke has received relatively little attention in the clinical setting. The objectives of this review were to examine the conceptual challenges for clinical measurement of walking adaptability and summarize the current state of clinical assessment for walking adaptability. We created nine domains of walking adaptability from dimensions of community mobility to address the conceptual challenges in measurement and reviewed performance-based clinical assessments of walking to determine if the assessments measure walking adaptability in these domains. Our literature review suggests the lack of a comprehensive well-tested clinical assessment tool for measuring walking adaptability. Accordingly, recommendations for the development of a comprehensive clinical assessment of walking adaptability after stroke have been presented. Such a clinical assessment will be essential for gauging recovery of walking adaptability with rehabilitation and for motivating novel strategies to enhance recovery of walking adaptability after stroke.
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Affiliation(s)
| | - David J. Clark
- Brain Rehabilitation Research Center (151A), Malcom Randall VA Medical Center, 1601 SW Archer Roadd, Gainesville, FL 32608, USA
- Department of Aging and Geriatric Research, University of Florida, Gainesville, FL 32603, USA
| | - Emily J. Fox
- Department of Physical Therapy, University of Florida, P.O. Box 100154, Gainesville, FL 32610-0154, USA
- Brooks Rehabilitation, Jacksonville, FL 32216, USA
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104
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A study on decoding models for the reconstruction of hand trajectories from the human magnetoencephalography. BIOMED RESEARCH INTERNATIONAL 2014; 2014:176857. [PMID: 25050324 PMCID: PMC4090526 DOI: 10.1155/2014/176857] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/28/2014] [Accepted: 05/21/2014] [Indexed: 11/18/2022]
Abstract
Decoding neural signals into control outputs has been a key to the development of brain-computer interfaces (BCIs). While many studies have identified neural correlates of kinematics or applied advanced machine learning algorithms to improve decoding performance, relatively less attention has been paid to optimal design of decoding models. For generating continuous movements from neural activity, design of decoding models should address how to incorporate movement dynamics into models and how to select a model given specific BCI objectives. Considering nonlinear and independent speed characteristics, we propose a hybrid Kalman filter to decode the hand direction and speed independently. We also investigate changes in performance of different decoding models (the linear and Kalman filters) when they predict reaching movements only or predict both reach and rest. Our offline study on human magnetoencephalography (MEG) during point-to-point arm movements shows that the performance of the linear filter or the Kalman filter is affected by including resting states for training and predicting movements. However, the hybrid Kalman filter consistently outperforms others regardless of movement states. The results demonstrate that better design of decoding models is achieved by incorporating movement dynamics into modeling or selecting a model according to decoding objectives.
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105
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David Hairston W, Whitaker KW, Ries AJ, Vettel JM, Cortney Bradford J, Kerick SE, McDowell K. Usability of four commercially-oriented EEG systems. J Neural Eng 2014; 11:046018. [PMID: 24980915 DOI: 10.1088/1741-2560/11/4/046018] [Citation(s) in RCA: 82] [Impact Index Per Article: 8.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
Electroencephalography (EEG) holds promise as a neuroimaging technology that can be used to understand how the human brain functions in real-world, operational settings while individuals move freely in perceptually-rich environments. In recent years, several EEG systems have been developed that aim to increase the usability of the neuroimaging technology in real-world settings. Here, the usability of three wireless EEG systems from different companies are compared to a conventional wired EEG system, BioSemi's ActiveTwo, which serves as an established laboratory-grade 'gold standard' baseline. The wireless systems compared include Advanced Brain Monitoring's B-Alert X10, Emotiv Systems' EPOC and the 2009 version of QUASAR's Dry Sensor Interface 10-20. The design of each wireless system is discussed in relation to its impact on the system's usability as a potential real-world neuroimaging system. Evaluations are based on having participants complete a series of cognitive tasks while wearing each of the EEG acquisition systems. This report focuses on the system design, usability factors and participant comfort issues that arise during the experimental sessions. In particular, the EEG systems are assessed on five design elements: adaptability of the system for differing head sizes, subject comfort and preference, variance in scalp locations for the recording electrodes, stability of the electrical connection between the scalp and electrode, and timing integration between the EEG system, the stimulus presentation computer and other external events.
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Affiliation(s)
- W David Hairston
- US Army Research Laboratory, Human Research and Engineering Directorate, Translational Neuroscience Branch, Aberdeen Proving Ground, MD, USA
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106
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Nonlinear EEG decoding based on a particle filter model. BIOMED RESEARCH INTERNATIONAL 2014; 2014:159486. [PMID: 24949420 PMCID: PMC4052086 DOI: 10.1155/2014/159486] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/28/2014] [Revised: 04/16/2014] [Accepted: 04/18/2014] [Indexed: 11/24/2022]
Abstract
While the world is stepping into the aging society, rehabilitation robots play a more and more important role in terms of both rehabilitation treatment and nursing of the patients with neurological diseases. Benefiting from the abundant contents of movement information, electroencephalography (EEG) has become a promising information source for rehabilitation robots control. Although the multiple linear regression model was used as the decoding model of EEG signals in some researches, it has been considered that it cannot reflect the nonlinear components of EEG signals. In order to overcome this shortcoming, we propose a nonlinear decoding model, the particle filter model. Two- and three-dimensional decoding experiments were performed to test the validity of this model. In decoding accuracy, the results are comparable to those of the multiple linear regression model and previous EEG studies. In addition, the particle filter model uses less training data and more frequency information than the multiple linear regression model, which shows the potential of nonlinear decoding models. Overall, the findings hold promise for the furtherance of EEG-based rehabilitation robots.
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107
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Numerical processing in the human parietal cortex during experimental and natural conditions. Nat Commun 2014; 4:2528. [PMID: 24129341 PMCID: PMC3826627 DOI: 10.1038/ncomms3528] [Citation(s) in RCA: 55] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2013] [Accepted: 08/30/2013] [Indexed: 11/16/2022] Open
Abstract
Human cognition is traditionally studied in experimental conditions wherein confounding complexities of the natural environment are intentionally eliminated. Thus, it remains unknown how a brain region involved in a particular experimental condition is engaged in natural conditions. Here we use electrocorticography to address this uncertainty in three participants implanted with intracranial electrodes and identify activations of neuronal populations within the intraparietal sulcus region during an experimental arithmetic condition. In a subsequent analysis, we report that the same intraparietal sulcus neural populations are activated when participants, engaged in social conversations, refer to objects with numerical content. Our prototype approach provides a means for both exploring human brain dynamics as they unfold in complex social settings and reconstructing natural experiences from recorded brain signals. Human neuronal activity during cognitive processing is usually studied under experimental conditions but activity under natural conditions is poorly understood. Here the authors develop a method to accurately characterize the activity of the same neuronal population under both conditions.
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108
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Cruz-Garza JG, Hernandez ZR, Nepaul S, Bradley KK, Contreras-Vidal JL. Neural decoding of expressive human movement from scalp electroencephalography (EEG). Front Hum Neurosci 2014; 8:188. [PMID: 24782734 PMCID: PMC3986521 DOI: 10.3389/fnhum.2014.00188] [Citation(s) in RCA: 43] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2013] [Accepted: 03/14/2014] [Indexed: 12/03/2022] Open
Abstract
Although efforts to characterize human movement through electroencephalography (EEG) have revealed neural activities unique to limb control that can be used to infer movement kinematics, it is still unknown the extent to which EEG can be used to discern the expressive qualities that influence such movements. In this study we used EEG and inertial sensors to record brain activity and movement of five skilled and certified Laban Movement Analysis (LMA) dancers. Each dancer performed whole body movements of three Action types: movements devoid of expressive qualities (“Neutral”), non-expressive movements while thinking about specific expressive qualities (“Think”), and enacted expressive movements (“Do”). The expressive movement qualities that were used in the “Think” and “Do” actions consisted of a sequence of eight Laban Effort qualities as defined by LMA—a notation system and language for describing, visualizing, interpreting and documenting all varieties of human movement. We used delta band (0.2–4 Hz) EEG as input to a machine learning algorithm that computed locality-preserving Fisher's discriminant analysis (LFDA) for dimensionality reduction followed by Gaussian mixture models (GMMs) to decode the type of Action. We also trained our LFDA-GMM models to classify all the possible combinations of Action Type and Laban Effort quality (giving a total of 17 classes). Classification accuracy rates were 59.4 ± 0.6% for Action Type and 88.2 ± 0.7% for Laban Effort quality Type. Ancillary analyses of the potential relations between the EEG and movement kinematics of the dancer's body, indicated that motion-related artifacts did not significantly influence our classification results. In summary, this research demonstrates that EEG has valuable information about the expressive qualities of movement. These results may have applications for advancing the understanding of the neural basis of expressive movements and for the development of neuroprosthetics to restore movements.
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Affiliation(s)
- Jesus G Cruz-Garza
- Laboratory for Noninvasive Brain-Machine Interface Systems, Department of Electrical and Computer Engineering, University of Houston Houston, TX, USA ; Center for Robotics and Intelligent Systems, Instituto Tecnológico y de Estudios Superiores de Monterrey Monterrey, Mexico
| | - Zachery R Hernandez
- Laboratory for Noninvasive Brain-Machine Interface Systems, Department of Electrical and Computer Engineering, University of Houston Houston, TX, USA ; Department of Biomedical Engineering, University of Houston Houston, TX, USA
| | - Sargoon Nepaul
- Department of Neurobiology, University of Maryland, College Park MD, USA
| | - Karen K Bradley
- Department of Dance, University of Maryland, College Park MD, USA
| | - Jose L Contreras-Vidal
- Laboratory for Noninvasive Brain-Machine Interface Systems, Department of Electrical and Computer Engineering, University of Houston Houston, TX, USA ; Department of Biomedical Engineering, University of Houston Houston, TX, USA
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109
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Wagner J, Solis-Escalante T, Scherer R, Neuper C, Müller-Putz G. It's how you get there: walking down a virtual alley activates premotor and parietal areas. Front Hum Neurosci 2014; 8:93. [PMID: 24611043 PMCID: PMC3933811 DOI: 10.3389/fnhum.2014.00093] [Citation(s) in RCA: 109] [Impact Index Per Article: 10.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2013] [Accepted: 02/07/2014] [Indexed: 11/13/2022] Open
Abstract
Voluntary drive is crucial for motor learning, therefore we are interested in the role that motor planning plays in gait movements. In this study we examined the impact of an interactive Virtual Environment (VE) feedback task on the EEG patterns during robot assisted walking. We compared walking in the VE modality to two control conditions: walking with a visual attention paradigm, in which visual stimuli were unrelated to the motor task; and walking with mirror feedback, in which participants observed their own movements. Eleven healthy participants were considered. Application of independent component analysis to the EEG revealed three independent component clusters in premotor and parietal areas showing increased activity during walking with the adaptive VE training paradigm compared to the control conditions. During the interactive VE walking task spectral power in frequency ranges 8-12, 15-20, and 23-40 Hz was significantly (p ≤ 0.05) decreased. This power decrease is interpreted as a correlate of an active cortical area. Furthermore activity in the premotor cortex revealed gait cycle related modulations significantly different (p ≤ 0.05) from baseline in the frequency range 23-40 Hz during walking. These modulations were significantly (p ≤ 0.05) reduced depending on gait cycle phases in the interactive VE walking task compared to the control conditions. We demonstrate that premotor and parietal areas show increased activity during walking with the adaptive VE training paradigm, when compared to walking with mirror- and movement unrelated feedback. Previous research has related a premotor-parietal network to motor planning and motor intention. We argue that movement related interactive feedback enhances motor planning and motor intention. We hypothesize that this might improve gait recovery during rehabilitation.
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Affiliation(s)
- Johanna Wagner
- Laboratory of Brain-Computer Interfaces, Institute for Knowledge Discovery, BioTechMed, Graz University of TechnologyGraz, Austria
| | - Teodoro Solis-Escalante
- Laboratory of Brain-Computer Interfaces, Institute for Knowledge Discovery, BioTechMed, Graz University of TechnologyGraz, Austria
- Department of Biomechanical Engineering, Delft University of TechnologyDelft, Netherlands
| | - Reinhold Scherer
- Laboratory of Brain-Computer Interfaces, Institute for Knowledge Discovery, BioTechMed, Graz University of TechnologyGraz, Austria
- Rehabilitation Clinic Judendorf-StrassengelJudendorf-Strassengel, Austria
| | - Christa Neuper
- Laboratory of Brain-Computer Interfaces, Institute for Knowledge Discovery, BioTechMed, Graz University of TechnologyGraz, Austria
- Department of Psychology, BioTechMed, University of GrazGraz, Austria
| | - Gernot Müller-Putz
- Laboratory of Brain-Computer Interfaces, Institute for Knowledge Discovery, BioTechMed, Graz University of TechnologyGraz, Austria
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110
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About the cortical origin of the low-delta and high-gamma rhythms observed in EEG signals during treadmill walking. Neurosci Lett 2014; 561:166-70. [DOI: 10.1016/j.neulet.2013.12.059] [Citation(s) in RCA: 114] [Impact Index Per Article: 11.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2013] [Revised: 12/09/2013] [Accepted: 12/21/2013] [Indexed: 11/29/2022]
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111
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Koenraadt KLM, Roelofsen EGJ, Duysens J, Keijsers NLW. Cortical control of normal gait and precision stepping: An fNIRS study. Neuroimage 2014; 85 Pt 1:415-22. [PMID: 23631980 DOI: 10.1016/j.neuroimage.2013.04.070] [Citation(s) in RCA: 152] [Impact Index Per Article: 15.2] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2013] [Revised: 03/29/2013] [Accepted: 04/19/2013] [Indexed: 11/28/2022] Open
Affiliation(s)
- Koen L M Koenraadt
- Sint Maartenskliniek Nijmegen, Department of Research, PO box 9011, 6500 GM Nijmegen, The Netherlands.
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112
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Castermans T, Duvinage M, Cheron G, Dutoit T. Towards effective non-invasive brain-computer interfaces dedicated to gait rehabilitation systems. Brain Sci 2013; 4:1-48. [PMID: 24961699 PMCID: PMC4066236 DOI: 10.3390/brainsci4010001] [Citation(s) in RCA: 33] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2013] [Revised: 11/05/2013] [Accepted: 12/12/2013] [Indexed: 12/24/2022] Open
Abstract
In the last few years, significant progress has been made in the field of walk rehabilitation. Motor cortex signals in bipedal monkeys have been interpreted to predict walk kinematics. Epidural electrical stimulation in rats and in one young paraplegic has been realized to partially restore motor control after spinal cord injury. However, these experimental trials are far from being applicable to all patients suffering from motor impairments. Therefore, it is thought that more simple rehabilitation systems are desirable in the meanwhile. The goal of this review is to describe and summarize the progress made in the development of non-invasive brain-computer interfaces dedicated to motor rehabilitation systems. In the first part, the main principles of human locomotion control are presented. The paper then focuses on the mechanisms of supra-spinal centers active during gait, including results from electroencephalography, functional brain imaging technologies [near-infrared spectroscopy (NIRS), functional magnetic resonance imaging (fMRI), positron-emission tomography (PET), single-photon emission-computed tomography (SPECT)] and invasive studies. The first brain-computer interface (BCI) applications to gait rehabilitation are then presented, with a discussion about the different strategies developed in the field. The challenges to raise for future systems are identified and discussed. Finally, we present some proposals to address these challenges, in order to contribute to the improvement of BCI for gait rehabilitation.
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Affiliation(s)
| | | | - Guy Cheron
- LNMB lab, Université Libre de Bruxelles, Avenue Franklin Roosevelt 50, Bruxelles 1050, Belgium.
| | - Thierry Dutoit
- TCTS lab, Université de Mons, Place du Parc 20, Mons 7000, Belgium.
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113
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Affiliation(s)
- Nitish V Thakor
- SINAPSE Institute, National University of Singapore, Singapore 117456, and Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21205, USA
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114
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Borton D, Bonizzato M, Beauparlant J, DiGiovanna J, Moraud EM, Wenger N, Musienko P, Minev IR, Lacour SP, Millán JDR, Micera S, Courtine G. Corticospinal neuroprostheses to restore locomotion after spinal cord injury. Neurosci Res 2013; 78:21-9. [PMID: 24135130 DOI: 10.1016/j.neures.2013.10.001] [Citation(s) in RCA: 41] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2013] [Revised: 09/17/2013] [Accepted: 09/26/2013] [Indexed: 01/20/2023]
Abstract
In this conceptual review, we highlight our strategy for, and progress in the development of corticospinal neuroprostheses for restoring locomotor functions and promoting neural repair after thoracic spinal cord injury in experimental animal models. We specifically focus on recent developments in recording and stimulating neural interfaces, decoding algorithms, extraction of real-time feedback information, and closed-loop control systems. Each of these complex neurotechnologies plays a significant role for the design of corticospinal neuroprostheses. Even more challenging is the coordinated integration of such multifaceted technologies into effective and practical neuroprosthetic systems to improve movement execution, and augment neural plasticity after injury. In this review we address our progress in rodent animal models to explore the viability of a technology-intensive strategy for recovery and repair of the damaged nervous system. The technical, practical, and regulatory hurdles that lie ahead along the path toward clinical applications are enormous - and their resolution is uncertain at this stage. However, it is imperative that the discoveries and technological developments being made across the field of neuroprosthetics do not stay in the lab, but instead reach clinical fruition at the fastest pace possible.
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Affiliation(s)
- David Borton
- Center for Neuroprosthetics and Brain Mind Institute, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Marco Bonizzato
- Translational Neural Engineering Laboratory, Center for Neuroprosthetics and Institute of Bioengineering, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Janine Beauparlant
- Center for Neuroprosthetics and Brain Mind Institute, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Jack DiGiovanna
- Translational Neural Engineering Laboratory, Center for Neuroprosthetics and Institute of Bioengineering, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Eduardo M Moraud
- Translational Neural Engineering Laboratory, Center for Neuroprosthetics and Institute of Bioengineering, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland; Automatic Control Laboratory, Swiss Federal Institute of Technology (ETH), Zurich, Switzerland
| | - Nikolaus Wenger
- Center for Neuroprosthetics and Brain Mind Institute, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Pavel Musienko
- Center for Neuroprosthetics and Brain Mind Institute, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Ivan R Minev
- Laboratory for Soft Bioelectronic Interfaces, Center for Neuroprosthetics, IMT/IBI, EPFL, Switzerland
| | - Stéphanie P Lacour
- Laboratory for Soft Bioelectronic Interfaces, Center for Neuroprosthetics, IMT/IBI, EPFL, Switzerland
| | - José del R Millán
- Laboratory for Non-Invasive Brain-Machine Interface, Center for Neuroprosthetics and Institute of Bioengineering, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Silvestro Micera
- Translational Neural Engineering Laboratory, Center for Neuroprosthetics and Institute of Bioengineering, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland; The BioRobotics Institute, Scuola Superiore Sant'Anna, Pisa, Italy
| | - Grégoire Courtine
- Center for Neuroprosthetics and Brain Mind Institute, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland.
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115
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Castermans T, Duvinage M. Corticomuscular coherence revealed during treadmill walking: further evidence of supraspinal control in human locomotion. J Physiol 2013; 591:1407-8. [PMID: 23504234 DOI: 10.1113/jphysiol.2012.247593] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022] Open
Affiliation(s)
- T Castermans
- TCTS Laboratory, Faculty of Engineering, Universit´e deMons, Place du Parc 20, 7000, Mons, Belgium.
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116
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Severens M, Nienhuis B, Desain P, Duysens J. Feasibility of measuring event related desynchronization with electroencephalography during walking. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2013; 2012:2764-7. [PMID: 23366498 DOI: 10.1109/embc.2012.6346537] [Citation(s) in RCA: 55] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
Brain Computer Interfaces could be useful in rehabilitation of movement, perhaps also for gait. Until recently, research on movement related brain signals has not included measuring electroencephalography (EEG) during walking, because of the potential artifacts. We investigated if it is possible to measure the event Related Desynchronization (ERD) and event related spectral perturbations (ERSP) during walking. Six subjects walked on a treadmill with a slow speed, while EEG, electromyography (EMG) of the neck muscles and step cycle were measured. A Canonical Correlation Analysis (CCA) was used to remove EMG artifacts from the EEG signals. It was shown that this method correctly deleted EMG components. A strong ERD in the mu band and a somewhat less strong ERD in the beta band were found during walking compared to a baseline period. Furthermore, lateralized ERSPs were found, depending on the phase in the step cycle. It is concluded that this is a promising method to use in BCI research on walking. These results therefore pave the way for using brain signals related to walking in a BCI context.
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Affiliation(s)
- M Severens
- Research Development & Education department, Sint Maartenskliniek, Nijmegen, The Netherlands.
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117
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Bulea TC, Kilicarslan A, Ozdemir R, Paloski WH, Contreras-Vidal JL. Simultaneous scalp electroencephalography (EEG), electromyography (EMG), and whole-body segmental inertial recording for multi-modal neural decoding. J Vis Exp 2013. [PMID: 23912203 DOI: 10.3791/50602] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/31/2022] Open
Abstract
Recent studies support the involvement of supraspinal networks in control of bipedal human walking. Part of this evidence encompasses studies, including our previous work, demonstrating that gait kinematics and limb coordination during treadmill walking can be inferred from the scalp electroencephalogram (EEG) with reasonably high decoding accuracies. These results provide impetus for development of non-invasive brain-machine-interface (BMI) systems for use in restoration and/or augmentation of gait- a primary goal of rehabilitation research. To date, studies examining EEG decoding of activity during gait have been limited to treadmill walking in a controlled environment. However, to be practically viable a BMI system must be applicable for use in everyday locomotor tasks such as over ground walking and turning. Here, we present a novel protocol for non-invasive collection of brain activity (EEG), muscle activity (electromyography (EMG)), and whole-body kinematic data (head, torso, and limb trajectories) during both treadmill and over ground walking tasks. By collecting these data in the uncontrolled environment insight can be gained regarding the feasibility of decoding unconstrained gait and surface EMG from scalp EEG.
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Affiliation(s)
- Thomas C Bulea
- Functional and Applied Biomechanics Group, National Institutes of Health, Bethesda, MD, USA.
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118
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Duvinage M, Castermans T, Petieau M, Seetharaman K, Hoellinger T, Cheron G, Dutoit T. A subjective assessment of a P300 BCI system for lower-limb rehabilitation purposes. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2013; 2012:3845-9. [PMID: 23366767 DOI: 10.1109/embc.2012.6346806] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Recent research has shown that a P300 system can be used while walking without requiring any specific gait-related artifact removal techniques. Also, standard EEG-based Brain-Computer Interfaces (BCI) have not been really assessed for lower limb rehabilitation/prosthesis. Therefore, this paper gives a first baseline estimation (for future BCI comparisons) of the subjective and objective performances of a four-state P300 BCI plus a non-control state for lower-limb rehabilitation purposes. To assess usability and workload, the System Usability Scale and the NASA Task Load Index questionnaires were administered to five healthy subjects after performing a real-time treadmill speed control. Results show that the P300 BCI approach could suit fitness and rehabilitation applications, whereas prosthesis control, which suffers from a low reactivity, appears too sensitive for risky and crowded areas.
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Affiliation(s)
- Matthieu Duvinage
- Faculty of Electrical Engineering, TCTS Lab, University of Mons, 7000 Mons, Belgium.
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119
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Velu PD, de Sa VR. Single-trial classification of gait and point movement preparation from human EEG. Front Neurosci 2013; 7:84. [PMID: 23781166 PMCID: PMC3678086 DOI: 10.3389/fnins.2013.00084] [Citation(s) in RCA: 40] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2012] [Accepted: 05/07/2013] [Indexed: 11/13/2022] Open
Abstract
Neuroimaging studies provide evidence of cortical involvement immediately before and during gait and during gait-related behaviors such as stepping in place or motor imagery of gait. Here we attempt to perform single-trial classification of gait intent from another movement plan (point intent) or from standing in place. Subjects walked naturally from a starting position to a designated ending position, pointed at a designated position from the starting position, or remained standing at the starting position. The 700 ms of recorded electroencephalography (EEG) before movement onset was used for single-trial classification of trials based on action type and direction (left walk, forward walk, right walk, left point, right point, and stand) as well as action type regardless of direction (stand, walk, point). Classification using regularized LDA was performed on a principal components analysis (PCA) reduced feature space composed of coefficients from levels 1 to 9 of a discrete wavelet decomposition using the Daubechies 4 wavelet. We achieved significant classification for all conditions, with errors as low as 17% when averaged across nine subjects. LDA and PCA highly weighted frequency ranges that included movement related potentials (MRPs), with smaller contributions from frequency ranges that included mu and beta idle motor rhythms. Additionally, error patterns suggested a spatial structure to the EEG signal. Future applications of the cortical gait intent signal may include an additional dimension of control for prosthetics, preemptive corrective feedback for gait disturbances, or human computer interfaces (HCI).
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Affiliation(s)
- Priya D Velu
- Department of Neurosciences, University of California San Diego, La Jolla, CA, USA
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120
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Garipelli G, Chavarriaga R, Millán JDR. Single trial analysis of slow cortical potentials: a study on anticipation related potentials. J Neural Eng 2013; 10:036014. [PMID: 23611808 DOI: 10.1088/1741-2560/10/3/036014] [Citation(s) in RCA: 47] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
OBJECTIVE Abundant literature suggests the use of slow cortical potentials (SCPs) in a wide spectrum of basic and applied neuroscience areas. Due to their low signal to noise ratio, these potentials are often studied using grand-average analysis, which conceals trial-to-trial information. Moreover, most of the single trial analysis methods in the literature are based on classical electroencephalogram (EEG) features ([1-30] Hz) and are likely to be unsuitable for SCPs that have different signal properties (such as having the signal's spectral content in the range [0.2-0.7] Hz). In this paper we provide insights into the selection of appropriate parameters for spectral and spatial filtering. APPROACH We study anticipation related SCPs recorded using a web-browser application protocol and a full-band EEG (FbEEG) setup from 11 subjects on two different days. MAIN RESULTS We first highlight the role of a bandpass with [0.1-1.0] Hz in comparison with common practices (e.g., either with full dc, just a lowpass, or with a minimal highpass cut-off around 0.05 Hz). Secondly, we suggest that a combination of spatial-smoothing filter and common average reference (CAR) is more suitable than the spatial filters often reported in the literature (e.g., re-referencing to an electrode, Laplacian or CAR alone). Thirdly, with the help of these preprocessing steps, we demonstrate the generalization capabilities of linear classifiers across several days (AUC of 0.88 ± 0.05 on average with a minimum of 0.81 ± 0.03 and a maximum of 0.97 ± 0.01). We also report the possibility of further improvements using a Bayesian fusion technique applied to electrode-specific classifiers. SIGNIFICANCE We believe the suggested spatial and spectral preprocessing methods are advantageous for grand-average and single trial analysis of SCPs obtained from EEG, MEG as well as for electrocorticogram. The use of these methods will impact basic neurophysiological studies as well as the use of SCPs in the design of neuroprosthetics.
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Affiliation(s)
- Gangadhar Garipelli
- Chair on Non-Invasive Brain-Machine Interface, Center for Neuroprosthetics, School of Engineering, École Polytechnique Fédérale de Lausanne, Station 11, 1015 Lausanne, Switzerland.
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121
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Antelis JM, Montesano L, Ramos-Murguialday A, Birbaumer N, Minguez J. On the usage of linear regression models to reconstruct limb kinematics from low frequency EEG signals. PLoS One 2013; 8:e61976. [PMID: 23613992 PMCID: PMC3629197 DOI: 10.1371/journal.pone.0061976] [Citation(s) in RCA: 73] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2012] [Accepted: 03/18/2013] [Indexed: 12/03/2022] Open
Abstract
Several works have reported on the reconstruction of 2D/3D limb kinematics from low-frequency EEG signals using linear regression models based on positive correlation values between the recorded and the reconstructed trajectories. This paper describes the mathematical properties of the linear model and the correlation evaluation metric that may lead to a misinterpretation of the results of this type of decoders. Firstly, the use of a linear regression model to adjust the two temporal signals (EEG and velocity profiles) implies that the relevant component of the signal used for decoding (EEG) has to be in the same frequency range as the signal to be decoded (velocity profiles). Secondly, the use of a correlation to evaluate the fitting of two trajectories could lead to overly-optimistic results as this metric is invariant to scale. Also, the correlation has a non-linear nature that leads to higher values for sinus/cosinus-like signals at low frequencies. Analysis of these properties on the reconstruction results was carried out through an experiment performed in line with previous studies, where healthy participants executed predefined reaching movements of the hand in 3D space. While the correlations of limb velocity profiles reconstructed from low-frequency EEG were comparable to studies in this domain, a systematic statistical analysis revealed that these results were not above the chance level. The empirical chance level was estimated using random assignments of recorded velocity profiles and EEG signals, as well as combinations of randomly generated synthetic EEG with recorded velocity profiles and recorded EEG with randomly generated synthetic velocity profiles. The analysis shows that the positive correlation results in this experiment cannot be used as an indicator of successful trajectory reconstruction based on a neural correlate. Several directions are herein discussed to address the misinterpretation of results as well as the implications on previous invasive and non-invasive works.
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Affiliation(s)
- Javier M Antelis
- Aragon Institute of Engineering Research, I3A, University of Zaragoza, Zaragoza, Aragon, Spain.
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122
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Zanotto D, Rosati G, Spagnol S, Stegall P, Agrawal SK. Effects of complementary auditory feedback in robot-assisted lower extremity motor adaptation. IEEE Trans Neural Syst Rehabil Eng 2013; 21:775-86. [PMID: 23529102 DOI: 10.1109/tnsre.2013.2242902] [Citation(s) in RCA: 29] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
This study investigates how complementary auditory feedback may affect short-term gait modifications induced by four training sessions with a robotic exoskeleton. Healthy subjects walked on a treadmill and were instructed to match a modified gait pattern derived from their natural one, while receiving assistance by the robot (kinetic guidance). The main question we wanted to answer is whether the most commonly used combination of feedback (i.e., haptic and visual) could be either enhanced by adding auditory feedback or successfully substituted with a combination of kinetic guidance and auditory feedback. Participants were randomly assigned to one of four groups, all of which received kinetic guidance. The control group received additional visual feedback, while the three experimental groups were each provided with a different modality of auditory feedback. The third experimental group also received the same visual feedback as the control group. Differences among the training modalities in gait kinematics, timing and symmetry were assessed in three post-training sessions.
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123
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Duysens J, Severens M, Nienhuis B. How can active cycling produce less brain activity than passive cycling? Clin Neurophysiol 2013; 124:217-8. [DOI: 10.1016/j.clinph.2012.09.004] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2012] [Accepted: 09/01/2012] [Indexed: 11/16/2022]
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124
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Paek AY, Brown JD, Gillespie RB, O'Malley MK, Shewokis PA, Contreras-Vidal JL. Reconstructing surface EMG from scalp EEG during myoelectric control of a closed looped prosthetic device. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2013; 2013:5602-5605. [PMID: 24111007 DOI: 10.1109/embc.2013.6610820] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/02/2023]
Abstract
In this study, seven able-bodied human subjects controlled a robotic gripper with surface electromyography (sEMG) activity from the biceps. While subjects controlled the gripper, they felt the forces measured by the robotic gripper through an exoskeleton fitted on their non-dominant left arm. Subjects were instructed to identify objects with the force feedback provided by the exoskeleton. While subjects operated the robotic gripper, scalp electroencephalography (EEG) and functional near infrared spectroscopy (fNIRS) were recorded. We developed neural decoders that used scalp EEG to reconstruct the sEMG used to control the robotic gripper. The neural decoders used a genetic algorithm embedded in a linear model with memory to reconstruct the sEMG from a plurality of EEG channels. The performance of the decoders, measured with Pearson correlation coefficients (median r-value = 0.59, maximum r-value = 0.91) was found to be comparable to previous studies that reconstructed sEMG linear envelopes from neural activity recorded with invasive techniques. These results show the feasibility of developing EEG-based neural interfaces that in turn could be used to control a robotic device.
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125
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Jorquera FSM, Grassi S, Farine PA, Contreras-Vidal JL. Classification of Stance and Swing Gait States during Treadmill Walking from Non-invasive Scalp Electroencephalographic (EEG) Signals. BIOSYSTEMS & BIOROBOTICS 2013. [DOI: 10.1007/978-3-642-34546-3_81] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/24/2023]
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126
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Wagner J, Solis-Escalante T, Grieshofer P, Neuper C, Müller-Putz G, Scherer R. Level of participation in robotic-assisted treadmill walking modulates midline sensorimotor EEG rhythms in able-bodied subjects. Neuroimage 2012; 63:1203-11. [PMID: 22906791 DOI: 10.1016/j.neuroimage.2012.08.019] [Citation(s) in RCA: 195] [Impact Index Per Article: 16.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2012] [Revised: 07/26/2012] [Accepted: 08/05/2012] [Indexed: 11/15/2022] Open
Abstract
In robot assisted gait training, a pattern of human locomotion is executed repetitively with the intention to restore the motor programs associated with walking. Several studies showed that active contribution to the movement is critical for the encoding of motor memory. We propose to use brain monitoring techniques during gait training to encourage active participation in the movement. We investigated the spectral patterns in the electroencephalogram (EEG) that are related to active and passive robot assisted gait. Fourteen healthy participants were considered. Infomax independent component analysis separated the EEG into independent components representing brain, muscle, and eye movement activity, as well as other artifacts. An equivalent current dipole was calculated for each independent component. Independent components were clustered across participants based on their anatomical position and frequency spectra. Four clusters were identified in the sensorimotor cortices that accounted for differences between active and passive walking or showed activity related to the gait cycle. We show that in central midline areas the mu (8-12 Hz) and beta (18-21 Hz) rhythms are suppressed during active compared to passive walking. These changes are statistically significant: mu (F(1, 13)=11.2 p ≤ 0.01) and beta (F(1, 13)=7.7, p ≤ 0.05). We also show that these differences depend on the gait cycle phases. We provide first evidence of modulations of the gamma rhythm in the band 25 to 40 Hz, localized in central midline areas related to the phases of the gait cycle. We observed a trend (F(1, 8)=11.03, p ≤ 0.06) for suppressed low gamma rhythm when comparing active and passive walking. Additionally we found significant suppressions of the mu (F(1, 11)=20.1 p ≤ 0.01), beta (F(1, 11)=11.3 p ≤ 0.05) and gamma (F(1, 11)=4.9 p ≤ 0.05) rhythms near C3 (in the right hand area of the primary motor cortex) during phases of active vs. passive robot assisted walking. To our knowledge this is the first study showing EEG analysis during robot assisted walking. We provide evidence for significant differences in cortical activation between active and passive robot assisted gait. Our findings may help to define appropriate features for single trial detection of active participation in gait training. This work is a further step toward the evaluation of brain monitoring techniques and brain-computer interface technologies for improving gait rehabilitation therapies in a top-down approach.
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Affiliation(s)
- Johanna Wagner
- Laboratory of Brain-Computer Interfaces, Institute for Knowledge Discovery, Graz University of Technology, Krenngasse 37, 8010 Graz, Austria
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127
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Presacco A, Forrester LW, Contreras-Vidal JL. Decoding intra-limb and inter-limb kinematics during treadmill walking from scalp electroencephalographic (EEG) signals. IEEE Trans Neural Syst Rehabil Eng 2012; 20:212-9. [PMID: 22438336 DOI: 10.1109/tnsre.2012.2188304] [Citation(s) in RCA: 80] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
Brain-machine interface (BMI) research has largely been focused on the upper limb. Although restoration of gait function has been a long-standing focus of rehabilitation research, surprisingly very little has been done to decode the cortical neural networks involved in the guidance and control of bipedal locomotion. A notable exception is the work by Nicolelis' group at Duke University that decoded gait kinematics from chronic recordings from ensembles of neurons in primary sensorimotor areas in rhesus monkeys. Recently, we showed that gait kinematics from the ankle, knee, and hip joints during human treadmill walking can be inferred from the electroencephalogram (EEG) with decoding accuracies comparable to those using intracortical recordings. Here we show that both intra- and inter-limb kinematics from human treadmill walking can be achieved with high accuracy from as few as 12 electrodes using scalp EEG. Interestingly, forward and backward predictors from EEG signals lagging or leading the kinematics, respectively, showed different spatial distributions suggesting distinct neural networks for feedforward and feedback control of gait. Of interest is that average decoding accuracy across subjects and decoding modes was ~0.68±0.08, supporting the feasibility of EEG-based BMI systems for restoration of walking in patients with paralysis.
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Affiliation(s)
- Alessandro Presacco
- Department of Kinesiology, University of Maryland, College Park, MD 20742, USA.
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128
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Contreras-Vidal J, Presacco A, Agashe H, Paek A. Restoration of whole body movement: toward a noninvasive brain-machine interface system. IEEE Pulse 2012; 3:34-7. [PMID: 22344949 DOI: 10.1109/mpul.2011.2175635] [Citation(s) in RCA: 21] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
This article highlights recent advances in the design of noninvasive neural interfaces based on the scalp electroencephalogram (EEG). The simplest of physical tasks, such as turning the page to read this article, requires an intense burst of brain activity. It happens in milliseconds and requires little conscious thought. But for amputees and stroke victims with diminished motor-sensory skills, this process can be difficult or impossible. Our team at the University of Maryland, in conjunction with the Johns Hopkins Applied Physics Laboratory (APL) and the University of Maryland School of Medicine, hopes to offer these people newfound mobility and dexterity. In separate research thrusts, were using data gleaned from scalp EEG to develop reliable brainmachine interface (BMI) systems that could soon control modern devices such as prosthetic limbs or powered robotic exoskeletons.
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Affiliation(s)
- José Contreras-Vidal
- Department of Electrical and Computer Engineering, University of Houston, Texas, USA.
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129
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Sugata H, Goto T, Hirata M, Yanagisawa T, Shayne M, Matsushita K, Yoshimine T, Yorifuji S. Neural decoding of unilateral upper limb movements using single trial MEG signals. Brain Res 2012; 1468:29-37. [PMID: 22683716 DOI: 10.1016/j.brainres.2012.05.053] [Citation(s) in RCA: 22] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2012] [Revised: 05/28/2012] [Accepted: 05/29/2012] [Indexed: 11/30/2022]
Abstract
A brain machine interface (BMI) provides the possibility of controlling such external devices as prosthetic arms for patients with severe motor dysfunction using their own brain signals. However, there have been few studies investigating the decoding accuracy for multiclasses of useful unilateral upper limb movements using non-invasive measurements. We investigated the decoding accuracy for classifying three types of unilateral upper limb movements using single-trial magnetoencephalography (MEG) signals. Neuromagnetic activities were recorded in 9 healthy subjects performing 3 types of right upper limb movements: hand grasping, pinching, and elbow flexion. A support vector machine was used to classify the single-trial MEG signals. The movement types were predicted with an average accuracy of 66 ± 10% (chance level: 33.3%) using neuromagnetic activity during a 400-ms interval (-200 ms to 200 ms from movement onsets). To explore the time-dependency of the decoding accuracy, we also examined the time course of decoding accuracy in 50-ms sliding windows from -500 ms to 500 ms. Decoding accuracies significantly increased and peaked once before (50.1 ± 4.9%) and twice after (58.5 ± 7.5% and 64.4 ± 7.6%) movement onsets in all subjects. Significant variability in the decoding features in the first peak was evident in the channels over the parietal area and in the second and third peaks in the channels over the sensorimotor area. Our results indicate that the three types of unilateral upper limb movement can be inferred with high accuracy by detecting differences in movement-related brain activity in the parietal and sensorimotor areas.
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Affiliation(s)
- Hisato Sugata
- Division of Functional Diagnostic Science, Osaka University Graduate School of Medicine, 1-7 Yamadaoka, Suita, Osaka 565-0871, Japan
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130
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Demandt E, Mehring C, Vogt K, Schulze-Bonhage A, Aertsen A, Ball T. Reaching movement onset- and end-related characteristics of EEG spectral power modulations. Front Neurosci 2012; 6:65. [PMID: 22586364 PMCID: PMC3345572 DOI: 10.3389/fnins.2012.00065] [Citation(s) in RCA: 31] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2012] [Accepted: 04/16/2012] [Indexed: 11/26/2022] Open
Abstract
The spectral power of intracranial field potentials shows movement-related modulations during reaching movements to different target positions that in frequencies up to the high-γ range (approximately 50 to above 200 Hz) can be reliably used for single-trial inference of movement parameters. However, identifying spectral power modulations suitable for single-trial analysis for non-invasive approaches remains a challenge. We recorded non-invasive electroencephalography (EEG) during a self-paced center-out and center-in arm movement task, resulting in eight reaching movement classes (four center-out, four center-in). We found distinct slow (≤5 Hz), μ (7.5–10 Hz), β (12.5–25 Hz), low-γ (approximately 27.5–50 Hz), and high-γ (above 50 Hz) movement onset- and end-related responses. Movement class-specific spectral power modulations were restricted to the β band at approximately 1 s after movement end and could be explained by the sensitivity of this response to different static, post-movement electromyography (EMG) levels. Based on the β band, significant single-trial inference of reaching movement endpoints was possible. The findings of the present study support the idea that single-trial decoding of different reaching movements from non-invasive EEG spectral power modulations is possible, but also suggest that the informative time window is after movement end and that the informative frequency range is restricted to the β band.
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Affiliation(s)
- Evariste Demandt
- Neurobiology and Animal Physiology, Faculty of Biology I, University of Freiburg Freiburg, Germany
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131
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Abstract
National security organizations in the United States, including the armed services and the intelligence community, have developed a close relationship with the scientific establishment. The latest technology often fuels warfighting and counter-intelligence capacities, providing the tactical advantages thought necessary to maintain geopolitical dominance and national security. Neuroscience has emerged as a prominent focus within this milieu, annually receiving hundreds of millions of Department of Defense dollars. Its role in national security operations raises ethical issues that need to be addressed to ensure the pragmatic synthesis of ethical accountability and national security.
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132
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Cerquera A, Arns M, Buitrago E, Gutiérrez R, Freund J. Nonlinear dynamics measures applied to EEG recordings of patients with attention deficit/hyperactivity disorder: quantifying the effects of a neurofeedback treatment. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2012; 2012:1057-1060. [PMID: 23366077 DOI: 10.1109/embc.2012.6346116] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/01/2023]
Abstract
This work presents the application of nonlinear dynamics measures to electroencephalograms (EEG) acquired from patients with Attention Deficit/Hyperactivity Disorder (ADHD) before and after a neurofeedback therapy, with the aim to assess the effects of the neurofeedback in a quantitative way. The database contains EEG registers of seven patients acquired in eyes-closed and eyes-opened conditions, in pre-and post-treatment phases. Five measures were applied: largest Lyapunov exponent, Lempel-Ziv complexity, Hurst exponent, and multiscale entropy on two different scales. The purpose is to test whether these measures are apt to detect and quantify differences from EEG registers between pre- and post-treatment. The results indicate that these measures could have a potential utility for detection of quantitative changes in specific EEG channels. In addition, the performance of some of these measures improved when the bandwidth was reduced to 3-30 Hz.
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Affiliation(s)
- Alexander Cerquera
- Faculty of Electronic and Biomedical Engineering, Antonio Nariño University, Bogota, Colombia.
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