151
|
Białkowska J, Mroczkowska D, Huflejt ME, Wojtkiewicz J, Siwek T, Barczewska M, Maksymowicz W. COMPLEX TREATMENT OF AMYOTROPHIC LATERAL SCLEROSIS PATIENT. Acta Clin Croat 2019; 58:757-766. [PMID: 32595261 PMCID: PMC7314291 DOI: 10.20471/acc.2019.58.04.24] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022] Open
Abstract
Amyotrophic lateral sclerosis is a progressive and fatal degenerative neuromuscular disease with few if any treatment options and physical rehabilitation addressing specific deficits is the most frequent form of therapy. Patients also suffer from depression and increased anxiety. Our purpose was to assess the neurorehabilitation effectiveness in a patient with amyotrophic lateral sclerosis who underwent stem cell transplantation but refused physiotherapy due to depression. Disease progression was followed using the revised Amyotrophic Lateral Sclerosis Functional Rating Scale bimonthly for six months pre- and then post-stem cell transplantation. Psychological traits were assessed using six standardized tests. Quantitative electroencephalogram diagnostics was performed before the first and after the last neurofeedback session, and sessions were conducted on a 3-times-a-week basis. The physiotherapy protocol included proprioceptive neuromuscular facilitation, electrical modalities unit applied to the lumbar spine area, and breathing, relaxation and walking exercises, among others. Increased motivation and marked decrease in the pain level was associated with the patient's willingness to complete physiotherapy, which resulted in improvements in most neuromuscular deficits and in increased respiratory capacity. During the 12 post-rehabilitation months, progression of the disease decelerated, and a positive behavioral change was noted. The study suggested that neurofeedback could be used as a neurorehabilitation component of the personalized complex rehabilitation protocol in patients with amyotrophic lateral sclerosis.
Collapse
Affiliation(s)
| | - Dorota Mroczkowska
- 1Department of Public Health, Faculty of Health Sciences, Collegium Medicum, University of Warmia and Mazury, Olsztyn, Poland; 2Clinical University Hospital, Olsztyn, Poland; 3Department of Pathophysiology, School of Medicine, Collegium Medicum, University of Warmia and Mazury, Olsztyn, Poland; 4Department of Neurology and Neurosurgery, School of Medicine, Collegium Medicum, University of Warmia and Mazury, Olsztyn, Poland
| | - Margaret E Huflejt
- 1Department of Public Health, Faculty of Health Sciences, Collegium Medicum, University of Warmia and Mazury, Olsztyn, Poland; 2Clinical University Hospital, Olsztyn, Poland; 3Department of Pathophysiology, School of Medicine, Collegium Medicum, University of Warmia and Mazury, Olsztyn, Poland; 4Department of Neurology and Neurosurgery, School of Medicine, Collegium Medicum, University of Warmia and Mazury, Olsztyn, Poland
| | - Joanna Wojtkiewicz
- 1Department of Public Health, Faculty of Health Sciences, Collegium Medicum, University of Warmia and Mazury, Olsztyn, Poland; 2Clinical University Hospital, Olsztyn, Poland; 3Department of Pathophysiology, School of Medicine, Collegium Medicum, University of Warmia and Mazury, Olsztyn, Poland; 4Department of Neurology and Neurosurgery, School of Medicine, Collegium Medicum, University of Warmia and Mazury, Olsztyn, Poland
| | - Tomasz Siwek
- 1Department of Public Health, Faculty of Health Sciences, Collegium Medicum, University of Warmia and Mazury, Olsztyn, Poland; 2Clinical University Hospital, Olsztyn, Poland; 3Department of Pathophysiology, School of Medicine, Collegium Medicum, University of Warmia and Mazury, Olsztyn, Poland; 4Department of Neurology and Neurosurgery, School of Medicine, Collegium Medicum, University of Warmia and Mazury, Olsztyn, Poland
| | - Monika Barczewska
- 1Department of Public Health, Faculty of Health Sciences, Collegium Medicum, University of Warmia and Mazury, Olsztyn, Poland; 2Clinical University Hospital, Olsztyn, Poland; 3Department of Pathophysiology, School of Medicine, Collegium Medicum, University of Warmia and Mazury, Olsztyn, Poland; 4Department of Neurology and Neurosurgery, School of Medicine, Collegium Medicum, University of Warmia and Mazury, Olsztyn, Poland
| | - Wojciech Maksymowicz
- 1Department of Public Health, Faculty of Health Sciences, Collegium Medicum, University of Warmia and Mazury, Olsztyn, Poland; 2Clinical University Hospital, Olsztyn, Poland; 3Department of Pathophysiology, School of Medicine, Collegium Medicum, University of Warmia and Mazury, Olsztyn, Poland; 4Department of Neurology and Neurosurgery, School of Medicine, Collegium Medicum, University of Warmia and Mazury, Olsztyn, Poland
| |
Collapse
|
152
|
Ray AM, Figueiredo TDC, López-Larraz E, Birbaumer N, Ramos-Murguialday A. Brain oscillatory activity as a biomarker of motor recovery in chronic stroke. Hum Brain Mapp 2019; 41:1296-1308. [PMID: 31778265 PMCID: PMC7268060 DOI: 10.1002/hbm.24876] [Citation(s) in RCA: 26] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2019] [Revised: 10/21/2019] [Accepted: 11/13/2019] [Indexed: 12/31/2022] Open
Abstract
In the present work, we investigated the relationship of oscillatory sensorimotor brain activity to motor recovery. The neurophysiological data of 30 chronic stroke patients with severe upper‐limb paralysis are the basis of the observational study presented here. These patients underwent an intervention including movement training based on combined brain–machine interfaces and physiotherapy of several weeks recorded in a double‐blinded randomized clinical trial. We analyzed the alpha oscillations over the motor cortex of 22 of these patients employing multilevel linear predictive modeling. We identified a significant correlation between the evolution of the alpha desynchronization during rehabilitative intervention and clinical improvement. Moreover, we observed that the initial alpha desynchronization conditions its modulation during intervention: Patients showing a strong alpha desynchronization at the beginning of the training improved if they increased their alpha desynchronization. Patients showing a small alpha desynchronization at initial training stages improved if they decreased it further on both hemispheres. In all patients, a progressive shift of desynchronization toward the ipsilesional hemisphere correlates significantly with clinical improvement regardless of lesion location. The results indicate that initial alpha desynchronization might be key for stratification of patients undergoing BMI interventions and that its interhemispheric balance plays an important role in motor recovery.
Collapse
Affiliation(s)
- Andreas M Ray
- Institute of Medical Psychology and Behavioral Neurobiology, University of Tübingen, Tübingen, Germany
| | - Thiago D C Figueiredo
- Institute of Medical Psychology and Behavioral Neurobiology, University of Tübingen, Tübingen, Germany
| | - Eduardo López-Larraz
- Institute of Medical Psychology and Behavioral Neurobiology, University of Tübingen, Tübingen, Germany
| | - Niels Birbaumer
- Institute of Medical Psychology and Behavioral Neurobiology, University of Tübingen, Tübingen, Germany
| | - Ander Ramos-Murguialday
- Institute of Medical Psychology and Behavioral Neurobiology, University of Tübingen, Tübingen, Germany.,TECNALIA, Health Department, Neural Engineering Laboratory, San Sebastián, Spain
| |
Collapse
|
153
|
Foster PP, Baldwin CL, Thompson JC, Espeseth T, Jiang X, Greenwood PM. Editorial: Cognitive and Brain Aging: Interventions to Promote Well-Being in Old Age. Front Aging Neurosci 2019; 11:268. [PMID: 31680930 PMCID: PMC6803512 DOI: 10.3389/fnagi.2019.00268] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2019] [Accepted: 09/17/2019] [Indexed: 02/02/2023] Open
Affiliation(s)
- Philip P Foster
- Pulmonary Section, Department of Medicine, Center for Space Medicine, Baylor College of Medicine, Houston, TX, United States.,Department of Chemistry, Rice University, Houston, TX, United States.,Department of Medicine, McGovern Medical School, University of Texas, Houston, TX, United States.,Department of Mathematics and Statistics, University of Houston-Clear Lake, Houston, TX, United States
| | | | | | | | - Xiong Jiang
- Georgetown University, Washington, DC, United States
| | | |
Collapse
|
154
|
Pisarchik AN, Maksimenko VA, Hramov AE. From Novel Technology to Novel Applications: Comment on "An Integrated Brain-Machine Interface Platform With Thousands of Channels" by Elon Musk and Neuralink. J Med Internet Res 2019; 21:e16356. [PMID: 31674923 PMCID: PMC6914250 DOI: 10.2196/16356] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2019] [Revised: 10/16/2019] [Accepted: 10/20/2019] [Indexed: 01/20/2023] Open
Affiliation(s)
- Alexander N Pisarchik
- Center for Biomedical Technology, Technical University of Madrid, Pozuelo de Alarcón, Madrid, Spain.,Neuroscience and Cognitive Technology Laboratory, Center for Technologies in Robotics and Mechatronics Components, Innopolis University, Innopolis, Russian Federation
| | - Vladimir A Maksimenko
- Neuroscience and Cognitive Technology Laboratory, Center for Technologies in Robotics and Mechatronics Components, Innopolis University, Innopolis, Russian Federation
| | - Alexander E Hramov
- Neuroscience and Cognitive Technology Laboratory, Center for Technologies in Robotics and Mechatronics Components, Innopolis University, Innopolis, Russian Federation
| |
Collapse
|
155
|
Koch J, Schuettler M, Pasluosta C, Stieglitz T. Electrical connectors for neural implants: design, state of the art and future challenges of an underestimated component. J Neural Eng 2019; 16:061002. [PMID: 31362277 DOI: 10.1088/1741-2552/ab36df] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
Abstract
Technological advances in electrically active implantable devices have increased the complexity of hardware design. In particular, the increasing number of stimulation and recording channels requires innovative approaches for connectors that interface electrodes with the implant circuitry. OBJECTIVE This work aims to provide a common theoretical ground for implantable connector development with a focus on neural applications. APPROACH Aspects and experiences from several disciplines are compiled from an engineering perspective to discuss the state of the art of connector solutions. Whenever available, we also present general design guidelines. MAIN RESULTS Degradation mechanisms, material stability and design rules in terms of biocompatibility and biostability are introduced. Considering contact physics, we address the design and characterization of the contact zone and review contaminants, wear and contact degradation. For high-channel counts and body-like environments, insulation can be even more crucial than the electrical connection itself. Therefore, we also introduce the requirements for electrical insulation to prevent signal loss and distortion and discuss its impact on the practical implementation. SIGNIFICANCE A final review is dedicated to the state of the art connector concepts, their mechanical setup, electrical performance and the interface to other implant components. We conclude with an outlook for possible approaches for the future generations of implants.
Collapse
Affiliation(s)
- Julia Koch
- Laboratory for Biomedical Microtechnology, Department of Microsystems Engineering, University of Freiburg, Freiburg, Germany
| | | | | | | |
Collapse
|
156
|
Bockbrader MA, Francisco G, Lee R, Olson J, Solinsky R, Boninger ML. Brain Computer Interfaces in Rehabilitation Medicine. PM R 2019; 10:S233-S243. [PMID: 30269808 DOI: 10.1016/j.pmrj.2018.05.028] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2018] [Revised: 05/22/2018] [Accepted: 05/31/2018] [Indexed: 12/24/2022]
Abstract
One innovation currently influencing physical medicine and rehabilitation is brain-computer interface (BCI) technology. BCI systems used for motor control record neural activity associated with thoughts, perceptions, and motor intent; decode brain signals into commands for output devices; and perform the user's intended action through an output device. BCI systems used for sensory augmentation transduce environmental stimuli into neural signals interpretable by the central nervous system. Both types of systems have potential for reducing disability by facilitating a user's interaction with the environment. Investigational BCI systems are being used in the rehabilitation setting both as neuroprostheses to replace lost function and as potential plasticity-enhancing therapy tools aimed at accelerating neurorecovery. Populations benefitting from motor and somatosensory BCI systems include those with spinal cord injury, motor neuron disease, limb amputation, and stroke. This article discusses the basic components of BCI for rehabilitation, including recording systems and locations, signal processing and translation algorithms, and external devices controlled through BCI commands. An overview of applications in motor and sensory restoration is provided, along with ethical questions and user perspectives regarding BCI technology.
Collapse
Affiliation(s)
- Marcia A Bockbrader
- Department of Physical Medicine & Rehabilitation, The Ohio State University, 480 Medical Center Dr, Columbus, OH 43210; and Neurological Institute, Ohio State University Wexner Medical Center, Columbus, OH(∗).
| | - Gerard Francisco
- Department of Physical Medicine & Rehabilitation, The University of Texas, Houston, TX(†)
| | - Ray Lee
- Department of Orthopaedic and Rehabilitation, Schwab Rehabilitation Hospital, University of Chicago, Chicago, IL(‡)
| | - Jared Olson
- Department of Physical Medicine and Rehabilitation, University of Colorado, Aurora, CO(§)
| | - Ryan Solinsky
- Spaulding Rehabilitation Hospital, Boston; and Department of Physical Medicine and Rehabilitation, Harvard Medical School, Boston, MA(¶)
| | - Michael L Boninger
- Department of Physical Medicine and Rehabilitation, University of Pittsburgh, Pittsburgh; and VA Pittsburgh Health Care System, Pittsburgh, PA(#)
| |
Collapse
|
157
|
Meziani A, Djouani K, Medkour T, Chibani A. A Lasso quantile periodogram based feature extraction for EEG-based motor imagery. J Neurosci Methods 2019; 328:108434. [PMID: 31569036 DOI: 10.1016/j.jneumeth.2019.108434] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2019] [Revised: 09/08/2019] [Accepted: 09/08/2019] [Indexed: 11/29/2022]
Abstract
BACKGROUND The extraction of relevant and distinct features from the electroencephalogram (EEG) signals is one of the most challenging task when implementing Brain Computer Interface (BCI) based systems. Frequency analysis techniques are recognised as one of the most suitable methods to have distinct information from EEG signals. However, existing studies use mostly classical approaches assuming that the signal is Gaussian, stationary and linear. These properties are not verified in the EEG case considering the complexity of the brain electrical activity. NEW METHOD This paper proposes two new spectral estimators that are robust against non-Gaussian, non-linear and non-stationary signals. These two approaches use quantile regression and L1-norm regularisation to estimate the spectrum of the motor imagery (MI) related EEG. RESULTS A dataset collected during a study of BCI motor imagery project conducted at Tshwane University of Technology (TUT), Pretoria, South Africa, is used to validate the proposed estimators. Experimental results demonstrate that the newly proposed approaches help improve the classification performance of MI. COMPARISON WITH EXISTING METHODS In order to show the effectiveness of the proposed estimators, a comparative study is conducted, considering classical commonly used techniques such as FFT and Welch periodogram through 5 classification algorithms. CONCLUSIONS The proposed Quantile-based spectral estimators are potential methods to improve the classification performance of the EEG-Based motor imagery systems.
Collapse
Affiliation(s)
- Aymen Meziani
- Laboratoire Images, Signaux et Systèmes Intelligents (LiSSi), Université de PARIS-EST, Paris, France; Department of Probability Statistics and Application, University of USTHB, Algiers, Algeria.
| | - Karim Djouani
- Laboratoire Images, Signaux et Systèmes Intelligents (LiSSi), Université de PARIS-EST, Paris, France; Tshwane University of Technology, FSATI, Pretoria, South Africa
| | - Tarek Medkour
- Department of Probability Statistics and Application, University of USTHB, Algiers, Algeria
| | - Abdelghani Chibani
- Laboratoire Images, Signaux et Systèmes Intelligents (LiSSi), Université de PARIS-EST, Paris, France
| |
Collapse
|
158
|
Khan S, Aziz T. Transcending the brain: is there a cost to hacking the nervous system? Brain Commun 2019; 1:fcz015. [PMID: 32954260 PMCID: PMC7425343 DOI: 10.1093/braincomms/fcz015] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2019] [Revised: 08/08/2019] [Accepted: 08/19/2019] [Indexed: 11/13/2022] Open
Abstract
Great advancements have recently been made to understand the brain and the potential that we can extract out of it. Much of this has been centred on modifying electrical activity of the nervous system for improved physical and cognitive performance in those with clinical impairment. However, there is a risk of going beyond purely physiological performance improvements and striving for human enhancement beyond traditional human limits. Simple ethical guidelines and legal doctrine must be examined to keep ahead of technological advancement in light of the impending mergence between biology and machine. By understanding the role of modern ethics, this review aims to appreciate the fine boundary between what is considered ethically justified for current neurotechnology.
Collapse
Affiliation(s)
- Shujhat Khan
- School of Medicine, Imperial College London, London SW7 2AZ, UK
| | - Tipu Aziz
- Department of Neurosurgery, John Radcliffe Hospital, University of Oxford, Oxford OX3 9DU, UK
| |
Collapse
|
159
|
Bartur G, Pratt H, Soroker N. Changes in mu and beta amplitude of the EEG during upper limb movement correlate with motor impairment and structural damage in subacute stroke. Clin Neurophysiol 2019; 130:1644-1651. [DOI: 10.1016/j.clinph.2019.06.008] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2018] [Revised: 04/24/2019] [Accepted: 06/18/2019] [Indexed: 01/15/2023]
|
160
|
Neural Activities Classification of Human Inhibitory Control Using Hierarchical Model. SENSORS 2019; 19:s19173791. [PMID: 31480570 PMCID: PMC6749522 DOI: 10.3390/s19173791] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/14/2019] [Revised: 08/18/2019] [Accepted: 08/29/2019] [Indexed: 11/30/2022]
Abstract
Human inhibitory control refers to the suppression of behavioral response in real environments, such as when driving a car or riding a motorcycle, playing a game and operating a machine. The P300 wave is a neural marker of human inhibitory control, and it can be used to recognize the symptoms of attention deficit hyperactivity disorder (ADHD) in human. In addition, the P300 neural marker can be considered as a stop command in the brain-computer interface (BCI) technologies. Therefore, the present study of electroencephalography (EEG) recognizes the mindset of human inhibition by observing the brain dynamics, like P300 wave in the frontal lobe, supplementary motor area, and in the right temporoparietal junction of the brain, all of them have been associated with response inhibition. Our work developed a hierarchical classification model to identify the neural activities of human inhibition. To accomplish this goal phase-locking value (PLV) method was used to select coupled brain regions related to inhibition because this method has demonstrated the best performance of the classification system. The PLVs were used with pattern recognition algorithms to classify a successful-stop versus a failed-stop in left-and right-hand inhibitions. The results demonstrate that quadratic discriminant analysis (QDA) yielded an average classification accuracy of 94.44%. These findings implicate the neural activities of human inhibition can be utilized as a stop command in BCI technologies, as well as to identify the symptoms of ADHD patients in clinical research.
Collapse
|
161
|
Branco MP, de Boer LM, Ramsey NF, Vansteensel MJ. Encoding of kinetic and kinematic movement parameters in the sensorimotor cortex: A Brain-Computer Interface perspective. Eur J Neurosci 2019; 50:2755-2772. [PMID: 30633413 PMCID: PMC6625947 DOI: 10.1111/ejn.14342] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2018] [Revised: 11/30/2018] [Accepted: 01/07/2019] [Indexed: 01/23/2023]
Abstract
For severely paralyzed people, Brain-Computer Interfaces (BCIs) can potentially replace lost motor output and provide a brain-based control signal for augmentative and alternative communication devices or neuroprosthetics. Many BCIs focus on neuronal signals acquired from the hand area of the sensorimotor cortex, employing changes in the patterns of neuronal firing or spectral power associated with one or more types of hand movement. Hand and finger movement can be described by two groups of movement features, namely kinematics (spatial and motion aspects) and kinetics (muscles and forces). Despite extensive primate and human research, it is not fully understood how these features are represented in the SMC and how they lead to the appropriate movement. Yet, the available information may provide insight into which features are most suitable for BCI control. To that purpose, the current paper provides an in-depth review on the movement features encoded in the SMC. Even though there is no consensus on how exactly the SMC generates movement, we conclude that some parameters are well represented in the SMC and can be accurately used for BCI control with discrete as well as continuous feedback. However, the vast evidence also suggests that movement should be interpreted as a combination of multiple parameters rather than isolated ones, pleading for further exploration of sensorimotor control models for accurate BCI control.
Collapse
Affiliation(s)
- Mariana P. Branco
- Brain Center Rudolf MagnusDepartment of Neurology and NeurosurgeryUniversity Medical Center UtrechtUtrechtThe Netherlands
| | | | - Nick F. Ramsey
- Brain Center Rudolf MagnusDepartment of Neurology and NeurosurgeryUniversity Medical Center UtrechtUtrechtThe Netherlands
| | - Mariska J. Vansteensel
- Brain Center Rudolf MagnusDepartment of Neurology and NeurosurgeryUniversity Medical Center UtrechtUtrechtThe Netherlands
| |
Collapse
|
162
|
Hayashi M, Tsuchimoto S, Mizuguchi N, Miyatake M, Kasuga S, Ushiba J. Two-stage regression of high-density scalp electroencephalograms visualizes force regulation signaling during muscle contraction. J Neural Eng 2019; 16:056020. [DOI: 10.1088/1741-2552/ab221a] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
|
163
|
Jochumsen M, Navid MS, Rashid U, Haavik H, Niazi IK. EMG- Versus EEG-Triggered Electrical Stimulation for Inducing Corticospinal Plasticity. IEEE Trans Neural Syst Rehabil Eng 2019; 27:1901-1908. [PMID: 31380763 DOI: 10.1109/tnsre.2019.2932104] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Brain-computer interfaces have been proposed for stroke rehabilitation. Motor cortical activity derived from the electroencephalography (EEG) can trigger external devices that provide congruent sensory feedback. However, many stroke patients regain residual muscle (EMG: electromyography) control due to spontaneous recovery and rehabilitation; therefore, EEG may not be necessary as a control signal. In this paper, a direct comparison was made between the induction of corticospinal plasticity using either EEG- or EMG-controlled electrical nerve stimulation. Twenty healthy participants participated in two intervention sessions consisting of EEG- and EMG-controlled electrical stimulation. The sessions consisted of 50 pairings between foot dorsiflexion movements (decoded through either EEG or EMG) and electrical stimulation of the common peroneal nerve. Before, immediately after and 30 minutes after the intervention, 15 motor evoked potentials (MEPs) were elicited in tibialis anterior through transcranial magnetic stimulation. Increased MEPs were observed immediately after (62 ± 26%, 73 ± 27% for EEG- and EMG-triggered electrical stimulation, respectively) and 30 minutes after each of the two interventions (79 ± 26% and 72 ± 27%) compared to the pre-intervention measurement. There was no difference between the interventions. Both EEG- and EMG-controlled electrical stimulation can induce corticospinal plasticity which suggests that stroke patients with residual EMG can use that modality instead of EEG to trigger stimulation.
Collapse
|
164
|
Tsuchimoto S, Shindo K, Hotta F, Hanakawa T, Liu M, Ushiba J. Sensorimotor Connectivity after Motor Exercise with Neurofeedback in Post-Stroke Patients with Hemiplegia. Neuroscience 2019; 416:109-125. [PMID: 31356896 DOI: 10.1016/j.neuroscience.2019.07.037] [Citation(s) in RCA: 25] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2018] [Revised: 07/21/2019] [Accepted: 07/23/2019] [Indexed: 11/27/2022]
Abstract
Impaired finger motor function in post-stroke hemiplegia is a debilitating condition with no evidence-based or accessible treatments. Here, we evaluated the neurophysiological effectiveness of direct brain control of robotic exoskeleton that provides movement support contingent with brain activity. To elucidate the mechanisms underlying the neurofeedback intervention, we assessed resting-state functional connectivity with functional magnetic resonance imaging (rsfcMRI) between the ipsilesional sensory and motor cortices before and after a single 1-h intervention. Eighteen stroke patients were randomly assigned to crossover interventions in a double-blind and sham-controlled design. One patient dropped out midway through the study, and 17 patients were included in this analysis. Interventions involved motor imagery, robotic assistance, and neuromuscular electrical stimulation administered to a paretic finger. The neurofeedback intervention delivered stimulations contingent on desynchronized ipsilesional electroencephalographic (EEG) oscillations during imagined movement, and the control intervention delivered sensorimotor stimulations that were independent of EEG oscillations. There was a significant time × intervention interaction in rsfcMRI in the ipsilesional sensorimotor cortex. Post-hoc analysis showed a larger gain in increased functional connectivity during the neurofeedback intervention. Although the neurofeedback intervention delivered fewer total sensorimotor stimulations compared to the sham-control, rsfcMRI in the ipsilesional sensorimotor cortices was increased during the neurofeedback intervention compared to the sham-control. Higher coactivation of the sensory and motor cortices during neurofeedback intervention enhanced rsfcMRI in the ipsilesional sensorimotor cortices. This study showed neurophysiological evidence that EEG-contingent neurofeedback is a promising strategy to induce intrinsic ipsilesional sensorimotor reorganization, supporting the importance of integrating closed-loop sensorimotor processing at a neurophysiological level.
Collapse
Affiliation(s)
- Shohei Tsuchimoto
- School of Fundamental Science and Technology, Graduate School of Keio University, Kanagawa, 223-8522, Japan; Japan Society for the Promotion of Science, Tokyo, 102-0083, Japan
| | - Keiichiro Shindo
- Department of Rehabilitation Medicine, Keio University School of Medicine, Tokyo, 160-8582, Japan; Shonan Keiiku Hospital, Kanagawa, 252-0816, Japan
| | - Fujiko Hotta
- Department of Rehabilitation Medicine, Keio University School of Medicine, Tokyo, 160-8582, Japan; Tokyo Metropolitan Rehabilitation Hospital, Tokyo, 131-0034, Japan
| | - Takashi Hanakawa
- Integrative Brain Imaging Center, National Center of Neurology and Psychiatry, Tokyo, 187-8551, Japan; Japan Science and Technology Agency, Precursory Research for Embryonic Science and Technology, 332-0012, Saitama, Japan
| | - Meigen Liu
- Department of Rehabilitation Medicine, Keio University School of Medicine, Tokyo, 160-8582, Japan
| | - Junichi Ushiba
- Department of Biosciences and Informatics, Faculty of Science and Technology, Keio University, Kanagawa, 223-8522, Japan; Keio Institute of Pure and Applied Sciences, Faculty of Science and Technology Graduate School of Science and Technology, Keio University, Kanagawa, 223-8522, Japan.
| |
Collapse
|
165
|
Mirzaee MS, Moghimi S. Detection of reaching intention using EEG signals and nonlinear dynamic system identification. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2019; 175:151-161. [PMID: 31104704 DOI: 10.1016/j.cmpb.2019.04.023] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/23/2018] [Revised: 04/19/2019] [Accepted: 04/21/2019] [Indexed: 06/09/2023]
Abstract
BACKGROUND AND OBJECTIVES Low frequency electroencephalography (EEG) signals are associated with preparation of movement and thus provide valuable information for brain-machine interface applications. The purpose of this study was to detect movement intention from EEG signals before execution of self-paced arm reaching movements. METHODS Ten healthy individuals were recruited. Movement onset was determined from surface electromyography recordings time-locked with EEG signals. Unlike previous studies, which employed feature extraction and classification for decoding, a nonlinear dynamic multiple-input/single output (MISO) model was developed. The MISO model consisted of a cascade of Volterra structures and a threshold block, generating the binary output corresponding to intention/no-intention. The modeling process included input selection from a pool of different EEG channels. The predictive performance of the model was evaluated using the receiver operating characteristics curve, from which the optimum threshold was also selected. The Mann-Whitney statistics was employed to select the significant EEG channels for the output by examining the statistical significance of improvement in the predictive capability of the model when the respective channels were included. RESULTS With the proposed approach, movement intention was detected approximately 500 ms before the movement onset and on average, with an accuracy of 96.37 ± 0.94%, a sensitivity of 77.93 ± 4.40% and a specificity of 98.52 ± 1.19%. CONCLUSIONS The model output can be converted to motion commands for neuroprosthetic devices and exoskeletons in future applications.
Collapse
Affiliation(s)
| | - Sahar Moghimi
- Electrical Engineering Department, Ferdowsi University of Mashhad, Mashhad, Iran; Rayan Center for Neuroscience and Behavior, Ferdowsi University of Mashhad, Mashhad, Iran.
| |
Collapse
|
166
|
Bublitz C, Wolkenstein A, Jox RJ, Friedrich O. Legal liabilities of BCI-users: Responsibility gaps at the intersection of mind and machine? INTERNATIONAL JOURNAL OF LAW AND PSYCHIATRY 2019; 65:101399. [PMID: 30449603 DOI: 10.1016/j.ijlp.2018.10.002] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/04/2018] [Revised: 09/30/2018] [Accepted: 10/11/2018] [Indexed: 06/09/2023]
Affiliation(s)
- Christoph Bublitz
- Faculty of Law, Universität Hamburg, Rothenbaumchaussee 33, 20148 Hamburg, Germany.
| | - Andreas Wolkenstein
- Institute of Ethics, History and Theory of Medicine, Ludwig-Maximilians-Universität München, Lessingstr. 2, 80336 Munich, Germany
| | - Ralf J Jox
- Centre Hospitalier Universitaire Vaudois (CHUV), Avenue Pierre-Decker 5, CH-1011 Lausanne, Switzerland
| | - Orsolya Friedrich
- Institute of Ethics, History and Theory of Medicine, Ludwig-Maximilians-Universität München, Lessingstr. 2, 80336 Munich, Germany
| |
Collapse
|
167
|
Edelman BJ, Meng J, Suma D, Zurn C, Nagarajan E, Baxter BS, Cline CC, He B. Noninvasive neuroimaging enhances continuous neural tracking for robotic device control. Sci Robot 2019; 4:eaaw6844. [PMID: 31656937 PMCID: PMC6814169 DOI: 10.1126/scirobotics.aaw6844] [Citation(s) in RCA: 142] [Impact Index Per Article: 28.4] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
Brain-computer interfaces (BCIs) utilizing signals acquired with intracortical implants have achieved successful high-dimensional robotic device control useful for completing daily tasks. However, the substantial amount of medical and surgical expertise required to correctly implant and operate these systems significantly limits their use beyond a few clinical cases. A noninvasive counterpart requiring less intervention that can provide high-quality control would profoundly impact the integration of BCIs into the clinical and home setting. Here, we present and validate a noninvasive framework utilizing electroencephalography (EEG) to achieve the neural control of a robotic device for continuous random target tracking. This framework addresses and improves upon both the "brain" and "computer" components by respectively increasing user engagement through a continuous pursuit task and associated training paradigm, and the spatial resolution of noninvasive neural data through EEG source imaging. In all, our unique framework enhanced BCI learning by nearly 60% for traditional center-out tasks and by over 500% in the more realistic continuous pursuit task. We further demonstrated an additional enhancement in BCI control of almost 10% by using online noninvasive neuroimaging. Finally, this framework was deployed in a physical task, demonstrating a near seamless transition from the control of an unconstrained virtual cursor to the real-time control of a robotic arm. Such combined advances in the quality of neural decoding and the practical utility of noninvasive robotic arm control will have major implications on the eventual development and implementation of neurorobotics by means of noninvasive BCI.
Collapse
Affiliation(s)
- B. J. Edelman
- Department of Biomedical Engineering, University of Minnesota, Minneapolis, MN 55455, USA
| | - J. Meng
- Department of Biomedical Engineering, Carnegie Mellon University, Pittsburgh, PA 15213, USA
| | - D. Suma
- Department of Biomedical Engineering, Carnegie Mellon University, Pittsburgh, PA 15213, USA
| | - C. Zurn
- Department of Biomedical Engineering, University of Minnesota, Minneapolis, MN 55455, USA
| | - E. Nagarajan
- Department of Neuroscience, University of Minnesota, Minneapolis, MN 55455, USA
| | - B. S. Baxter
- Department of Biomedical Engineering, University of Minnesota, Minneapolis, MN 55455, USA
| | - C. C. Cline
- Department of Biomedical Engineering, University of Minnesota, Minneapolis, MN 55455, USA
| | - B. He
- Department of Biomedical Engineering, University of Minnesota, Minneapolis, MN 55455, USA
- Department of Biomedical Engineering, Carnegie Mellon University, Pittsburgh, PA 15213, USA
| |
Collapse
|
168
|
Bortone I, Leonardis D, Mastronicola N, Crecchi A, Bonfiglio L, Procopio C, Solazzi M, Frisoli A. Wearable Haptics and Immersive Virtual Reality Rehabilitation Training in Children With Neuromotor Impairments. IEEE Trans Neural Syst Rehabil Eng 2019; 26:1469-1478. [PMID: 29985156 DOI: 10.1109/tnsre.2018.2846814] [Citation(s) in RCA: 28] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
The past decade has seen the emergence of rehabilitation treatments using virtual reality (VR) environments although translation into clinical practice has been limited so far. In this paper, an immersive VR rehabilitation training system endowed with wearable haptics is proposed for children with neuromotor impairments: it aims to enhance involvement and engagement of patients, to provide congruent multi-sensory afferent feedback during motor exercises and to benefit from the flexibility of VR in adapting exercises to the patient's need. An experimental rehabilitation session conducted with children with cerebral palsy (CP) and developmental dyspraxia (DD) has been performed to evaluate the usability of the system and proof of concept trial of the proposed approach. We compared CP/DD performance with both typically developing children and adult control group. Results show the system was compliant with different levels of motor skills and allowed patients to complete the experimental rehabilitation session, with performance varying according to the expected motor abilities of different groups. Moreover, a kinematic assessmentbased on the presented system has been designed. Obtained results reflected different motor abilities of patients and participants, suggesting suitability of the proposed kinematic assessment as a motor function outcome.
Collapse
|
169
|
Self-Paced Online vs. Cue-Based Offline Brain-Computer Interfaces for Inducing Neural Plasticity. Brain Sci 2019; 9:brainsci9060127. [PMID: 31159454 PMCID: PMC6627467 DOI: 10.3390/brainsci9060127] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2019] [Revised: 05/23/2019] [Accepted: 05/28/2019] [Indexed: 02/01/2023] Open
Abstract
: Brain-computer interfaces (BCIs), operated in a cue-based (offline) or self-paced (online) mode, can be used for inducing cortical plasticity for stroke rehabilitation by the pairing of movement-related brain activity with peripheral electrical stimulation. The aim of this study was to compare the difference in cortical plasticity induced by the two BCI modes. Fifteen healthy participants participated in two experimental sessions: cue-based BCI and self-paced BCI. In both sessions, imagined dorsiflexions were extracted from continuous electroencephalogram (EEG) and paired 50 times with the electrical stimulation of the common peroneal nerve. Before, immediately after, and 30 minutes after each intervention, the cortical excitability was measured through the motor-evoked potentials (MEPs) of tibialis anterior elicited through transcranial magnetic stimulation. Linear mixed regression models showed that the MEP amplitudes increased significantly (p < 0.05) from pre- to post- and 30-minutes post-intervention in terms of both the absolute and relative units, regardless of the intervention type. Compared to pre-interventions, the absolute MEP size increased by 79% in post- and 68% in 30-minutes post-intervention in the self-paced mode (with a true positive rate of ~75%), and by 37% in post- and 55% in 30-minutes post-intervention in the cue-based mode. The two modes were significantly different (p = 0.03) at post-intervention (relative units) but were similar at both post timepoints (absolute units). These findings suggest that immediate changes in cortical excitability may have implications for stroke rehabilitation, where it could be used as a priming protocol in conjunction with another intervention; however, the findings need to be validated in studies involving stroke patients.
Collapse
|
170
|
Fiedler P, Muhle R, Griebel S, Pedrosa P, Fonseca C, Vaz F, Zanow F, Haueisen J. Contact Pressure and Flexibility of Multipin Dry EEG Electrodes. IEEE Trans Neural Syst Rehabil Eng 2019; 26:750-757. [PMID: 29641379 DOI: 10.1109/tnsre.2018.2811752] [Citation(s) in RCA: 30] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
In state-of-the-art electroencephalography (EEG) Silver/Silver-Chloride electrodes are applied together with electrolyte gels or pastes. Their application requires extensive preparation, trained medical staff and limits measurement time and mobility. We recently proposed a novel multichannel cap system for dry EEG electrodes for mobile and out-of-the-lab EEG acquisition. During the tests with these novel polymer-based multipin dry electrodes, we observed that the quality of the recording depends on the applied normal force and resulting contact pressure. Consequently, in this paper we systematically investigate the influence of electrode-skin contact pressure and electrode substrate flexibility on interfacial impedance and perceived wearing comfort in a study on 12 volunteers. The normal force applied to the electrode was varied between the minimum required force to achieve impedances and a maximum of 4 N, using a new force measurement applicator. We found that for a polymer shore hardness A98, with increasing normal force, the impedance decreases from and to and at frontal hairless and temporal hairy positions, respectively. Similar results were obtained for shore A90, A80, and A70. The best compromise of low and stable impedances as well as a good wearing comfort was determined for applied normal forces between 2 and 3 N using electrodes with shore A98 or A90. Our results provide the basis for improved EEG cap designs with optimal wearing comfort and recording quality for dry multipin electrodes, which will enable new fields of application for EEG.
Collapse
|
171
|
Slutzky MW. Brain-Machine Interfaces: Powerful Tools for Clinical Treatment and Neuroscientific Investigations. Neuroscientist 2019; 25:139-154. [PMID: 29772957 PMCID: PMC6611552 DOI: 10.1177/1073858418775355] [Citation(s) in RCA: 33] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/15/2023]
Abstract
Brain-machine interfaces (BMIs) have exploded in popularity in the past decade. BMIs, also called brain-computer interfaces, provide a direct link between the brain and a computer, usually to control an external device. BMIs have a wide array of potential clinical applications, ranging from restoring communication to people unable to speak due to amyotrophic lateral sclerosis or a stroke, to restoring movement to people with paralysis from spinal cord injury or motor neuron disease, to restoring memory to people with cognitive impairment. Because BMIs are controlled directly by the activity of prespecified neurons or cortical areas, they also provide a powerful paradigm with which to investigate fundamental questions about brain physiology, including neuronal behavior, learning, and the role of oscillations. This article reviews the clinical and neuroscientific applications of BMIs, with a primary focus on motor BMIs.
Collapse
Affiliation(s)
- Marc W Slutzky
- 1 Departments of Neurology, Physiology, and Physical Medicine & Rehabilitation, Northwestern University, Chicago, IL, USA
| |
Collapse
|
172
|
Ehrlich SK, Agres KR, Guan C, Cheng G. A closed-loop, music-based brain-computer interface for emotion mediation. PLoS One 2019; 14:e0213516. [PMID: 30883569 PMCID: PMC6422328 DOI: 10.1371/journal.pone.0213516] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2018] [Accepted: 02/23/2019] [Indexed: 11/29/2022] Open
Abstract
Emotions play a critical role in rational and intelligent behavior; a better fundamental knowledge of them is indispensable for understanding higher order brain function. We propose a non-invasive brain-computer interface (BCI) system to feedback a person’s affective state such that a closed-loop interaction between the participant’s brain responses and the musical stimuli is established. We realized this concept technically in a functional prototype of an algorithm that generates continuous and controllable patterns of synthesized affective music in real-time, which is embedded within a BCI architecture. We evaluated our concept in two separate studies. In the first study, we tested the efficacy of our music algorithm by measuring subjective affective responses from 11 participants. In a second pilot study, the algorithm was embedded in a real-time BCI architecture to investigate affective closed-loop interactions in 5 participants. Preliminary results suggested that participants were able to intentionally modulate the musical feedback by self-inducing emotions (e.g., by recalling memories), suggesting that the system was able not only to capture the listener’s current affective state in real-time, but also potentially provide a tool for listeners to mediate their own emotions by interacting with music. The proposed concept offers a tool to study emotions in the loop, promising to cast a complementary light on emotion-related brain research, particularly in terms of clarifying the interactive, spatio-temporal dynamics underlying affective processing in the brain.
Collapse
Affiliation(s)
- Stefan K. Ehrlich
- Chair for Cognitive Systems, Department of Electrical and Computer Engineering, Technische Universität München (TUM), Munich, Germany
- * E-mail:
| | - Kat R. Agres
- Institute of High Performance Computing, Social and Cognitive Computing Department, Agency for Science, Technology and Research (A*STAR), Singapore, Singapore
- Yong Siew Toh Conservatory of Music, National University of Singapore (NUS), Singapore, Singapore
| | - Cuntai Guan
- School of Computer Science and Engineering, Nanyang Technological University (NTU), Singapore, Singapore
| | - Gordon Cheng
- Chair for Cognitive Systems, Department of Electrical and Computer Engineering, Technische Universität München (TUM), Munich, Germany
| |
Collapse
|
173
|
Chen ML, Fu D, Boger J, Jiang N. Age-Related Changes in Vibro-Tactile EEG Response and Its Implications in BCI Applications: A Comparison Between Older and Younger Populations. IEEE Trans Neural Syst Rehabil Eng 2019; 27:603-610. [PMID: 30872232 DOI: 10.1109/tnsre.2019.2890968] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
The rapid increase in the number of older adults around the world is accelerating research in applications to support age-related conditions, such as brain-computer interface (BCI) applications for post-stroke neurorehabilitation. The signal processing algorithms for electroencephalogram (EEG) and other physiological signals that are currently used in BCI have been developed on data from much younger populations. It is unclear how age-related changes may affect the EEG signal and therefore the use of BCI by older adults. This research investigated the EEG response to vibro-tactile stimulation from 11 younger (21.7±2.76 years old) and 11 older (72.0±8.07 years old) subjects. The results showed that: 1) the spatial patterns of cortical activation in older subjects were significantly different from those of younger subjects, with markedly reduced lateralization; 2) there is a general power reduction of the EEG measured from older subjects. The average left vs. right BCI performance accuracy of older subjects was 66.4±5.70%, 15.9% lower than that of the younger subjects (82.3±12.4%) and statistically significantly different (t(10)= -3.57, p= 0.005). Future research should further investigate age-differences that may exist in electrophysiology and take these into consideration when developing applications that target the older population.
Collapse
|
174
|
Kögel J, Schmid JR, Jox RJ, Friedrich O. Using brain-computer interfaces: a scoping review of studies employing social research methods. BMC Med Ethics 2019; 20:18. [PMID: 30845952 PMCID: PMC6407281 DOI: 10.1186/s12910-019-0354-1] [Citation(s) in RCA: 29] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2018] [Accepted: 02/22/2019] [Indexed: 12/11/2022] Open
Abstract
Background The rapid expansion of research on Brain-Computer Interfaces (BCIs) is not only due to the promising solutions offered for persons with physical impairments. There is also a heightened need for understanding BCIs due to the challenges regarding ethics presented by new technology, especially in its impact on the relationship between man and machine. Here we endeavor to present a scoping review of current studies in the field to gain insight into the complexity of BCI use. By examining studies related to BCIs that employ social research methods, we seek to demonstrate the multitude of approaches and concerns from various angles in considering the social and human impact of BCI technology. Methods For this scoping review of research on BCIs’ social and ethical implications, we systematically analyzed six databases, encompassing the fields of medicine, psychology, and the social sciences, in order to identify empirical studies on BCIs. The search yielded 73 publications that employ quantitative, qualitative, or mixed methods. Results Of the 73 publications, 71 studies address the user perspective. Some studies extend to consideration of other BCI stakeholders such as medical technology experts, caregivers, or health care professionals. The majority of the studies employ quantitative methods. Recurring themes across the studies examined were general user opinion towards BCI, central technical or social issues reported, requests/demands made by users of the technology, the potential/future of BCIs, and ethical aspects of BCIs. Conclusions Our findings indicate that while technical aspects of BCIs such as usability or feasibility are being studied extensively, comparatively little in-depth research has been done on the self-image and self-experience of the BCI user. In general there is also a lack of focus or examination of the caregiver’s perspective. Electronic supplementary material The online version of this article (10.1186/s12910-019-0354-1) contains supplementary material, which is available to authorized users.
Collapse
Affiliation(s)
- Johannes Kögel
- Institute of Ethics, History and Theory of Medicine, LMU Munich, Lessingstr. 2, D-80336, Munich, Germany.
| | - Jennifer R Schmid
- Institute of Ethics, History and Theory of Medicine, LMU Munich, Lessingstr. 2, D-80336, Munich, Germany
| | - Ralf J Jox
- Institute of Ethics, History and Theory of Medicine, LMU Munich, Lessingstr. 2, D-80336, Munich, Germany
| | - Orsolya Friedrich
- Institute of Ethics, History and Theory of Medicine, LMU Munich, Lessingstr. 2, D-80336, Munich, Germany
| |
Collapse
|
175
|
Remsik AB, Williams L, Gjini K, Dodd K, Thoma J, Jacobson T, Walczak M, McMillan M, Rajan S, Young BM, Nigogosyan Z, Advani H, Mohanty R, Tellapragada N, Allen J, Mazrooyisebdani M, Walton LM, van Kan PLE, Kang TJ, Sattin JA, Nair VA, Edwards DF, Williams JC, Prabhakaran V. Ipsilesional Mu Rhythm Desynchronization and Changes in Motor Behavior Following Post Stroke BCI Intervention for Motor Rehabilitation. Front Neurosci 2019; 13:53. [PMID: 30899211 PMCID: PMC6417367 DOI: 10.3389/fnins.2019.00053] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2018] [Accepted: 01/21/2019] [Indexed: 01/26/2023] Open
Abstract
Loss of motor function is a common deficit following stroke insult and often manifests as persistent upper extremity (UE) disability which can affect a survivor's ability to participate in activities of daily living. Recent research suggests the use of brain-computer interface (BCI) devices might improve UE function in stroke survivors at various times since stroke. This randomized crossover-controlled trial examines whether intervention with this BCI device design attenuates the effects of hemiparesis, encourages reorganization of motor related brain signals (EEG measured sensorimotor rhythm desynchronization), and improves movement, as measured by the Action Research Arm Test (ARAT). A sample of 21 stroke survivors, presenting with varied times since stroke and levels of UE impairment, received a maximum of 18-30 h of intervention with a novel electroencephalogram-based BCI-driven functional electrical stimulator (EEG-BCI-FES) device. Driven by spectral power recordings from contralateral EEG electrodes during cued attempted grasping of the hand, the user's input to the EEG-BCI-FES device modulates horizontal movement of a virtual cursor and also facilitates concurrent stimulation of the impaired UE. Outcome measures of function and capacity were assessed at baseline, mid-therapy, and at completion of therapy while EEG was recorded only during intervention sessions. A significant increase in r-squared values [reflecting Mu rhythm (8-12 Hz) desynchronization as the result of attempted movements of the impaired hand] presented post-therapy compared to baseline. These findings suggest that intervention corresponds with greater desynchronization of Mu rhythm in the ipsilesional hemisphere during attempted movements of the impaired hand and this change is related to changes in behavior as a result of the intervention. BCI intervention may be an effective way of addressing the recovery of a stroke impaired UE and studying neuromechanical coupling with motor outputs. Clinical Trial Registration: ClinicalTrials.gov, identifier NCT02098265.
Collapse
Affiliation(s)
- Alexander B. Remsik
- Department of Radiology, University of Wisconsin–Madison, Madison, WI, United States
- Department of Kinesiology, University of Wisconsin–Madison, Madison, WI, United States
- Institute for Clinical and Translational Research, University of Wisconsin–Madison, Madison, WI, United States
| | - Leroy Williams
- Department of Radiology, University of Wisconsin–Madison, Madison, WI, United States
- Department of Educational Psychology, University of Wisconsin–Madison, Madison, WI, United States
- Center for Women’s Health Research, University of Wisconsin–Madison, Madison, WI, United States
| | - Klevest Gjini
- Department of Radiology, University of Wisconsin–Madison, Madison, WI, United States
- Department of Neurology, University of Wisconsin–Madison, Madison, WI, United States
| | - Keith Dodd
- Department of Radiology, University of Wisconsin–Madison, Madison, WI, United States
- Department of Biomedical Engineering, University of Wisconsin–Madison, Madison, WI, United States
| | - Jaclyn Thoma
- Department of Radiology, University of Wisconsin–Madison, Madison, WI, United States
- Neuroscience Training Program, University of Wisconsin School of Medicine and Public Health, Madison, WI, United States
| | - Tyler Jacobson
- Department of Radiology, University of Wisconsin–Madison, Madison, WI, United States
- Neuroscience Training Program, University of Wisconsin School of Medicine and Public Health, Madison, WI, United States
| | - Matt Walczak
- Department of Radiology, University of Wisconsin–Madison, Madison, WI, United States
| | - Matthew McMillan
- Department of Radiology, University of Wisconsin–Madison, Madison, WI, United States
- Department of Biomedical Engineering, University of Wisconsin–Madison, Madison, WI, United States
| | - Shruti Rajan
- Department of Radiology, University of Wisconsin–Madison, Madison, WI, United States
- Department of Psychology, University of Wisconsin–Madison, Madison, WI, United States
| | - Brittany M. Young
- Department of Radiology, University of Wisconsin–Madison, Madison, WI, United States
- Institute for Clinical and Translational Research, University of Wisconsin–Madison, Madison, WI, United States
- Neuroscience Training Program, University of Wisconsin School of Medicine and Public Health, Madison, WI, United States
- Clinical Neuroengineering Training Program, University of Wisconsin–Madison, Madison, WI, United States
- Medical Scientist Training Program, University of Wisconsin School of Medicine and Public Health, Madison, WI, United States
| | - Zack Nigogosyan
- Department of Radiology, University of Wisconsin–Madison, Madison, WI, United States
| | - Hemali Advani
- Department of Radiology, University of Wisconsin–Madison, Madison, WI, United States
| | - Rosaleena Mohanty
- Department of Radiology, University of Wisconsin–Madison, Madison, WI, United States
- Department of Electrical and Computer Engineering, University of Wisconsin–Madison, Madison, WI, United States
| | - Neelima Tellapragada
- Department of Radiology, University of Wisconsin–Madison, Madison, WI, United States
| | - Janerra Allen
- Department of Materials Science and Engineering, University of Wisconsin–Madison, Madison, WI, United States
| | | | - Leo M. Walton
- Department of Biomedical Engineering, University of Wisconsin–Madison, Madison, WI, United States
- Neuroscience Training Program, University of Wisconsin School of Medicine and Public Health, Madison, WI, United States
| | - Peter L. E. van Kan
- Department of Kinesiology, University of Wisconsin–Madison, Madison, WI, United States
| | - Theresa J. Kang
- Department of Radiology, University of Wisconsin–Madison, Madison, WI, United States
- Department of Neurology, University of Wisconsin–Madison, Madison, WI, United States
| | - Justin A. Sattin
- Neuroscience Training Program, University of Wisconsin School of Medicine and Public Health, Madison, WI, United States
| | - Veena A. Nair
- Department of Radiology, University of Wisconsin–Madison, Madison, WI, United States
| | | | - Justin C. Williams
- Department of Biomedical Engineering, University of Wisconsin–Madison, Madison, WI, United States
- Department of Neurological Surgery, University of Wisconsin–Madison, Madison, WI, United States
| | - Vivek Prabhakaran
- Department of Radiology, University of Wisconsin–Madison, Madison, WI, United States
- Department of Neurology, University of Wisconsin–Madison, Madison, WI, United States
- Neuroscience Training Program, University of Wisconsin School of Medicine and Public Health, Madison, WI, United States
- Department of Psychology, University of Wisconsin–Madison, Madison, WI, United States
- Medical Scientist Training Program, University of Wisconsin School of Medicine and Public Health, Madison, WI, United States
- Department of Psychiatry, University of Wisconsin–Madison, Madison, WI, United States
| |
Collapse
|
176
|
Espinosa-Ramos JI, Capecci E, Kasabov N. A Computational Model of Neuroreceptor-Dependent Plasticity (NRDP) Based on Spiking Neural Networks. IEEE Trans Cogn Dev Syst 2019. [DOI: 10.1109/tcds.2017.2776863] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
|
177
|
Papo D. Neurofeedback: Principles, appraisal, and outstanding issues. Eur J Neurosci 2019; 49:1454-1469. [PMID: 30570194 DOI: 10.1111/ejn.14312] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2018] [Revised: 11/21/2018] [Accepted: 11/27/2018] [Indexed: 12/16/2022]
Abstract
Neurofeedback is a form of brain training in which subjects are fed back information about some measure of their brain activity which they are instructed to modify in a way thought to be functionally advantageous. Over the last 20 years, neurofeedback has been used to treat various neurological and psychiatric conditions, and to improve cognitive function in various contexts. However, in spite of a growing popularity, neurofeedback protocols typically make (often covert) assumptions on what aspects of brain activity to target, where in the brain to act and how, which have far-reaching implications for the assessment of its potential and efficacy. Here we critically examine some conceptual and methodological issues associated with the way neurofeedback's general objectives and neural targets are defined. The neural mechanisms through which neurofeedback may act at various spatial and temporal scales, and the way its efficacy is appraised are reviewed, and the extent to which neurofeedback may be used to control functional brain activity discussed. Finally, it is proposed that gauging neurofeedback's potential, as well as assessing and improving its efficacy will require better understanding of various fundamental aspects of brain dynamics and a more precise definition of functional brain activity and brain-behaviour relationships.
Collapse
Affiliation(s)
- David Papo
- SCALab, CNRS, Université de Lille, Villeneuve d'Ascq, France
| |
Collapse
|
178
|
Yang M, Yang Z, Yuan T, Feng W, Wang P. A Systemic Review of Functional Near-Infrared Spectroscopy for Stroke: Current Application and Future Directions. Front Neurol 2019; 10:58. [PMID: 30804877 PMCID: PMC6371039 DOI: 10.3389/fneur.2019.00058] [Citation(s) in RCA: 70] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2018] [Accepted: 01/16/2019] [Indexed: 02/05/2023] Open
Abstract
Background: Survivors of stroke often experience significant disability and impaired quality of life. The recovery of motor or cognitive function requires long periods. Neuroimaging could measure changes in the brain and monitor recovery process in order to offer timely treatment and assess the effects of therapy. A non-invasive neuroimaging technique near-infrared spectroscopy (NIRS) with its ambulatory, portable, low-cost nature without fixation of subjects has attracted extensive attention. Methods: We conducted a comprehensive literature review in order to review the use of NIRS in stroke or post-stroke patients in July 2018. NCBI Pubmed database, EMBASE database, Cochrane Library and ScienceDirect database were searched. Results: Overall, we reviewed 66 papers. NIRS has a wide range of application, including in monitoring upper limb, lower limb recovery, motor learning, cortical function recovery, cerebral hemodynamic changes, cerebral oxygenation, as well as in therapeutic method, clinical researches, and evaluation of the risk for stroke. Conclusions: This study provides a preliminary evidence of the application of NIRS in stroke patients as a monitoring, therapeutic, and research tool. Further studies could give more emphasize on the combination of NIRS with other techniques and its utility in the prevention of stroke.
Collapse
Affiliation(s)
- Muyue Yang
- Department of Rehabilitation Medicine, Ruijin Hospital, Shanghai, China.,School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Zhen Yang
- Core Facility of West China Hospital, Sichuan University, Chengdu, China
| | - Tifei Yuan
- Shanghai Mental Health Centre, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Wuwei Feng
- Department of Neurology, Medical University of South Carolina, Charleston, SC, United States
| | - Pu Wang
- Department of Rehabilitation Medicine, Ruijin Hospital, Shanghai, China
| |
Collapse
|
179
|
Ramos-Murguialday A, Curado MR, Broetz D, Yilmaz Ö, Brasil FL, Liberati G, Garcia-Cossio E, Cho W, Caria A, Cohen LG, Birbaumer N. Brain-Machine Interface in Chronic Stroke: Randomized Trial Long-Term Follow-up. Neurorehabil Neural Repair 2019; 33:188-198. [PMID: 30722727 DOI: 10.1177/1545968319827573] [Citation(s) in RCA: 47] [Impact Index Per Article: 9.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
BACKGROUND Brain-machine interfaces (BMIs) have been recently proposed as a new tool to induce functional recovery in stroke patients. OBJECTIVE Here we evaluated long-term effects of BMI training and physiotherapy in motor function of severely paralyzed chronic stroke patients 6 months after intervention. METHODS A total of 30 chronic stroke patients with severe hand paresis from our previous study were invited, and 28 underwent follow-up assessments. BMI training included voluntary desynchronization of ipsilesional EEG-sensorimotor rhythms triggering paretic upper-limb movements via robotic orthoses (experimental group, n = 16) or random orthoses movements (sham group, n = 12). Both groups received identical physiotherapy following BMI sessions and a home-based training program after intervention. Upper-limb motor assessment scores, electromyography (EMG), and functional magnetic resonance imaging (fMRI) were assessed before (Pre), immediately after (Post1), and 6 months after intervention (Post2). RESULTS The experimental group presented with upper-limb Fugl-Meyer assessment (cFMA) scores significantly higher in Post2 (13.44 ± 1.96) as compared with the Pre session (11.16 ± 1.73; P = .015) and no significant changes between Post1 and Post2 sessions. The Sham group showed no significant changes on cFMA scores. Ashworth scores and EMG activity in both groups increased from Post1 to Post2. Moreover, fMRI-BOLD laterality index showed no significant difference from Pre or Post1 to Post2 sessions. CONCLUSIONS BMI-based rehabilitation promotes long-lasting improvements in motor function of chronic stroke patients with severe paresis and represents a promising strategy in severe stroke neurorehabilitation.
Collapse
Affiliation(s)
- Ander Ramos-Murguialday
- 1 University of Tubingen, Tübingen, Germany.,2 TECNALIA Health Technologies, Neurotechnology Laboratory, San Sebastian, Spain
| | - Marco R Curado
- 1 University of Tubingen, Tübingen, Germany.,3 AbbVie Pharmaceuticals, Ludwigshafen, Germany
| | | | - Özge Yilmaz
- 1 University of Tubingen, Tübingen, Germany.,4 Bahcesehir University, Istanbul, Turkey
| | - Fabricio L Brasil
- 1 University of Tubingen, Tübingen, Germany.,5 Santos Dumont Institute, Macaiba, Brazil
| | - Giulia Liberati
- 1 University of Tubingen, Tübingen, Germany.,6 Université catholique de Louvain, Brussels, Belgium
| | - Eliana Garcia-Cossio
- 1 University of Tubingen, Tübingen, Germany.,7 NeuroCare Group, Mental Health Care, Munich, Germany
| | - Woosang Cho
- 1 University of Tubingen, Tübingen, Germany.,8 g.tec Medical Engineering GmbH, Schiedlberg, Austria
| | | | | | - Niels Birbaumer
- 1 University of Tubingen, Tübingen, Germany.,10 WYSS-Center of Bio- and Neuroengineering, Geneva, Switzerland
| |
Collapse
|
180
|
Zhang J, Jadavji Z, Zewdie E, Kirton A. Evaluating If Children Can Use Simple Brain Computer Interfaces. Front Hum Neurosci 2019; 13:24. [PMID: 30778293 PMCID: PMC6369154 DOI: 10.3389/fnhum.2019.00024] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2018] [Accepted: 01/18/2019] [Indexed: 11/13/2022] Open
Abstract
Background: The options for severely disabled children with intact cognition to interact with their environment are extremely limited. A brain computer interface (BCI) has the potential to allow such persons to gain meaningful function, communication, and independence. While the pediatric population might benefit most from BCI technology, research to date has been predominantly in adults. Methods: In this prospective, cross-over study, we quantified the ability of healthy school-aged children to perform simple tasks using a basic, commercially available, EEG-based BCI. Typically developing children aged 6-18 years were recruited from the community. BCI training consisted of a brief set-up and EEG recording while performing specific tasks using an inexpensive, commercially available BCI system (EMOTIV EPOC). Two tasks were trained (driving a remote-control car and moving a computer cursor) each using two strategies (sensorimotor and visual imagery). Primary outcome was the kappa coefficient between requested and achieved performance. Effects of task, strategy, age, and learning were also explored. Results: Twenty-six of thirty children completed the study (mean age 13.2 ± 3.6 years, 27% female). Tolerability was excellent with >90% reporting the experience as neutral or pleasant. Older children achieved performance comparable to adult studies, but younger age was associated with lesser though still good performance. The car task demonstrated higher performance compared to the cursor task (p = 0.027). Thought strategy was also associated with performance with visual imagery strategies outperforming sensorimotor approaches (p = 0.031). Conclusion: Children can quickly achieve control and execute multiple tasks using simple EEG-based BCI systems. Performance depends on strategy, task and age. Such success in the developing brain mandates exploration of such practical systems in severely disabled children.
Collapse
Affiliation(s)
- Jack Zhang
- Calgary Pediatric Stroke Program, Alberta Children's Hospital Research Institute, University of Calgary, Calgary, AB, Canada.,Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada
| | - Zeanna Jadavji
- Calgary Pediatric Stroke Program, Alberta Children's Hospital Research Institute, University of Calgary, Calgary, AB, Canada.,Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada
| | - Ephrem Zewdie
- Calgary Pediatric Stroke Program, Alberta Children's Hospital Research Institute, University of Calgary, Calgary, AB, Canada.,Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada
| | - Adam Kirton
- Calgary Pediatric Stroke Program, Alberta Children's Hospital Research Institute, University of Calgary, Calgary, AB, Canada.,Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada.,Alberta Children's Hospital Research Institute, University of Calgary, Calgary, AB, Canada
| |
Collapse
|
181
|
Song M, Kim J. A Paradigm to Enhance Motor Imagery Using Rubber Hand Illusion Induced by Visuo-Tactile Stimulus. IEEE Trans Neural Syst Rehabil Eng 2019; 27:477-486. [PMID: 30703031 DOI: 10.1109/tnsre.2019.2895029] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
Enhancing motor imagery (MI) results in amplified event-related desynchronization (ERD) and is important for MI-based rehabilitation and brain-computer interface (BCI) applications. Many attempts to enhance the MI by providing a visual guidance have been reported. We believe that the rubber hand illusion (RHI), which induces body ownership over an external object, can provide better guidance to enhance MI; thus, an RHI-based paradigm with motorized moving rubber hand was proposed. To validate the proposed MI enhancing paradigm, we conducted an experimental comparison among paradigms with 20 healthy subjects. The peak amplitude and arrival times of ERD were compared at contralateral and ipsilateral electroencephalogram channels. We found significantly amplified ERD caused by the proposed paradigm, which is similar to the ERD caused by motor execution. In addition, the arrival time suggests that the proposed paradigm is applicable for BCI. In conclusion, the proposed paradigm can significantly enhance the MI with better characteristics for use with BCI.
Collapse
|
182
|
Corsi MC, Chavez M, Schwartz D, Hugueville L, Khambhati AN, Bassett DS, De Vico Fallani F. Integrating EEG and MEG Signals to Improve Motor Imagery Classification in Brain–Computer Interface. Int J Neural Syst 2019; 29:1850014. [DOI: 10.1142/s0129065718500144] [Citation(s) in RCA: 47] [Impact Index Per Article: 9.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
We adopted a fusion approach that combines features from simultaneously recorded electroencephalogram (EEG) and magnetoencephalogram (MEG) signals to improve classification performances in motor imagery-based brain–computer interfaces (BCIs). We applied our approach to a group of 15 healthy subjects and found a significant classification performance enhancement as compared to standard single-modality approaches in the alpha and beta bands. Taken together, our findings demonstrate the advantage of considering multimodal approaches as complementary tools for improving the impact of noninvasive BCIs.
Collapse
Affiliation(s)
- Marie-Constance Corsi
- Inria, Aramis project-team, F-75013, Paris, France
- Institut du Cerveau et de la Moelle épinière, ICM, F-75013, Paris, France
- Inserm, U 1127, F-75013, Paris, France
- CNRS, UMR 7225, F-75013, Paris, France
- Sorbonne Université, F-75013, Paris, France
| | | | - Denis Schwartz
- Centre de NeuroImagerie de Recherche — CENIR, Centre de Recherche de l’Institut du Cerveau et de la Moelle Epinère, Université Pierre et Marie Curie-Paris 6 UMR-S975, INSERM U975, CNRS UMR7225, Groupe Hospitalier Pitié-Salpêtrière, Paris, France
| | - Laurent Hugueville
- Centre de NeuroImagerie de Recherche — CENIR, Centre de Recherche de l’Institut du Cerveau et de la Moelle Epinère, Université Pierre et Marie Curie-Paris 6 UMR-S975, INSERM U975, CNRS UMR7225, Groupe Hospitalier Pitié-Salpêtrière, Paris, France
| | - Ankit N. Khambhati
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Danielle S. Bassett
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Electrical and Systems Engineering, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Physics, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Neurology, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Fabrizio De Vico Fallani
- Inria, Aramis project-team, F-75013, Paris, France
- Institut du Cerveau et de la Moelle épinière, ICM, F-75013, Paris, France
- Inserm, U 1127, F-75013, Paris, France
- CNRS, UMR 7225, F-75013, Paris, France
- Sorbonne Université, F-75013, Paris, France
| |
Collapse
|
183
|
Abiri R, Borhani S, Sellers EW, Jiang Y, Zhao X. A comprehensive review of EEG-based brain–computer interface paradigms. J Neural Eng 2019; 16:011001. [DOI: 10.1088/1741-2552/aaf12e] [Citation(s) in RCA: 270] [Impact Index Per Article: 54.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
|
184
|
De Vico Fallani F, Bassett DS. Network neuroscience for optimizing brain-computer interfaces. Phys Life Rev 2019; 31:304-309. [PMID: 30642781 DOI: 10.1016/j.plrev.2018.10.001] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2018] [Revised: 05/29/2018] [Accepted: 10/10/2018] [Indexed: 01/30/2023]
Abstract
Human-machine interactions are being increasingly explored to create alternative ways of communication and to improve our daily life. Based on a classification of the user's intention from the user's underlying neural activity, brain-computer interfaces (BCIs) allow direct interactions with the external environment while bypassing the traditional effector of the musculoskeletal system. Despite the enormous potential of BCIs, there are still a number of challenges that limit their societal impact, ranging from the correct decoding of a human's thoughts, to the application of effective learning strategies. Despite several important engineering advances, the basic neuroscience behind these challenges remains poorly explored. Indeed, BCIs involve complex dynamic changes related to neural plasticity at a diverse range of spatiotemporal scales. One promising antidote to this complexity lies in network science, which provides a natural language in which to model the organizational principles of brain architecture and function as manifest in its interconnectivity. Here, we briefly review the main limitations currently affecting BCIs, and we offer our perspective on how they can be addressed by means of network theoretic approaches. We posit that the emerging field of network neuroscience will prove to be an effective tool to unlock human-machine interactions.
Collapse
Affiliation(s)
- Fabrizio De Vico Fallani
- Inria Paris, Aramis project-team, F-75013, Paris, France; Institut du Cerveau et de la Moelle Epiniere, ICM, Inserm, U 1127, CNRS, UMR 7225, Sorbonne Université, F-75013, Paris, France.
| | - Danielle S Bassett
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, 19104, USA; Department of Electrical and Systems Engineering, University of Pennsylvania, Philadelphia, PA, 19104, USA; Department of Physics and Astronomy, University of Pennsylvania, Philadelphia, PA, 19104, USA; Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| |
Collapse
|
185
|
Carvalho R, Dias N, Cerqueira JJ. Brain-machine interface of upper limb recovery in stroke patients rehabilitation: A systematic review. PHYSIOTHERAPY RESEARCH INTERNATIONAL 2019; 24:e1764. [PMID: 30609208 DOI: 10.1002/pri.1764] [Citation(s) in RCA: 32] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
Abstract
BACKGROUND Technologies such as brain-computer interfaces are able to guide mental practice, in particular motor imagery performance, to promote recovery in stroke patients, as a combined approach to conventional therapy. OBJECTIVE The aim of this systematic review was to provide a status report regarding advances in brain-computer interface, focusing in particular in upper limb motor recovery. METHODS The databases PubMed, Scopus, and PEDro were systematically searched for articles published between January 2010 and December 2017. The selected studies were randomized controlled trials involving brain-computer interface interventions in stroke patients, with upper limb assessment as primary outcome measures. Reviewers independently extracted data and assessed the methodological quality of the trials, using the PEDro methodologic rating scale. RESULTS From 309 titles, we included nine studies with high quality (PEDro ≥ 6). We found that the most common interface used was non-invasive electroencephalography, and the main neurofeedback, in stroke rehabilitation, was usually visual abstract or a combination with the control of an orthosis/robotic limb. Moreover, the Fugl-Meyer Assessment Scale was a major outcome measure in eight out of nine studies. In addition, the benefits of functional electric stimulation associated to an interface were found in three studies. CONCLUSIONS Neurofeedback training with brain-computer interface systems seem to promote clinical and neurophysiologic changes in stroke patients, in particular those with long-term efficacy.
Collapse
Affiliation(s)
- Raquel Carvalho
- Department of Physical Therapy, CESPU, Institute of Research and Advanced Training in Health Sciences and Technologies, Gandra, Portugal.,Life and Health Sciences Research Institute (ICVS), School of Health Sciences, University of Minho, Braga, Portugal
| | - Nuno Dias
- Life and Health Sciences Research Institute (ICVS), School of Health Sciences, University of Minho, Braga, Portugal.,ICVS/3B's-PT Government Associate Laboratory, Braga/Guimarães, Portugal.,2Ai - Polytechnic Institute of Cavado and Ave, Barcelos, Portugal
| | - João José Cerqueira
- Life and Health Sciences Research Institute (ICVS), School of Health Sciences, University of Minho, Braga, Portugal.,ICVS/3B's-PT Government Associate Laboratory, Braga/Guimarães, Portugal
| |
Collapse
|
186
|
Closed-Loop Systems and In Vitro Neuronal Cultures: Overview and Applications. ADVANCES IN NEUROBIOLOGY 2019; 22:351-387. [DOI: 10.1007/978-3-030-11135-9_15] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/12/2023]
|
187
|
Maksimenko VA, Hramov AE, Frolov NS, Lüttjohann A, Nedaivozov VO, Grubov VV, Runnova AE, Makarov VV, Kurths J, Pisarchik AN. Increasing Human Performance by Sharing Cognitive Load Using Brain-to-Brain Interface. Front Neurosci 2018; 12:949. [PMID: 30631262 PMCID: PMC6315120 DOI: 10.3389/fnins.2018.00949] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2018] [Accepted: 11/29/2018] [Indexed: 11/13/2022] Open
Abstract
Brain-computer interfaces (BCIs) attract a lot of attention because of their ability to improve the brain's efficiency in performing complex tasks using a computer. Furthermore, BCIs can increase human's performance not only due to human-machine interactions, but also thanks to an optimal distribution of cognitive load among all members of a group working on a common task, i.e., due to human-human interaction. The latter is of particular importance when sustained attention and alertness are required. In every day practice, this is a common occurrence, for example, among office workers, pilots of a military or a civil aircraft, power plant operators, etc. Their routinely work includes continuous monitoring of instrument readings and implies a heavy cognitive load due to processing large amounts of visual information. In this paper, we propose a brain-to-brain interface (BBI) which estimates brain states of every participant and distributes a cognitive load among all members of the group accomplishing together a common task. The BBI allows sharing the whole workload between all participants depending on their current cognitive performance estimated from their electrical brain activity. We show that the team efficiency can be increased due to redistribution of the work between participants so that the most difficult workload falls on the operator who exhibits maximum performance. Finally, we demonstrate that the human-to-human interaction is more efficient in the presence of a certain delay determined by brain rhythms. The obtained results are promising for the development of a new generation of communication systems based on neurophysiological brain activity of interacting people. Such BBIs will distribute a common task between all group members according to their individual physical conditions.
Collapse
Affiliation(s)
- Vladimir A Maksimenko
- REC "Artificial Intelligence Systems and Neurotechnology", Yuri Gagarin State Technical University of Saratov, Saratov, Russia
| | - Alexander E Hramov
- REC "Artificial Intelligence Systems and Neurotechnology", Yuri Gagarin State Technical University of Saratov, Saratov, Russia
| | - Nikita S Frolov
- REC "Artificial Intelligence Systems and Neurotechnology", Yuri Gagarin State Technical University of Saratov, Saratov, Russia
| | | | - Vladimir O Nedaivozov
- REC "Artificial Intelligence Systems and Neurotechnology", Yuri Gagarin State Technical University of Saratov, Saratov, Russia
| | - Vadim V Grubov
- REC "Artificial Intelligence Systems and Neurotechnology", Yuri Gagarin State Technical University of Saratov, Saratov, Russia
| | - Anastasia E Runnova
- REC "Artificial Intelligence Systems and Neurotechnology", Yuri Gagarin State Technical University of Saratov, Saratov, Russia
| | - Vladimir V Makarov
- REC "Artificial Intelligence Systems and Neurotechnology", Yuri Gagarin State Technical University of Saratov, Saratov, Russia
| | - Jürgen Kurths
- Potsdam Institute for Climate Impact Research, Potsdam, Germany.,Department of Physics, Humboldt University, Berlin, Germany.,Institute for Complex Systems and Mathematical Biology, University of Aberdeen, Aberdeen, United Kingdom
| | - Alexander N Pisarchik
- REC "Artificial Intelligence Systems and Neurotechnology", Yuri Gagarin State Technical University of Saratov, Saratov, Russia.,Center for Biomedical Technology, Technical University of Madrid, Madrid, Spain
| |
Collapse
|
188
|
Zhang K, Chen X, Liu F, Tang H, Wang J, Wen W. System Framework of Robotics in Upper Limb Rehabilitation on Poststroke Motor Recovery. Behav Neurol 2018; 2018:6737056. [PMID: 30651892 PMCID: PMC6311736 DOI: 10.1155/2018/6737056] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2018] [Revised: 08/04/2018] [Accepted: 08/28/2018] [Indexed: 11/24/2022] Open
Abstract
Neurological impairments such as stroke cause damage to the functional mobility of survivors and affect their ability to perform activities of daily living. Recently, robotic treatment for upper limb stroke rehabilitation has received significant attention because it can provide high-intensity and repetitive movement therapy. In this review, the current status of upper limb rehabilitation robots is explored. Firstly, an overview of mechanical design of robotics for upper-limb rehabilitation and clinical effects of part robots are provided. Then, the comparisons of human-machine interactions, control strategies, driving modes, and training modes are described. Finally, the development and the possible future directions of the upper limb rehabilitation robot are discussed.
Collapse
Affiliation(s)
- Kai Zhang
- Institute of Robotics and Intelligent System, School of Mechanical Engineering, Xi'an Jiaotong University, Xi'an 710049, China
- Shaanxi Key Laboratory of Intelligent Robots, Xi'an 710049, China
| | | | - Fei Liu
- Baoxing Hospital, Shenzhen 518100, China
| | - Haili Tang
- Baoxing Hospital, Shenzhen 518100, China
| | - Jing Wang
- Institute of Robotics and Intelligent System, School of Mechanical Engineering, Xi'an Jiaotong University, Xi'an 710049, China
- Shaanxi Key Laboratory of Intelligent Robots, Xi'an 710049, China
| | - Weina Wen
- Baoxing Hospital, Shenzhen 518100, China
| |
Collapse
|
189
|
Shahtalebi S, Mohammadi A. Bayesian Optimized Spectral Filters Coupled With Ternary ECOC for Single-Trial EEG Classification. IEEE Trans Neural Syst Rehabil Eng 2018; 26:2249-2259. [DOI: 10.1109/tnsre.2018.2877987] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
|
190
|
Hu M, Ji F, Lu Z, Huang W, Khosrowabadi R, Zhao L, Ang KK, Phua KS, Nasrallah FA, Chuang KH, Stephenson MC, Totman J, Jiang X, Chew E, Guan C, Zhou J. Differential Amplitude of Low-Frequency Fluctuations in brain networks after BCI Training with and without tDCS in Stroke. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2018; 2018:1050-1053. [PMID: 30440571 DOI: 10.1109/embc.2018.8512395] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Mapping the brain alterations post stroke and post intervention is important for rehabilitation therapy development. Previous work has shown changes in functional connectivity based on resting-state fMRI, structural connectivity derived from diffusion MRI and perfusion as a result of brain-computer interface-assisted motor imagery (MI-BCI) and transcranial direct current stimulation (tDCS) in upper-limb stroke rehabilitation. Besides functional connectivity, regional amplitude of local low-frequency fluctuations (ALFF) may provide complementary information on the underlying neural mechanism in disease. Yet, findings on spontaneous brain activity during resting-state in stroke patients after intervention are limited and inconsistent. Here, we sought to investigate the different brain alteration patterns induced by tDCS compared to MI-BCI for upper-limb rehabilitation in chronic stroke patients using resting-state fMRI-based ALFF method. Our results suggested that stroke patients have lower ALFF in the ipsilesional somatomotor network compared to controls at baseline. Increased ALFF at contralesional somatomotor network and alterations in higher-level cognitive networks such as the default mode network (DMN) and salience networks accompany motor recovery after intervention; though the MI-BCI alone group and MI-BCI combined with tDCS group exhibit differential patterns.
Collapse
|
191
|
Jeunet C, Glize B, McGonigal A, Batail JM, Micoulaud-Franchi JA. Using EEG-based brain computer interface and neurofeedback targeting sensorimotor rhythms to improve motor skills: Theoretical background, applications and prospects. Neurophysiol Clin 2018; 49:125-136. [PMID: 30414824 DOI: 10.1016/j.neucli.2018.10.068] [Citation(s) in RCA: 43] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2018] [Revised: 10/17/2018] [Accepted: 10/17/2018] [Indexed: 11/28/2022] Open
Abstract
Many Brain Computer Interface (BCI) and neurofeedback studies have investigated the impact of sensorimotor rhythm (SMR) self-regulation training procedures on motor skills enhancement in healthy subjects and patients with motor disabilities. This critical review aims first to introduce the different definitions of SMR EEG target in BCI/Neurofeedback studies and to summarize the background from neurophysiological and neuroplasticity studies that led to SMR being considered as reliable and valid EEG targets to improve motor skills through BCI/neurofeedback procedures. The second objective of this review is to introduce the main findings regarding SMR BCI/neurofeedback in healthy subjects. Third, the main findings regarding BCI/neurofeedback efficiency in patients with hypokinetic activities (in particular, motor deficit following stroke) as well as in patients with hyperkinetic activities (in particular, Attention Deficit Hyperactivity Disorder, ADHD) will be introduced. Due to a range of limitations, a clear association between SMR BCI/neurofeedback training and enhanced motor skills has yet to be established. However, SMR BCI/neurofeedback appears promising, and highlights many important challenges for clinical neurophysiology with regards to therapeutic approaches using BCI/neurofeedback.
Collapse
Affiliation(s)
- Camille Jeunet
- Laboratoire cognition, langues, langage, ergonomie (CLLE), CNRS/Université Toulouse Jean-Jaurès, 31058 Toulouse, France
| | - Bertrand Glize
- EA4136, Physical and Rehabilitation Medicine Unit, University of Bordeaux, Bordeaux University Hospital, 33000 Bordeaux, France
| | - Aileen McGonigal
- Inserm, Aix Marseille Université, INS, institut de neurosciences des systèmes, 13005 Marseille, France; Service de neurophysiologie clinique, centre hospitalo universitaire de la Timone, 264, rue Saint-Pierre, 13005 Marseille, France
| | - Jean-Marie Batail
- Academic Psychiatry Department, centre hospitalier Guillaume-Régnier, 35033 Rennes, France; EA 4712 Behavior and Basal Ganglia, Rennes 1 University, CHU de Rennes, 35033 Rennes, France
| | - Jean-Arthur Micoulaud-Franchi
- Service d'explorations fonctionnelles du système nerveux, clinique du sommeil, CHU de Bordeaux, place Amélie Raba-Léon, 33076 Bordeaux, France; USR CNRS 3413 SANPSY, université de Bordeaux, CHU Pellegrin, 33076 Bordeaux, France.
| |
Collapse
|
192
|
Takahashi K, Kato K, Mizuguchi N, Ushiba J. Precise estimation of human corticospinal excitability associated with the levels of motor imagery-related EEG desynchronization extracted by a locked-in amplifier algorithm. J Neuroeng Rehabil 2018; 15:93. [PMID: 30384845 PMCID: PMC6211493 DOI: 10.1186/s12984-018-0440-5] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2017] [Accepted: 10/18/2018] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Physical motor exercise aided by an electroencephalogram (EEG)-based brain-computer interface (BCI) is known to improve motor recovery in patients with stroke. In such a BCI paradigm, event-related desynchronization (ERD) in the alpha and beta bands extracted from EEG recorded over the primary sensorimotor area (SM1) is often used, since ERD has been suggested to be associated with an increase of corticospinal excitability. Recently, we demonstrated a novel online lock-in amplifier (LIA) algorithm to estimate the amplitude modulation of motor-related SM1 ERD. With this algorithm, the delay time, accuracy, and stability to estimate motor-related SM1 ERD were significantly improved compared with the conventional fast Fourier transformation (FFT) algorithm. These technical improvements to extract an ERD trace imply a potential advantage for a better trace of the excitatory status of the SM1 in a BCI context. Therefore, the aim of this study was to assess the precision of LIA-based ERD tracking for estimation of corticospinal excitability using a transcranial magnetic stimulation (TMS) paradigm. METHODS The motor evoked potentials (MEPs) induced by single-pulse TMS over the primary motor cortex depending on the magnitudes of SM1 ERD (i.e., 35% and 70%) extracted by the online LIA or FFT algorithm were monitored during a motor imagery task of wrist extension in 17 healthy participants. Then, the peak-to-peak amplitudes of MEPs and their variabilities were assessed to investigate the precision of the algorithms. RESULTS We found greater MEP amplitude evoked by single-pulse TMS triggered by motor imagery-related alpha SM1 ERD than at rest. This enhancement was associated with the magnitude of ERD in both FFT and LIA algorithms. Moreover, we found that the variabilities of peak-to-peak MEP amplitudes at 35% and 70% ERDs calculated by the novel online LIA algorithm were smaller than those extracted using the conventional FFT algorithm. CONCLUSIONS The present study demonstrated that the calculation of motor imagery-related SM1 ERDs using the novel online LIA algorithm led to a more precise estimation of corticospinal excitability than when the ordinary FFT-based algorithm was used.
Collapse
Affiliation(s)
- Kensho Takahashi
- Faculty of Science and Technology, Keio University, 3-14-1 Hiyoshi, Kohoku-ku, Yokohama, Kanagawa, 223-8522, Japan
| | - Kenji Kato
- Faculty of Science and Technology, Keio University, 3-14-1 Hiyoshi, Kohoku-ku, Yokohama, Kanagawa, 223-8522, Japan.,Department of Rehabilitation Medicine, Keio University School of Medicine, 35 Shinanomachi, Shinjuku, Tokyo, 160-8582, Japan.,Present address: Center of Assistive Robotics and Rehabilitation for Longevity and Good Health, National Center for Geriatrics and Gerontology, 7-430, Morioka-cho, Obu, Aichi, 474-8511, Japan
| | - Nobuaki Mizuguchi
- Faculty of Science and Technology, Keio University, 3-14-1 Hiyoshi, Kohoku-ku, Yokohama, Kanagawa, 223-8522, Japan.,The Japan Society for the Promotion of Science, 5-3-1 Kojimachi, Chiyoda-ku, Tokyo, 102-0083, Japan
| | - Junichi Ushiba
- Faculty of Science and Technology, Keio University, 3-14-1 Hiyoshi, Kohoku-ku, Yokohama, Kanagawa, 223-8522, Japan. .,Keio Institute of Pure and Applied Sciences (KiPAS), Keio University, 3-14-1 Hiyoshi, Kohoku-ku, Yokohama, Kanagawa, 223-8522, Japan. .,Department of Biosciences and Informatics, Faculty of Science and Technology, Keio University, 3-14-1 Hiyoshi, Kouhoku-ku, Yokohama, Kanagawa, 223-8522, Japan.
| |
Collapse
|
193
|
López-Larraz E, Sarasola-Sanz A, Irastorza-Landa N, Birbaumer N, Ramos-Murguialday A. Brain-machine interfaces for rehabilitation in stroke: A review. NeuroRehabilitation 2018; 43:77-97. [PMID: 30056435 DOI: 10.3233/nre-172394] [Citation(s) in RCA: 55] [Impact Index Per Article: 9.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
BACKGROUND Motor paralysis after stroke has devastating consequences for the patients, families and caregivers. Although therapies have improved in the recent years, traditional rehabilitation still fails in patients with severe paralysis. Brain-machine interfaces (BMI) have emerged as a promising tool to guide motor rehabilitation interventions as they can be applied to patients with no residual movement. OBJECTIVE This paper reviews the efficiency of BMI technologies to facilitate neuroplasticity and motor recovery after stroke. METHODS We provide an overview of the existing rehabilitation therapies for stroke, the rationale behind the use of BMIs for motor rehabilitation, the current state of the art and the results achieved so far with BMI-based interventions, as well as the future perspectives of neural-machine interfaces. RESULTS Since the first pilot study by Buch and colleagues in 2008, several controlled clinical studies have been conducted, demonstrating the efficacy of BMIs to facilitate functional recovery in completely paralyzed stroke patients with noninvasive technologies such as the electroencephalogram (EEG). CONCLUSIONS Despite encouraging results, motor rehabilitation based on BMIs is still in a preliminary stage, and further improvements are required to boost its efficacy. Invasive and hybrid approaches are promising and might set the stage for the next generation of stroke rehabilitation therapies.
Collapse
Affiliation(s)
- E López-Larraz
- Institute of Medical Psychology and Behavioral Neurobiology, University of Tübingen, Tübingen, Germany
| | - A Sarasola-Sanz
- Institute of Medical Psychology and Behavioral Neurobiology, University of Tübingen, Tübingen, Germany.,International Max Planck Research School (IMPRS) for Cognitive and Systems Neuroscience, University of Tübingen, Tübingen, Germany.,Neurotechnology, Tecnalia Research & Innovation, San Sebastián, Spain
| | - N Irastorza-Landa
- Institute of Medical Psychology and Behavioral Neurobiology, University of Tübingen, Tübingen, Germany.,International Max Planck Research School (IMPRS) for Cognitive and Systems Neuroscience, University of Tübingen, Tübingen, Germany.,IKERBASQUE, Basque Foundation for Science, Bilbao, Spain
| | - 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.,Neurotechnology, Tecnalia Research & Innovation, San Sebastián, Spain
| |
Collapse
|
194
|
Norman SL, McFarland DJ, Miner A, Cramer SC, Wolbrecht ET, Wolpaw JR, Reinkensmeyer DJ. Controlling pre-movement sensorimotor rhythm can improve finger extension after stroke. J Neural Eng 2018; 15:056026. [PMID: 30063219 PMCID: PMC6158016 DOI: 10.1088/1741-2552/aad724] [Citation(s) in RCA: 25] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
OBJECTIVE Brain-computer interface (BCI) technology is attracting increasing interest as a tool for enhancing recovery of motor function after stroke, yet the optimal way to apply this technology is unknown. Here, we studied the immediate and therapeutic effects of BCI-based training to control pre-movement sensorimotor rhythm (SMR) amplitude on robot-assisted finger extension in people with stroke. APPROACH Eight people with moderate to severe hand impairment due to chronic stroke completed a four-week three-phase protocol during which they practiced finger extension with assistance from the FINGER robotic exoskeleton. In Phase 1, we identified spatiospectral SMR features for each person that correlated with the intent to extend the index and/or middle finger(s). In Phase 2, the participants learned to increase or decrease SMR features given visual feedback, without movement. In Phase 3, the participants were cued to increase or decrease their SMR features, and when successful, were then cued to immediately attempt to extend the finger(s) with robot assistance. MAIN RESULTS Of the four participants that achieved SMR control in Phase 2, three initiated finger extensions with a reduced reaction time after decreasing (versus increasing) pre-movement SMR amplitude during Phase 3. Two also extended at least one of their fingers more forcefully after decreasing pre-movement SMR amplitude. Hand function, measured by the box and block test (BBT), improved by 7.3 ± 7.5 blocks versus 3.5 ± 3.1 blocks in those with and without SMR control, respectively. Higher BBT scores at baseline correlated with a larger change in BBT score. SIGNIFICANCE These results suggest that learning to control person-specific pre-movement SMR features associated with finger extension can improve finger extension ability after stroke for some individuals. These results merit further investigation in a rehabilitation context.
Collapse
Affiliation(s)
- S L Norman
- University of California Irvine, Irvine, CA, United States of America
| | | | | | | | | | | | | |
Collapse
|
195
|
Chu Y, Zhao X, Zou Y, Xu W, Han J, Zhao Y. A Decoding Scheme for Incomplete Motor Imagery EEG With Deep Belief Network. Front Neurosci 2018; 12:680. [PMID: 30323737 PMCID: PMC6172343 DOI: 10.3389/fnins.2018.00680] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2018] [Accepted: 09/10/2018] [Indexed: 01/03/2023] Open
Abstract
High accuracy decoding of electroencephalogram (EEG) signal is still a major challenge that can hardly be solved in the design of an effective motor imagery-based brain-computer interface (BCI), especially when the signal contains various extreme artifacts and outliers arose from data loss. The conventional process to avoid such cases is to directly reject the entire severely contaminated EEG segments, which leads to a drawback that the BCI has no decoding results during that certain period. In this study, a novel decoding scheme based on the combination of Lomb-Scargle periodogram (LSP) and deep belief network (DBN) was proposed to recognize the incomplete motor imagery EEG. Particularly, instead of discarding the entire segment, two forms of data removal were adopted to eliminate the EEG portions with extreme artifacts and data loss. The LSP was utilized to steadily extract the power spectral density (PSD) features from the incomplete EEG constructed by the remaining portions. A DBN structure based on the restricted Boltzmann machine (RBM) was exploited and optimized to perform the classification task. Various comparative experiments were conducted and evaluated on simulated signal and real incomplete motor imagery EEG, including the comparison of three PSD extraction methods (fast Fourier transform, Welch and LSP) and two classifiers (DBN and support vector machine, SVM). The results demonstrate that the LSP can estimate relative robust PSD features and the proposed scheme can significantly improve the decoding performance for the incomplete motor imagery EEG. This scheme can provide an alternative decoding solution for the motor imagery EEG contaminated by extreme artifacts and data loss. It can be beneficial to promote the stability, smoothness and maintain consecutive outputs without interruption for a BCI system that is suitable for the online and long-term application.
Collapse
Affiliation(s)
- Yaqi Chu
- State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, China
- Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Xingang Zhao
- State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, China
- Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang, China
| | - Yijun Zou
- State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, China
- Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Weiliang Xu
- State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, China
- Department of Mechanical Engineering, University of Auckland, Auckland, New Zealand
| | - Jianda Han
- State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, China
- Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang, China
| | - Yiwen Zhao
- State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, China
- Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang, China
| |
Collapse
|
196
|
Chouhan T, Robinson N, Vinod AP, Ang KK, Guan C. Wavlet phase-locking based binary classification of hand movement directions from EEG. J Neural Eng 2018; 15:066008. [PMID: 30181429 DOI: 10.1088/1741-2552/aadeed] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
OBJECTIVE Brain signals can be used to extract relevant features to decode various limb movement parameters such as the direction of upper limb movements. Amplitude based feature extraction techniques have been used to study such motor activity of upper limbs whereas phase synchrony, used to estimate functional relationship between signals, has rarely been used to study single hand movements in different directions. APPROACH In this paper, a novel phase-locking-based feature extraction method, called wavelet phase-locking value (W-PLV) is proposed to analyse synchronous EEG channel-pairs and classify hand movement directions. EEG data collected from seven subjects performing right hand movements in four orthogonal directions in the horizontal plane is used for this analysis. MAIN RESULTS Our proposed W-PLV based method achieves a mean binary classification accuracy of 76.85% over seven subjects using wavelet levels corresponding to ⩽12 Hz EEG. The results also show direction-dependent information in various wavelet levels and indicate the presence of relevant information in slow cortical potentials (<1 Hz) as well as higher wavelet levels (⩽12 Hz). SIGNIFICANCE This study presents a thorough analysis of the phase-locking patterns extracted from EEG corresponding to hand movements in different directions using W-PLV across various wavelet levels and verifies their discriminative ability in the single trial binary classification of hand movement directions.
Collapse
Affiliation(s)
- Tushar Chouhan
- School of Computer Science and Engineering, Nanyang Technological University, Singapore
| | | | | | | | | |
Collapse
|
197
|
Pereira J, Sburlea AI, Müller-Putz GR. EEG patterns of self-paced movement imaginations towards externally-cued and internally-selected targets. Sci Rep 2018; 8:13394. [PMID: 30190543 PMCID: PMC6127278 DOI: 10.1038/s41598-018-31673-2] [Citation(s) in RCA: 30] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2018] [Accepted: 08/23/2018] [Indexed: 11/25/2022] Open
Abstract
In this study, we investigate the neurophysiological signature of the interacting processes which lead to a single reach-and-grasp movement imagination (MI). While performing this task, the human healthy participants could either define their movement targets according to an external cue, or through an internal selection process. After defining their target, they could start the MI whenever they wanted. We recorded high density electroencephalographic (EEG) activity and investigated two neural correlates: the event-related potentials (ERPs) associated with the target selection, which reflect the perceptual and cognitive processes prior to the MI, and the movement-related cortical potentials (MRCPs), associated with the planning of the self-paced MI. We found differences in frontal and parietal areas between the late ERP components related to the internally-driven selection and the externally-cued process. Furthermore, we could reliably estimate the MI onset of the self-paced task. Next, we extracted MRCP features around the MI onset to train classifiers of movement vs. rest directly on self-paced MI data. We attained performance significantly higher than chance level for both time-locked and asynchronous classification. These findings contribute to the development of more intuitive brain-computer interfaces in which movement targets are defined internally and the movements are self-paced.
Collapse
Affiliation(s)
- Joana Pereira
- Institute of Neural Engineering, Graz University of Technology, Graz, Austria
| | | | | |
Collapse
|
198
|
Suefusa K, Tanaka T. Asynchronous Brain–Computer Interfacing Based on Mixed-Coded Visual Stimuli. IEEE Trans Biomed Eng 2018; 65:2119-2129. [DOI: 10.1109/tbme.2017.2785412] [Citation(s) in RCA: 20] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
|
199
|
Curtin A, Ayaz H. The Age of Neuroergonomics: Towards Ubiquitous and Continuous Measurement of Brain Function with fNIRS. JAPANESE PSYCHOLOGICAL RESEARCH 2018. [DOI: 10.1111/jpr.12227] [Citation(s) in RCA: 52] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
|
200
|
Pan G, Li JJ, Qi Y, Yu H, Zhu JM, Zheng XX, Wang YM, Zhang SM. Rapid Decoding of Hand Gestures in Electrocorticography Using Recurrent Neural Networks. Front Neurosci 2018; 12:555. [PMID: 30210272 PMCID: PMC6119703 DOI: 10.3389/fnins.2018.00555] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2018] [Accepted: 07/20/2018] [Indexed: 11/25/2022] Open
Abstract
Brain-computer interface (BCI) is a direct communication pathway between brain and external devices, and BCI-based prosthetic devices are promising to provide new rehabilitation options for people with motor disabilities. Electrocorticography (ECoG) signals contain rich information correlated with motor activities, and have great potential in hand gesture decoding. However, most existing decoders use long time windows, thus ignore the temporal dynamics within the period. In this study, we propose to use recurrent neural networks (RNNs) to exploit the temporal information in ECoG signals for robust hand gesture decoding. With RNN's high nonlinearity modeling ability, our method can effectively capture the temporal information in ECoG time series for robust gesture recognition. In the experiments, we decode three hand gestures using ECoG signals of two participants, and achieve an accuracy of 90%. Specially, we investigate the possibility of recognizing the gestures in a time interval as short as possible after motion onsets. Our method rapidly recognizes gestures within 0.5 s after motion onsets with an accuracy of about 80%. Experimental results also indicate that the temporal dynamics is especially informative for effective and rapid decoding of hand gestures.
Collapse
Affiliation(s)
- Gang Pan
- State Key Lab of CAD&CG, Zhejiang University, Hangzhou, China
- College of Computer Science and Technology, Zhejiang University, Hangzhou, China
| | - Jia-Jun Li
- College of Computer Science and Technology, Zhejiang University, Hangzhou, China
| | - Yu Qi
- College of Computer Science and Technology, Zhejiang University, Hangzhou, China
| | - Hang Yu
- College of Computer Science and Technology, Zhejiang University, Hangzhou, China
| | - Jun-Ming Zhu
- Department of Neurosurgery, The Second Affiliated Hospital of Zhejiang University, Hangzhou, China
| | - Xiao-Xiang Zheng
- Qiushi Academy for Advanced Studies, Zhejiang University, Hangzhou, China
| | - Yue-Ming Wang
- Qiushi Academy for Advanced Studies, Zhejiang University, Hangzhou, China
| | - Shao-Min Zhang
- Qiushi Academy for Advanced Studies, Zhejiang University, Hangzhou, China
| |
Collapse
|