101
|
Lulé D, Kübler A, Ludolph AC. Ethical Principles in Patient-Centered Medical Care to Support Quality of Life in Amyotrophic Lateral Sclerosis. Front Neurol 2019; 10:259. [PMID: 30967833 PMCID: PMC6439311 DOI: 10.3389/fneur.2019.00259] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2019] [Accepted: 02/26/2019] [Indexed: 12/12/2022] Open
Abstract
It is one of the primary goals of medical care to secure good quality of life (QoL) while prolonging survival. This is a major challenge in severe medical conditions with a prognosis such as amyotrophic lateral sclerosis (ALS). Further, the definition of QoL and the question whether survival in this severe condition is compatible with a good QoL is a matter of subjective and culture-specific debate. Some people without neurodegenerative conditions believe that physical decline is incompatible with satisfactory QoL. Current data provide extensive evidence that psychosocial adaptation in ALS is possible, indicated by a satisfactory QoL. Thus, there is no fatalistic link of loss of QoL when physical health declines. There are intrinsic and extrinsic factors that have been shown to successfully facilitate and secure QoL in ALS which will be reviewed in the following article following the four ethical principles (1) Beneficence, (2) Non-maleficence, (3) Autonomy and (4) Justice, which are regarded as key elements of patient centered medical care according to Beauchamp and Childress. This is a JPND-funded work to summarize findings of the project NEEDSinALS (www.NEEDSinALS.com) which highlights subjective perspectives and preferences in medical decision making in ALS.
Collapse
Affiliation(s)
- Dorothée Lulé
- Department of Neurology, University of Ulm, Ulm, Germany
| | - Andrea Kübler
- Interventional Psychology, Psychology III, University of Würzburg, Würzburg, Germany
| | | |
Collapse
|
102
|
Krauth R, Schwertner J, Vogt S, Lindquist S, Sailer M, Sickert A, Lamprecht J, Perdikis S, Corbet T, Millán JDR, Hinrichs H, Heinze HJ, Sweeney-Reed CM. Cortico-Muscular Coherence Is Reduced Acutely Post-stroke and Increases Bilaterally During Motor Recovery: A Pilot Study. Front Neurol 2019; 10:126. [PMID: 30842752 PMCID: PMC6391349 DOI: 10.3389/fneur.2019.00126] [Citation(s) in RCA: 31] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2018] [Accepted: 01/30/2019] [Indexed: 12/11/2022] Open
Abstract
Motor recovery following stroke is believed to necessitate alteration in functional connectivity between cortex and muscle. Cortico-muscular coherence has been proposed as a potential biomarker for post-stroke motor deficits, enabling a quantification of recovery, as well as potentially indicating the regions of cortex involved in recovery of function. We recorded simultaneous EEG and EMG during wrist extension from healthy participants and patients following ischaemic stroke, evaluating function at three time points post-stroke. EEG–EMG coherence increased over time, as wrist mobility recovered clinically, and by the final evaluation, coherence was higher in the patient group than in the healthy controls. Moreover, the cortical distribution differed between the groups, with coherence involving larger and more bilaterally scattered areas of cortex in the patients than in the healthy participants. The findings suggest that EEG–EMG coherence has the potential to serve as a biomarker for motor recovery and to provide information about the cortical regions that should be targeted in rehabilitation therapies based on real-time EEG.
Collapse
Affiliation(s)
- Richard Krauth
- Neurocybernetics and Rehabilitation, Department of Neurology and Stereotactic Neurosurgery, Otto-von-Guericke University, Magdeburg, Germany
| | - Johanna Schwertner
- Neurocybernetics and Rehabilitation, Department of Neurology and Stereotactic Neurosurgery, Otto-von-Guericke University, Magdeburg, Germany
| | - Susanne Vogt
- Department of Neurology, Otto-von-Guericke University, Magdeburg, Germany
| | | | - Michael Sailer
- MEDIAN Klinik, Neurological Rehabilitation Center, Magdeburg, Germany.,Institute for Neurorehabilitation, Affiliated Institute of the Otto-von-Guericke University, Magdeburg, Germany
| | - Almut Sickert
- MEDIAN Klinik, Neurological Rehabilitation Center, Magdeburg, Germany
| | - Juliane Lamprecht
- Institute for Neurorehabilitation, Affiliated Institute of the Otto-von-Guericke University, Magdeburg, Germany
| | - Serafeim Perdikis
- Defitech Chair in Brain-Machine Interface, Ecole Polytechnique Fédérale de Lausanne, Geneva, Switzerland.,Brain-Computer Interfaces and Neural Engineering Laboratory, School of Computer Science and Electronic Engineering, University of Essex, Colchester, United Kingdom
| | - Tiffany Corbet
- Defitech Chair in Brain-Machine Interface, Ecole Polytechnique Fédérale de Lausanne, Geneva, Switzerland
| | - José Del R Millán
- Defitech Chair in Brain-Machine Interface, Ecole Polytechnique Fédérale de Lausanne, Geneva, Switzerland
| | - Hermann Hinrichs
- Department of Neurology, Otto-von-Guericke University, Magdeburg, Germany.,Department of Behavioral Neurology, Leibniz Institute for Neurobiology, Magdeburg, Germany.,German Centre for Neurodegenerative Diseases, Magdeburg, Germany
| | - Hans-Jochen Heinze
- Department of Neurology, Otto-von-Guericke University, Magdeburg, Germany.,Department of Behavioral Neurology, Leibniz Institute for Neurobiology, Magdeburg, Germany.,German Centre for Neurodegenerative Diseases, Magdeburg, Germany
| | - Catherine M Sweeney-Reed
- Neurocybernetics and Rehabilitation, Department of Neurology and Stereotactic Neurosurgery, Otto-von-Guericke University, Magdeburg, Germany
| |
Collapse
|
103
|
Oken B, Memmott T, Eddy B, Wiedrick J, Fried-Oken M. Vigilance state fluctuations and performance using brain-computer interface for communication. BRAIN-COMPUTER INTERFACES 2019; 5:146-156. [PMID: 31236425 DOI: 10.1080/2326263x.2019.1571356] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
The effect of fatigue and drowsiness on brain-computer interface (BCI) performance was evaluated. 20 healthy participants performed a standardized 11-minute calibration of a Rapid Serial Visual Presentation BCI system five times over two hours. For each calibration, BCI performance was evaluated using area under the receiver operating characteristic curve (AUC). Self-rated measures were obtained following each calibration including the Karolinska Sleepiness Scale and a standardized boredom scale. Physiological measures were obtained during each calibration including P300 amplitude, theta power, alpha power, median power frequency and eye-blink rate. There was a significant decrease in AUC over the five sessions. This was paralleled by increases in self-rated sleepiness and boredom and decreases in P300 amplitude. Alpha power, median power frequency, and eye-blink rate also increased but more modestly. AUC changes were only partly explained by changes in P300 amplitude. There was a decrease in BCI performance over time that related to increases in sleepiness and boredom. This worsened performance was only partly explained by decreases in P300 amplitude. Thus, drowsiness and boredom have a negative impact on BCI performance. Increased BCI performance may be possible by developing physiological measures to provide feedback to the user or to adapt the classifier to state.
Collapse
Affiliation(s)
- Barry Oken
- Department of Neurology, Oregon Health & Science University, 3181 SW Sam Jackson Park Road, Portland, OR, 97239 USA
| | - Tab Memmott
- Department of Neurology, Oregon Health & Science University, 3181 SW Sam Jackson Park Road, Portland, OR, 97239 USA
| | - Brandon Eddy
- Institute on Development and Disability, Oregon Health & Science University, 3181 SW Sam Jackson Park Road, Portland, OR, 97239 USA
| | - Jack Wiedrick
- Biostatistics and Design Program, Oregon Health & Science University, 3181 SW Sam Jackson Park Road, Portland, OR, 97239 USA
| | - Melanie Fried-Oken
- Institute on Development and Disability, Oregon Health & Science University, 3181 SW Sam Jackson Park Road, Portland, OR, 97239 USA
| |
Collapse
|
104
|
|
105
|
Sannelli C, Vidaurre C, Müller KR, Blankertz B. A large scale screening study with a SMR-based BCI: Categorization of BCI users and differences in their SMR activity. PLoS One 2019; 14:e0207351. [PMID: 30682025 PMCID: PMC6347432 DOI: 10.1371/journal.pone.0207351] [Citation(s) in RCA: 45] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2018] [Accepted: 10/30/2018] [Indexed: 11/18/2022] Open
Abstract
Brain-Computer Interfaces (BCIs) are inefficient for a non-negligible part of the population, estimated around 25%. To understand this phenomenon in Sensorimotor Rhythm (SMR) based BCIs, data from a large-scale screening study conducted on 80 novice participants with the Berlin BCI system and its standard machine-learning approach were investigated. Each participant performed one BCI session with resting state Encephalography, Motor Observation, Motor Execution and Motor Imagery recordings and 128 electrodes. A significant portion of the participants (40%) could not achieve BCI control (feedback performance > 70%). Based on the performance of the calibration and feedback runs, BCI users were stratified in three groups. Analyses directed to detect and elucidate the differences in the SMR activity of these groups were performed. Statistics on reactive frequencies, task prevalence and classification results are reported. Based on their SMR activity, also a systematic list of potential reasons leading to performance drops and thus hints for possible improvements of BCI experimental design are given. The categorization of BCI users has several advantages, allowing researchers 1) to select subjects for further analyses as well as for testing new BCI paradigms or algorithms, 2) to adopt a better subject-dependent training strategy and 3) easier comparisons between different studies.
Collapse
Affiliation(s)
- Claudia Sannelli
- Department of Neurotechnology, Technische Universität Berlin, Berlin, Germany
| | - Carmen Vidaurre
- Department of Machine Learning, Technische Universität Berlin, Berlin, Germany
- Department of Mathematics, Public University of Navarre, Pamplona, Spain
| | - Klaus-Robert Müller
- Department of Machine Learning, Technische Universität Berlin, Berlin, Germany
- Department of Brain and Cognitive Engineering, Korea University, Seoul, South Korea
- Max Planck Institute for Informatics, Saarbrücken, Germany
| | - Benjamin Blankertz
- Department of Neurotechnology, Technische Universität Berlin, Berlin, Germany
| |
Collapse
|
106
|
Zou Y, Zhao X, Chu Y, Zhao Y, Xu W, Han J. An inter-subject model to reduce the calibration time for motion imagination-based brain-computer interface. Med Biol Eng Comput 2018; 57:939-952. [PMID: 30498878 DOI: 10.1007/s11517-018-1917-x] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2017] [Accepted: 10/19/2018] [Indexed: 11/28/2022]
Abstract
A major factor blocking the practical application of brain-computer interfaces (BCI) is the long calibration time. To obtain enough training trials, participants must spend a long time in the calibration stage. In this paper, we propose a new framework to reduce the calibration time through knowledge transferred from the electroencephalogram (EEG) of other subjects. We trained the motor recognition model for the target subject using both the target's EEG signal and the EEG signals of other subjects. To reduce the individual variation of different datasets, we proposed two data mapping methods. These two methods separately diminished the variation caused by dissimilarities in the brain activation region and the strength of the brain activation in different subjects. After these data mapping stages, we adopted an ensemble method to aggregate the EEG signals from all subjects into a final model. We compared our method with other methods that reduce the calibration time. The results showed that our method achieves a satisfactory recognition accuracy using very few training trials (32 samples). Compared with existing methods using few training trials, our method achieved much greater accuracy. Graphical abstract The framework of the proposed method. The workflow of the framework have three steps: 1, process each subjects EEG signals according to the target subject's EEG signal. 2, generate models from each subjects' processed signals. 3, ensemble these models to a final model, the final model is a model for the target subject.
Collapse
Affiliation(s)
- Yijun Zou
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Xingang Zhao
- Key Laboratory of Networked Control System, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, 110016, China.
| | - Yaqi Chu
- University of Chinese Academy of Sciences, Beijing, 100049, China.,Key Laboratory of Networked Control System, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, 110016, China
| | - Yiwen Zhao
- Key Laboratory of Networked Control System, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, 110016, China
| | - Weiliang Xu
- Department of Mechanical Engineering, University of Auckland, Auckland, New Zealand
| | - Jianda Han
- Key Laboratory of Networked Control System, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, 110016, China
| |
Collapse
|
107
|
Wierzgała P, Zapała D, Wojcik GM, Masiak J. Most Popular Signal Processing Methods in Motor-Imagery BCI: A Review and Meta-Analysis. Front Neuroinform 2018; 12:78. [PMID: 30459588 PMCID: PMC6232268 DOI: 10.3389/fninf.2018.00078] [Citation(s) in RCA: 34] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2018] [Accepted: 10/17/2018] [Indexed: 11/21/2022] Open
Abstract
Brain-Computer Interfaces (BCI) constitute an alternative channel of communication between humans and environment. There are a number of different technologies which enable the recording of brain activity. One of these is electroencephalography (EEG). The most common EEG methods include interfaces whose operation is based on changes in the activity of Sensorimotor Rhythms (SMR) during imagery movement, so-called Motor Imagery BCI (MIBCI).The present article is a review of 131 articles published from 1997 to 2017 discussing various procedures of data processing in MIBCI. The experiments described in these publications have been compared in terms of the methods used for data registration and analysis. Some of the studies (76 reports) were subjected to meta-analysis which showed corrected average classification accuracy achieved in these studies at the level of 51.96%, a high degree of heterogeneity of results (Q = 1806577.61; df = 486; p < 0.001; I2 = 99.97%), as well as significant effects of number of channels, number of mental images, and method of spatial filtering. On the other hand the meta-regression failed to provide evidence that there was an increase in the effectiveness of the solutions proposed in the articles published in recent years. The authors have proposed a newly developed standard for presenting results acquired during MIBCI experiments, which is designed to facilitate communication and comparison of essential information regarding the effects observed. Also, based on the findings of descriptive analysis and meta-analysis, the authors formulated recommendations regarding practices applied in research on signal processing in MIBCIs.
Collapse
Affiliation(s)
- Piotr Wierzgała
- Department of Neuroinformatics, Faculty of Mathematics, Physics and Computer Science, Institute of Computer Science Maria Curie-Sklodowska University, Lublin, Poland
| | - Dariusz Zapała
- Department of Experimental Psychology The John Paul II Catholic University of Lublin, Lublin, Poland
| | - Grzegorz M Wojcik
- Department of Neuroinformatics, Faculty of Mathematics, Physics and Computer Science, Institute of Computer Science Maria Curie-Sklodowska University, Lublin, Poland
| | - Jolanta Masiak
- Neurophysiological Independent Unit of the Department of Psychiatry Medical University of Lublin, Lublin, Poland
| |
Collapse
|
108
|
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
|
109
|
Robinson N, Thomas KP, Vinod AP. Neurophysiological predictors and spectro-spatial discriminative features for enhancing SMR-BCI. J Neural Eng 2018; 15:066032. [PMID: 30277219 DOI: 10.1088/1741-2552/aae597] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
Neural engineering research is actively engaged in optimizing the robustness of sensorimotor rhythms (SMR)-brain-computer interface (BCI) to boost its potential real-world use. OBJECTIVE This paper investigates two vital factors in efficient and robust SMR-BCI design-algorithms that address subject-variability of optimal features and neurophysiological factors that correlate with BCI performance. Existing SMR-BCI research using electroencephalogram (EEG) to classify bilateral motor imagery (MI) focus on identifying subject-specific frequency bands with most discriminative motor patterns localized to sensorimotor region. APPROACH A novel strategy to further optimize BCI performance by taking into account the variability of discriminative spectral regions across various EEG channels is proposed in this paper. MAIN RESULTS The proposed technique results in a significant ([Formula: see text]) increase in average ([Formula: see text]) classification accuracy by [Formula: see text] accompanied by a considerable reduction in number of channels and bands. The session-to-session transfer variation in spectro-spatial patterns using proposed algorithm is investigated offline and classification performance of the optimized BCI model is successfully evaluated in an online SMR-BCI. Further, the effective prediction of SMR-BCI performance with physiological indicators derived from multi-channel resting-state EEG is demonstrated. The results indicate that the resting state activation patterns such as entropy and gamma power from pre-motor (fronto-central) and posterior (parietal and centro-parietal) areas, and beta power from posterior (centro-parietal) areas estimate BCI performance with minimum error. These patterns, strongly related to BCI performance, may represent certain cognitive states during rest. SIGNIFICANCE Findings reported in this paper imply the need for subject-specific modelling of BCI and the prediction of BCI performance using multi-channel rest-state parameters, to ensure enhanced BCI performance.
Collapse
Affiliation(s)
- Neethu Robinson
- School of Computer Science and Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore 639798, Singapore
| | | | | |
Collapse
|
110
|
Lindgren JT, Merlini A, Lecuyer A, Andriulli FP. simBCI - A framework for studying BCI methods by simulated EEG. IEEE Trans Neural Syst Rehabil Eng 2018; 26:2096-2105. [PMID: 30281468 DOI: 10.1109/tnsre.2018.2873061] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/28/2024]
Abstract
Brain-Computer Interface (BCI) methods are commonly studied using Electroencephalogram (EEG) data recorded from human experiments. For understanding and developing BCI signal processing techniques real data is costly to obtain and its composition is apriori unknown. The brain mechanisms generating the EEG are not directly observable and their states cannot be uniquely identified from the EEG. Subsequently, we do not have generative ground truth for real data. In this paper we propose a novel convenience framework called simBCI to alleviate testing and studying BCI signal processing methods in simulated, controlled conditions. The framework can be used to generate artificial BCI data and to test classification pipelines with such data. Models and parameters on both data generation and the signal processing side can be iterated over to examine the interplay of different combinations. The framework provides the first time open source implementations of several models and methods. We invite researchers to insert more advanced models. The proposed system does not intend to replace human experiments. Instead, it can be used to discover hypotheses, study algorithms, to educate about BCI, and to debug signal processing pipelines of other BCI systems. The proposed framework is modular, extensible and freely available as open source1. It currently requires Matlab.
Collapse
|
111
|
McClay W. A Magnetoencephalographic/Encephalographic (MEG/EEG) Brain-Computer Interface Driver for Interactive iOS Mobile Videogame Applications Utilizing the Hadoop Ecosystem, MongoDB, and Cassandra NoSQL Databases. Diseases 2018; 6:diseases6040089. [PMID: 30274210 PMCID: PMC6313514 DOI: 10.3390/diseases6040089] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2018] [Revised: 08/08/2018] [Accepted: 08/21/2018] [Indexed: 11/17/2022] Open
Abstract
In Phase I, we collected data on five subjects yielding over 90% positive performance in Magnetoencephalographic (MEG) mid-and post-movement activity. In addition, a driver was developed that substituted the actions of the Brain Computer Interface (BCI) as mouse button presses for real-time use in visual simulations. The process was interfaced to a flight visualization demonstration utilizing left or right brainwave thought movement, the user experiences, the aircraft turning in the chosen direction, or on iOS Mobile Warfighter Videogame application. The BCI’s data analytics of a subject’s MEG brain waves and flight visualization performance videogame analytics were stored and analyzed using the Hadoop Ecosystem as a quick retrieval data warehouse. In Phase II portion of the project involves the Emotiv Encephalographic (EEG) Wireless Brain–Computer interfaces (BCIs) allow for people to establish a novel communication channel between the human brain and a machine, in this case, an iOS Mobile Application(s). The EEG BCI utilizes advanced and novel machine learning algorithms, as well as the Spark Directed Acyclic Graph (DAG), Cassandra NoSQL database environment, and also the competitor NoSQL MongoDB database for housing BCI analytics of subject’s response and users’ intent illustrated for both MEG/EEG brainwave signal acquisition. The wireless EEG signals that were acquired from the OpenVibe and the Emotiv EPOC headset can be connected via Bluetooth to an iPhone utilizing a thin Client architecture. The use of NoSQL databases were chosen because of its schema-less architecture and Map Reduce computational paradigm algorithm for housing a user’s brain signals from each referencing sensor. Thus, in the near future, if multiple users are playing on an online network connection and an MEG/EEG sensor fails, or if the connection is lost from the smartphone and the webserver due to low battery power or failed data transmission, it will not nullify the NoSQL document-oriented (MongoDB) or column-oriented Cassandra databases. Additionally, NoSQL databases have fast querying and indexing methodologies, which are perfect for online game analytics and technology. In Phase II, we collected data on five MEG subjects, yielding over 90% positive performance on iOS Mobile Applications with Objective-C and C++, however on EEG signals utilized on three subjects with the Emotiv wireless headsets and (n < 10) subjects from the OpenVibe EEG database the Variational Bayesian Factor Analysis Algorithm (VBFA) yielded below 60% performance and we are currently pursuing extending the VBFA algorithm to work in the time-frequency domain referred to as VBFA-TF to enhance EEG performance in the near future. The novel usage of NoSQL databases, Cassandra and MongoDB, were the primary main enhancements of the BCI Phase II MEG/EEG brain signal data acquisition, queries, and rapid analytics, with MapReduce and Spark DAG demonstrating future implications for next generation biometric MEG/EEG NoSQL databases.
Collapse
Affiliation(s)
- Wilbert McClay
- Information Systems Department, Northeastern University, Boston, MA 02115, USA.
- Department School of Social Work, Tulane University School of Medicine, New Orleans, LA 70112, USA.
- Department of Information Assurance, Northeastern University, Boston, MA 02115, USA.
- Lawrence Livermore National Laboratory, Livermore, CA 94550, USA.
- Department of Information Assurance, Brandeis University, Waltham, MA 02453, USA.
- Department of Mathematics, Brandeis University, Waltham, MA 02453, USA.
| |
Collapse
|
112
|
Jiang Y, Hau NT, Chung WY. Semiasynchronous BCI Using Wearable Two-Channel EEG. IEEE Trans Cogn Dev Syst 2018. [DOI: 10.1109/tcds.2017.2716973] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
|
113
|
User Evaluation of the Neurodildo: A Mind-Controlled Sex Toy for People with Disabilities and an Exploration of Its Applications to Sex Robots. ROBOTICS 2018. [DOI: 10.3390/robotics7030046] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
In this paper, we present the Neurodildo, a sex toy remotely controlled by brain waves, which is pressure sensitive and has electrical stimulation (e-stim) feedback. The Neurodildo was originally presented as a conference paper at the 3rd International Congress on Love and Sex with Robots (2017). We designed and explored the application of a mind-controlled sex toy for the people with mobility disabilities, for example with spinal cord injury (SCI), who have difficulty handling a commercial toy and that might experience difficulties in a sexual encounter. The system consists of the sex toy with Bluetooth and sensors, the brain-computer interface (BCI) headset, the e-stim device, and a computer for running the necessary software. The first user wears the headset and the e-stim device, and by focusing in trained patterns, he/she can control the vibration of the sex toy. The pressure applied to the sex toy by the second user is measured by sensors and transmitted and converted to the first user, who feels muscle contractions. We discuss the design process, the limitations of the prototype and how evaluating the user requirements is necessary for a better product. We also included a background and discussion on the application of sex robots for assisting disabled people and how the Neurodildo could be integrated with this futuristic technology. The goal of this project is to design a sex toy that might help people with disabilities and people in long-distance relationships (LDR), trying to fill the gap of sex toys designed for people with disabilities.
Collapse
|
114
|
Tariq M, Trivailo PM, Simic M. EEG-Based BCI Control Schemes for Lower-Limb Assistive-Robots. Front Hum Neurosci 2018; 12:312. [PMID: 30127730 PMCID: PMC6088276 DOI: 10.3389/fnhum.2018.00312] [Citation(s) in RCA: 88] [Impact Index Per Article: 14.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2018] [Accepted: 07/16/2018] [Indexed: 12/14/2022] Open
Abstract
Over recent years, brain-computer interface (BCI) has emerged as an alternative communication system between the human brain and an output device. Deciphered intents, after detecting electrical signals from the human scalp, are translated into control commands used to operate external devices, computer displays and virtual objects in the real-time. BCI provides an augmentative communication by creating a muscle-free channel between the brain and the output devices, primarily for subjects having neuromotor disorders, or trauma to nervous system, notably spinal cord injuries (SCI), and subjects with unaffected sensorimotor functions but disarticulated or amputated residual limbs. This review identifies the potentials of electroencephalography (EEG) based BCI applications for locomotion and mobility rehabilitation. Patients could benefit from its advancements such as wearable lower-limb (LL) exoskeletons, orthosis, prosthesis, wheelchairs, and assistive-robot devices. The EEG communication signals employed by the aforementioned applications that also provide feasibility for future development in the field are sensorimotor rhythms (SMR), event-related potentials (ERP) and visual evoked potentials (VEP). The review is an effort to progress the development of user's mental task related to LL for BCI reliability and confidence measures. As a novel contribution, the reviewed BCI control paradigms for wearable LL and assistive-robots are presented by a general control framework fitting in hierarchical layers. It reflects informatic interactions, between the user, the BCI operator, the shared controller, the robotic device and the environment. Each sub layer of the BCI operator is discussed in detail, highlighting the feature extraction, classification and execution methods employed by the various systems. All applications' key features and their interaction with the environment are reviewed for the EEG-based activity mode recognition, and presented in form of a table. It is suggested to structure EEG-BCI controlled LL assistive devices within the presented framework, for future generation of intent-based multifunctional controllers. Despite the development of controllers, for BCI-based wearable or assistive devices that can seamlessly integrate user intent, practical challenges associated with such systems exist and have been discerned, which can be constructive for future developments in the field.
Collapse
Affiliation(s)
| | | | - Milan Simic
- School of Engineering, RMIT University Melbourne, Melbourne, VIC, Australia
| |
Collapse
|
115
|
Lawhern VJ, Solon AJ, Waytowich NR, Gordon SM, Hung CP, Lance BJ. EEGNet: a compact convolutional neural network for EEG-based brain–computer interfaces. J Neural Eng 2018; 15:056013. [DOI: 10.1088/1741-2552/aace8c] [Citation(s) in RCA: 814] [Impact Index Per Article: 135.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
|
116
|
Biasiucci A, Leeb R, Iturrate I, Perdikis S, Al-Khodairy A, Corbet T, Schnider A, Schmidlin T, Zhang H, Bassolino M, Viceic D, Vuadens P, Guggisberg AG, Millán JDR. Brain-actuated functional electrical stimulation elicits lasting arm motor recovery after stroke. Nat Commun 2018; 9:2421. [PMID: 29925890 PMCID: PMC6010454 DOI: 10.1038/s41467-018-04673-z] [Citation(s) in RCA: 247] [Impact Index Per Article: 41.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2017] [Accepted: 05/09/2018] [Indexed: 12/29/2022] Open
Abstract
Brain-computer interfaces (BCI) are used in stroke rehabilitation to translate brain signals into intended movements of the paralyzed limb. However, the efficacy and mechanisms of BCI-based therapies remain unclear. Here we show that BCI coupled to functional electrical stimulation (FES) elicits significant, clinically relevant, and lasting motor recovery in chronic stroke survivors more effectively than sham FES. Such recovery is associated to quantitative signatures of functional neuroplasticity. BCI patients exhibit a significant functional recovery after the intervention, which remains 6-12 months after the end of therapy. Electroencephalography analysis pinpoints significant differences in favor of the BCI group, mainly consisting in an increase in functional connectivity between motor areas in the affected hemisphere. This increase is significantly correlated with functional improvement. Results illustrate how a BCI-FES therapy can drive significant functional recovery and purposeful plasticity thanks to contingent activation of body natural efferent and afferent pathways.
Collapse
Affiliation(s)
- A Biasiucci
- Defitech Foundation Chair in Brain-Machine Interface, Center for Neuroprosthetics and Institute of Bioengineering, École Polytechnique Fédérale de Lausanne, Geneva, 1202, Switzerland
| | - R Leeb
- Defitech Foundation Chair in Brain-Machine Interface, Center for Neuroprosthetics and Institute of Bioengineering, École Polytechnique Fédérale de Lausanne, Geneva, 1202, Switzerland.,Center for Neuroprosthetics, École Polytechnique Fédérale de Lausanne, Sion, 1951, Switzerland
| | - I Iturrate
- Defitech Foundation Chair in Brain-Machine Interface, Center for Neuroprosthetics and Institute of Bioengineering, École Polytechnique Fédérale de Lausanne, Geneva, 1202, Switzerland
| | - S Perdikis
- Defitech Foundation Chair in Brain-Machine Interface, Center for Neuroprosthetics and Institute of Bioengineering, École Polytechnique Fédérale de Lausanne, Geneva, 1202, Switzerland.,Wyss Center for Bio and Neuroengineering, Geneva, 1202, Switzerland
| | - A Al-Khodairy
- SUVACare - Clinique Romande de Réadaptation, Sion, 1951, Switzerland
| | - T Corbet
- Defitech Foundation Chair in Brain-Machine Interface, Center for Neuroprosthetics and Institute of Bioengineering, École Polytechnique Fédérale de Lausanne, Geneva, 1202, Switzerland
| | - A Schnider
- Division of Neurorehabilitation, Department of Clinical Neurosciences, University Hospital of Geneva, Geneva, 1211, Switzerland
| | - T Schmidlin
- Center for Neuroprosthetics, École Polytechnique Fédérale de Lausanne, Sion, 1951, Switzerland
| | - H Zhang
- Defitech Foundation Chair in Brain-Machine Interface, Center for Neuroprosthetics and Institute of Bioengineering, École Polytechnique Fédérale de Lausanne, Geneva, 1202, Switzerland
| | - M Bassolino
- Center for Neuroprosthetics, École Polytechnique Fédérale de Lausanne, Sion, 1951, Switzerland
| | - D Viceic
- Center for Neuroprosthetics, École Polytechnique Fédérale de Lausanne, Sion, 1951, Switzerland
| | - P Vuadens
- SUVACare - Clinique Romande de Réadaptation, Sion, 1951, Switzerland
| | - A G Guggisberg
- Division of Neurorehabilitation, Department of Clinical Neurosciences, University Hospital of Geneva, Geneva, 1211, Switzerland
| | - J D R Millán
- Defitech Foundation Chair in Brain-Machine Interface, Center for Neuroprosthetics and Institute of Bioengineering, École Polytechnique Fédérale de Lausanne, Geneva, 1202, Switzerland.
| |
Collapse
|
117
|
Omedes J, Schwarz A, Müller-Putz GR, Montesano L. Factors that affect error potentials during a grasping task: toward a hybrid natural movement decoding BCI. J Neural Eng 2018; 15:046023. [DOI: 10.1088/1741-2552/aac1a1] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
|
118
|
Riccio A, Schettini F, Simione L, Pizzimenti A, Inghilleri M, Olivetti-Belardinelli M, Mattia D, Cincotti F. On the Relationship Between Attention Processing and P300-Based Brain Computer Interface Control in Amyotrophic Lateral Sclerosis. Front Hum Neurosci 2018; 12:165. [PMID: 29892218 PMCID: PMC5985322 DOI: 10.3389/fnhum.2018.00165] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2017] [Accepted: 04/09/2018] [Indexed: 12/13/2022] Open
Abstract
Our objective was to investigate the capacity to control a P3-based brain-computer interface (BCI) device for communication and its related (temporal) attention processing in a sample of amyotrophic lateral sclerosis (ALS) patients with respect to healthy subjects. The ultimate goal was to corroborate the role of cognitive mechanisms in event-related potential (ERP)-based BCI control in ALS patients. Furthermore, the possible differences in such attentional mechanisms between the two groups were investigated in order to unveil possible alterations associated with the ALS condition. Thirteen ALS patients and 13 healthy volunteers matched for age and years of education underwent a P3-speller BCI task and a rapid serial visual presentation (RSVP) task. The RSVP task was performed by participants in order to screen their temporal pattern of attentional resource allocation, namely: (i) the temporal attentional filtering capacity (scored as T1%); and (ii) the capability to adequately update the attentive filter in the temporal dynamics of the attentional selection (scored as T2%). For the P3-speller BCI task, the online accuracy and information transfer rate (ITR) were obtained. Centroid Latency and Mean Amplitude of N200 and P300 were also obtained. No significant differences emerged between ALS patients and Controls with regards to online accuracy (p = 0.13). Differently, the performance in controlling the P3-speller expressed as ITR values (calculated offline) were compromised in ALS patients (p < 0.05), with a delay in the latency of P3 when processing BCI stimuli as compared with Control group (p < 0.01). Furthermore, the temporal aspect of attentional filtering which was related to BCI control (r = 0.51; p < 0.05) and to the P3 wave amplitude (r = 0.63; p < 0.05) was also altered in ALS patients (p = 0.01). These findings ground the knowledge required to develop sensible classes of BCI specifically designed by taking into account the influence of the cognitive characteristics of the possible candidates in need of a BCI system for communication.
Collapse
Affiliation(s)
- Angela Riccio
- Neuroelectrical Imaging and BCI Laboratory, NeiLab, Fondazione Santa Lucia (IRCCS), Rome, Italy
| | - Francesca Schettini
- Servizio Ausilioteca per Riabilitazione Assistita con Tecnologia (SARA-t), Fondazione Santa Lucia (IRCCS), Rome, Italy
| | - Luca Simione
- Institute of Cognitive Sciences and Technologies, Consiglio Nazionale delle Ricerche (CNR), Rome, Italy
| | | | - Maurizio Inghilleri
- Department of Neurology and Psychiatry, Sapienza University of Rome, Rome, Italy
| | - Marta Olivetti-Belardinelli
- Centro Interuniversitario di Ricerca sull'Elaborazione Cognitiva in Sistemi Naturali e Artificiali (ECoNA), Rome, Italy.,ECONA Interuniversity Centre for Reseach on Natural and Artificial Systems, Sapienza University of Rome, Rome, Italy
| | - Donatella Mattia
- Neuroelectrical Imaging and BCI Laboratory, NeiLab, Fondazione Santa Lucia (IRCCS), Rome, Italy
| | - Febo Cincotti
- Neuroelectrical Imaging and BCI Laboratory, NeiLab, Fondazione Santa Lucia (IRCCS), Rome, Italy.,Department of Computer, Control and Management Engineering Antonio Ruberti, Sapienza University of Rome, Rome, Italy
| |
Collapse
|
119
|
Hubner D, Verhoeven T, Muller KR, Kindermans PJ, Tangermann M. Unsupervised Learning for Brain-Computer Interfaces Based on Event-Related Potentials: Review and Online Comparison [Research Frontier]. IEEE COMPUT INTELL M 2018. [DOI: 10.1109/mci.2018.2807039] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
|
120
|
Lopes Dias C, Sburlea AI, Müller-Putz GR. Masked and unmasked error-related potentials during continuous control and feedback. J Neural Eng 2018; 15:036031. [PMID: 29557346 DOI: 10.1088/1741-2552/aab806] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
The detection of error-related potentials (ErrPs) in tasks with discrete feedback is well established in the brain-computer interface (BCI) field. However, the decoding of ErrPs in tasks with continuous feedback is still in its early stages. OBJECTIVE We developed a task in which subjects have continuous control of a cursor's position by means of a joystick. The cursor's position was shown to the participants in two different modalities of continuous feedback: normal and jittered. The jittered feedback was created to mimic the instability that could exist if participants controlled the trajectory directly with brain signals. APPROACH This paper studies the electroencephalographic (EEG)-measurable signatures caused by a loss of control over the cursor's trajectory, causing a target miss. MAIN RESULTS In both feedback modalities, time-locked potentials revealed the typical frontal-central components of error-related potentials. Errors occurring during the jittered feedback (masked errors) were delayed in comparison to errors occurring during normal feedback (unmasked errors). Masked errors displayed lower peak amplitudes than unmasked errors. Time-locked classification analysis allowed a good distinction between correct and error classes (average Cohen-[Formula: see text], average TPR = 81.8% and average TNR = 96.4%). Time-locked classification analysis between masked error and unmasked error classes revealed results at chance level (average Cohen-[Formula: see text], average TPR = 60.9% and average TNR = 58.3%). Afterwards, we performed asynchronous detection of ErrPs, combining both masked and unmasked trials. The asynchronous detection of ErrPs in a simulated online scenario resulted in an average TNR of 84.0% and in an average TPR of 64.9%. SIGNIFICANCE The time-locked classification results suggest that the masked and unmasked errors were indistinguishable in terms of classification. The asynchronous classification results suggest that the feedback modality did not hinder the asynchronous detection of ErrPs.
Collapse
Affiliation(s)
- Catarina Lopes Dias
- Institute of Neural Engineering, Graz University of Technology, Graz, Austria
| | | | | |
Collapse
|
121
|
Coogan CG, He B. Brain-computer interface control in a virtual reality environment and applications for the internet of things. IEEE ACCESS : PRACTICAL INNOVATIONS, OPEN SOLUTIONS 2018; 6:10840-10849. [PMID: 30271700 PMCID: PMC6157750 DOI: 10.1109/access.2018.2809453] [Citation(s) in RCA: 49] [Impact Index Per Article: 8.2] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/28/2023]
Abstract
Brain-computer interfaces (BCIs) have enabled individuals to control devices such as spellers, robotic arms, drones, and wheelchairs, but often these BCI applications are restricted to research laboratories. With the advent of virtual reality (VR) systems and the internet of things (IoT) we can couple these technologies to offer real-time control of a user's virtual and physical environment. Likewise, BCI applications are often single-use with user's having no control outside of the restrictions placed upon the applications at the time of creation. Therefore, there is a need to create a tool that allows users the flexibility to create and modularize aspects of BCI applications for control of IoT devices and VR environments. Using a popular video game engine, Unity, and coupling it with BCI2000, we can create diverse applications that give the end-user additional autonomy during the task at hand. We demonstrate the validity of controlling a Unity-based VR environment and several commercial IoT devices via direct neural interfacing processed through BCI2000.
Collapse
Affiliation(s)
- Christopher G. Coogan
- Department of Biomedical Engineering, University of Minnesota, Minneapolis, MN 55106 USA
| | - Bin He
- Department of Biomedical Engineering, University of Minnesota, Minneapolis, MN 55106 USA
- Department of Biomedical Engineering, Carnegie Mellon University, Pittsburgh, PA 15213 USA
| |
Collapse
|
122
|
Ahn M, Cho H, Ahn S, Jun SC. User's Self-Prediction of Performance in Motor Imagery Brain-Computer Interface. Front Hum Neurosci 2018; 12:59. [PMID: 29497370 PMCID: PMC5818431 DOI: 10.3389/fnhum.2018.00059] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2017] [Accepted: 01/31/2018] [Indexed: 11/28/2022] Open
Abstract
Performance variation is a critical issue in motor imagery brain–computer interface (MI-BCI), and various neurophysiological, psychological, and anatomical correlates have been reported in the literature. Although the main aim of such studies is to predict MI-BCI performance for the prescreening of poor performers, studies which focus on the user’s sense of the motor imagery process and directly estimate MI-BCI performance through the user’s self-prediction are lacking. In this study, we first test each user’s self-prediction idea regarding motor imagery experimental datasets. Fifty-two subjects participated in a classical, two-class motor imagery experiment and were asked to evaluate their easiness with motor imagery and to predict their own MI-BCI performance. During the motor imagery experiment, an electroencephalogram (EEG) was recorded; however, no feedback on motor imagery was given to subjects. From EEG recordings, the offline classification accuracy was estimated and compared with several questionnaire scores of subjects, as well as with each subject’s self-prediction of MI-BCI performance. The subjects’ performance predictions during motor imagery task showed a high positive correlation (r = 0.64, p < 0.01). Interestingly, it was observed that the self-prediction became more accurate as the subjects conducted more motor imagery tasks in the Correlation coefficient (pre-task to 2nd run: r = 0.02 to r = 0.54, p < 0.01) and root mean square error (pre-task to 3rd run: 17.7% to 10%, p < 0.01). We demonstrated that subjects may accurately predict their MI-BCI performance even without feedback information. This implies that the human brain is an active learning system and, by self-experiencing the endogenous motor imagery process, it can sense and adopt the quality of the process. Thus, it is believed that users may be able to predict MI-BCI performance and results may contribute to a better understanding of low performance and advancing BCI.
Collapse
Affiliation(s)
- Minkyu Ahn
- School of Computer Science and Electrical Engineering, Handong Global University, Pohang, South Korea
| | - Hohyun Cho
- Wadsworth Center, New York State Department of Health, Albany, NY, United States
| | - Sangtae Ahn
- Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | - Sung C Jun
- School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology, Gwangju, South Korea
| |
Collapse
|
123
|
Kucukyilmaz A, Demiris Y. Learning Shared Control by Demonstration for Personalized Wheelchair Assistance. IEEE TRANSACTIONS ON HAPTICS 2018; 11:431-442. [PMID: 29994370 DOI: 10.1109/toh.2018.2804911] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
An emerging research problem in assistive robotics is the design of methodologies that allow robots to provide personalized assistance to users. For this purpose, we present a method to learn shared control policies from demonstrations offered by a human assistant. We train a Gaussian process (GP) regression model to continuously regulate the level of assistance between the user and the robot, given the user's previous and current actions and the state of the environment. The assistance policy is learned after only a single human demonstration, i.e. in one-shot. Our technique is evaluated in a one-of-a-kind experimental study, where the machine-learned shared control policy is compared to human assistance. Our analyses show that our technique is successful in emulating human shared control, by matching the location and amount of offered assistance on different trajectories. We observed that the effort requirement of the users were comparable between human-robot and human-human settings. Under the learned policy, the jerkiness of the user's joystick movements dropped significantly, despite a significant increase in the jerkiness of the robot assistant's commands. In terms of performance, even though the robotic assistance increased task completion time, the average distance to obstacles stayed in similar ranges to human assistance.
Collapse
|
124
|
Barsotti M, Leonardis D, Vanello N, Bergamasco M, Frisoli A. Effects of Continuous Kinaesthetic Feedback Based on Tendon Vibration on Motor Imagery BCI Performance. IEEE Trans Neural Syst Rehabil Eng 2018; 26:105-114. [DOI: 10.1109/tnsre.2017.2739244] [Citation(s) in RCA: 25] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
|
125
|
Randazzo L, Iturrate I, Perdikis S, Millan JDR. mano: A Wearable Hand Exoskeleton for Activities of Daily Living and Neurorehabilitation. IEEE Robot Autom Lett 2018. [DOI: 10.1109/lra.2017.2771329] [Citation(s) in RCA: 65] [Impact Index Per Article: 10.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
|
126
|
Baxter BS, Edelman BJ, Sohrabpour A, He B. Anodal Transcranial Direct Current Stimulation Increases Bilateral Directed Brain Connectivity during Motor-Imagery Based Brain-Computer Interface Control. Front Neurosci 2017; 11:691. [PMID: 29270110 PMCID: PMC5725434 DOI: 10.3389/fnins.2017.00691] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2017] [Accepted: 11/23/2017] [Indexed: 01/29/2023] Open
Abstract
Transcranial direct current stimulation (tDCS) has been shown to affect motor and cognitive task performance and learning when applied to brain areas involved in the task. Targeted stimulation has also been found to alter connectivity within the stimulated hemisphere during rest. However, the connectivity effect of the interaction of endogenous task specific activity and targeted stimulation is unclear. This study examined the aftereffects of concurrent anodal high-definition tDCS over the left sensorimotor cortex with motor network connectivity during a one-dimensional EEG based sensorimotor rhythm brain-computer interface (SMR-BCI) task. Directed connectivity following anodal tDCS illustrates altered connections bilaterally between frontal and parietal regions, and these alterations occur in a task specific manner; connections between similar cortical regions are altered differentially during left and right imagination trials. During right-hand imagination following anodal tDCS, there was an increase in outflow from the left premotor cortex (PMC) to multiple regions bilaterally in the motor network and increased inflow to the stimulated sensorimotor cortex from the ipsilateral PMC and contralateral sensorimotor cortex. During left-hand imagination following anodal tDCS, there was increased outflow from the stimulated sensorimotor cortex to regions across the motor network. Significant correlations between connectivity and the behavioral measures of total correct trials and time-to-hit (TTH) correct trials were also found, specifically that the input to the left PMC correlated with decreased right hand imagination performance and that flow from the ipsilateral posterior parietal cortex (PPC) to midline sensorimotor cortex correlated with improved performance for both right and left hand imagination. These results indicate that tDCS interacts with task-specific endogenous activity to alter directed connectivity during SMR-BCI. In order to predict and maximize the targeted effect of tDCS, the interaction of stimulation with the dynamics of endogenous activity needs to be examined comprehensively and understood.
Collapse
Affiliation(s)
- Bryan S. Baxter
- Department of Biomedical Engineering, University of Minnesota, Minneapolis, MN, United States
| | - Bradley J. Edelman
- Department of Biomedical Engineering, University of Minnesota, Minneapolis, MN, United States
| | - Abbas Sohrabpour
- Department of Biomedical Engineering, University of Minnesota, Minneapolis, MN, United States
| | - Bin He
- Department of Biomedical Engineering, University of Minnesota, Minneapolis, MN, United States
- Institute for Engineering in Medicine, University of Minnesota, Minneapolis, MN, United States
| |
Collapse
|
127
|
Tonoyan Y, Chanwimalueang T, Mandic DP, Van Hulle MM. Discrimination of emotional states from scalp- and intracranial EEG using multiscale Rényi entropy. PLoS One 2017; 12:e0186916. [PMID: 29099846 PMCID: PMC5669426 DOI: 10.1371/journal.pone.0186916] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2017] [Accepted: 10/10/2017] [Indexed: 11/22/2022] Open
Abstract
A data-adaptive, multiscale version of Rényi's quadratic entropy (RQE) is introduced for emotional state discrimination from EEG recordings. The algorithm is applied to scalp EEG recordings of 30 participants watching 4 emotionally-charged video clips taken from a validated public database. Krippendorff's inter-rater statistic reveals that multiscale RQE of the mid-frontal scalp electrodes best discriminates between five emotional states. Multiscale RQE is also applied to joint scalp EEG, amygdala- and occipital pole intracranial recordings of an implanted patient watching a neutral and an emotionally charged video clip. Unlike for the neutral video clip, the RQEs of the mid-frontal scalp electrodes and the amygdala-implanted electrodes are observed to coincide in the time range where the crux of the emotionally-charged video clip is revealed. In addition, also during this time range, phase synchrony between the amygdala and mid-frontal recordings is maximal, as well as our 30 participants' inter-rater agreement on the same video clip. A source reconstruction exercise using intracranial recordings supports our assertion that amygdala could contribute to mid-frontal scalp EEG. On the contrary, no such contribution was observed for the occipital pole's intracranial recordings. Our results suggest that emotional states discriminated from mid-frontal scalp EEG are likely to be mirrored by differences in amygdala activations in particular when recorded in response to emotionally-charged scenes.
Collapse
Affiliation(s)
- Yelena Tonoyan
- Research Group Neurophysiology, Laboratory for Neuro- and Psychophysiology, Leuven, Belgium
| | - Theerasak Chanwimalueang
- Communication and Signal Processing Research Group, Department of Electrical and Electronic Engineering, Imperial College London, London, United Kingdom
| | - Danilo P. Mandic
- Communication and Signal Processing Research Group, Department of Electrical and Electronic Engineering, Imperial College London, London, United Kingdom
| | - Marc M. Van Hulle
- Research Group Neurophysiology, Laboratory for Neuro- and Psychophysiology, Leuven, Belgium
| |
Collapse
|
128
|
Zeng H, Wang Y, Wu C, Song A, Liu J, Ji P, Xu B, Zhu L, Li H, Wen P. Closed-Loop Hybrid Gaze Brain-Machine Interface Based Robotic Arm Control with Augmented Reality Feedback. Front Neurorobot 2017; 11:60. [PMID: 29163123 PMCID: PMC5671634 DOI: 10.3389/fnbot.2017.00060] [Citation(s) in RCA: 37] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2017] [Accepted: 10/18/2017] [Indexed: 01/25/2023] Open
Abstract
Brain-machine interface (BMI) can be used to control the robotic arm to assist paralysis people for performing activities of daily living. However, it is still a complex task for the BMI users to control the process of objects grasping and lifting with the robotic arm. It is hard to achieve high efficiency and accuracy even after extensive trainings. One important reason is lacking of sufficient feedback information for the user to perform the closed-loop control. In this study, we proposed a method of augmented reality (AR) guiding assistance to provide the enhanced visual feedback to the user for a closed-loop control with a hybrid Gaze-BMI, which combines the electroencephalography (EEG) signals based BMI and the eye tracking for an intuitive and effective control of the robotic arm. Experiments for the objects manipulation tasks while avoiding the obstacle in the workspace are designed to evaluate the performance of our method for controlling the robotic arm. According to the experimental results obtained from eight subjects, the advantages of the proposed closed-loop system (with AR feedback) over the open-loop system (with visual inspection only) have been verified. The number of trigger commands used for controlling the robotic arm to grasp and lift the objects with AR feedback has reduced significantly and the height gaps of the gripper in the lifting process have decreased more than 50% compared to those trials with normal visual inspection only. The results reveal that the hybrid Gaze-BMI user can benefit from the information provided by the AR interface, improving the efficiency and reducing the cognitive load during the grasping and lifting processes.
Collapse
Affiliation(s)
- Hong Zeng
- School of Instrument Science and Engineering, Southeast University, Nanjing, China
| | - Yanxin Wang
- School of Instrument Science and Engineering, Southeast University, Nanjing, China
| | - Changcheng Wu
- College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, China
| | - Aiguo Song
- School of Instrument Science and Engineering, Southeast University, Nanjing, China
| | - Jia Liu
- Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, Nanjing University of Information Sciences and Technology, Nanjing, China
| | - Peng Ji
- School of Instrument Science and Engineering, Southeast University, Nanjing, China
| | - Baoguo Xu
- School of Instrument Science and Engineering, Southeast University, Nanjing, China
| | - Lifeng Zhu
- School of Instrument Science and Engineering, Southeast University, Nanjing, China
| | - Huijun Li
- School of Instrument Science and Engineering, Southeast University, Nanjing, China
| | - Pengcheng Wen
- AVIC Aeronautics Computing Technique Research Institute, Xi'an, China
| |
Collapse
|
129
|
Shin J, von Luhmann A, Blankertz B, Kim DW, Jeong J, Hwang HJ, Muller KR. Open Access Dataset for EEG+NIRS Single-Trial Classification. IEEE Trans Neural Syst Rehabil Eng 2017; 25:1735-1745. [PMID: 27849545 DOI: 10.1109/tnsre.2016.2628057] [Citation(s) in RCA: 80] [Impact Index Per Article: 11.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
We provide an open access dataset for hybrid brain-computer interfaces (BCIs) using electroencephalography (EEG) and near-infrared spectroscopy (NIRS). For this, we con-ducted two BCI experiments (left vs. right hand motor imagery; mental arithmetic vs. resting state). The dataset was validated using baseline signal analysis methods, with which classification performance was evaluated for each modality and a combination of both modalities. As already shown in previous literature, the capability of discriminating different mental states can be en-hanced by using a hybrid approach, when comparing to single modality analyses. This makes the provided data highly suitable for hybrid BCI investigations. Since our open access dataset also comprises motion artifacts and physiological data, we expect that it can be used in a wide range of future validation approaches in multimodal BCI research.
Collapse
|
130
|
Isaksen JL, Mohebbi A, Puthusserypady S. Optimal pseudorandom sequence selection for online c-VEP based BCI control applications. PLoS One 2017; 12:e0184785. [PMID: 28902895 PMCID: PMC5597237 DOI: 10.1371/journal.pone.0184785] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2016] [Accepted: 08/30/2017] [Indexed: 11/19/2022] Open
Abstract
Background In a c-VEP BCI setting, test subjects can have highly varying performances when different pseudorandom sequences are applied as stimulus, and ideally, multiple codes should be supported. On the other hand, repeating the experiment with many different pseudorandom sequences is a laborious process. Aims This study aimed to suggest an efficient method for choosing the optimal stimulus sequence based on a fast test and simple measures to increase the performance and minimize the time consumption for research trials. Methods A total of 21 healthy subjects were included in an online wheelchair control task and completed the same task using stimuli based on the m-code, the gold-code, and the Barker-code. Correct/incorrect identification and time consumption were obtained for each identification. Subject-specific templates were characterized and used in a forward-step first-order model to predict the chance of completion and accuracy score. Results No specific pseudorandom sequence showed superior accuracy on the group basis. When isolating the individual performances with the highest accuracy, time consumption per identification was not significantly increased. The Accuracy Score aids in predicting what pseudorandom sequence will lead to the best performance using only the templates. The Accuracy Score was higher when the template resembled a delta function the most and when repeated templates were consistent. For completion prediction, only the shape of the template was a significant predictor. Conclusions The simple and fast method presented in this study as the Accuracy Score, allows c-VEP based BCI systems to support multiple pseudorandom sequences without increase in trial length. This allows for more personalized BCI systems with better performance to be tested without increased costs.
Collapse
Affiliation(s)
- Jonas L. Isaksen
- Department of Electrical Engineering, Technical University of Denmark, Kongens Lyngby, Denmark
- Laboratory of Experimental Cardiology, Department of Biomedical Sciences, University of Copenhagen, Copenhagen, Denmark
- * E-mail:
| | - Ali Mohebbi
- Department of Electrical Engineering, Technical University of Denmark, Kongens Lyngby, Denmark
| | | |
Collapse
|
131
|
Pinegger A, Hiebel H, Wriessnegger SC, Müller-Putz GR. Composing only by thought: Novel application of the P300 brain-computer interface. PLoS One 2017; 12:e0181584. [PMID: 28877175 PMCID: PMC5587109 DOI: 10.1371/journal.pone.0181584] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2017] [Accepted: 07/03/2017] [Indexed: 11/19/2022] Open
Abstract
The P300 event-related potential is a well-known pattern in the electroencephalogram (EEG). This kind of brain signal is used for many different brain-computer interface (BCI) applications, e.g., spellers, environmental controllers, web browsers, or for painting. In recent times, BCI systems are mature enough to leave the laboratories to be used by the end-users, namely severely disabled people. Therefore, new challenges arise and the systems should be implemented and evaluated according to user-centered design (USD) guidelines. We developed and implemented a new system that utilizes the P300 pattern to compose music. Our Brain Composing system consists of three parts: the EEG acquisition device, the P300-based BCI, and the music composing software. Seventeen musical participants and one professional composer performed a copy-spelling, a copy-composing, and a free-composing task with the system. According to the USD guidelines, we investigated the efficiency, the effectiveness and subjective criteria in terms of satisfaction, enjoyment, frustration, and attractiveness. The musical participants group achieved high average accuracies: 88.24% (copy-spelling), 88.58% (copy-composing), and 76.51% (free-composing). The professional composer achieved also high accuracies: 100% (copy-spelling), 93.62% (copy-composing), and 98.20% (free-composing). General results regarding the subjective criteria evaluation were that the participants enjoyed the usage of the Brain Composing system and were highly satisfied with the system. Showing very positive results with healthy people in this study, this was the first step towards a music composing system for severely disabled people.
Collapse
Affiliation(s)
- Andreas Pinegger
- Institute of Neural Engineering, Graz University of Technology, Graz, Austria
| | - Hannah Hiebel
- Institute of Psychology, University of Graz, Graz, Austria
| | | | | |
Collapse
|
132
|
Tidoni E, Abu-Alqumsan M, Leonardis D, Kapeller C, Fusco G, Guger C, Hintermuller C, Peer A, Frisoli A, Tecchia F, Bergamasco M, Aglioti SM. Local and Remote Cooperation With Virtual and Robotic Agents: A P300 BCI Study in Healthy and People Living With Spinal Cord Injury. IEEE Trans Neural Syst Rehabil Eng 2017; 25:1622-1632. [DOI: 10.1109/tnsre.2016.2626391] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
|
133
|
Klosterman SL, Estepp JR, Monnin JW, Christensen JC. Day-to-day variability in hybrid, passive brain-computer interfaces: comparing two studies assessing cognitive workload. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2017; 2016:1584-1590. [PMID: 28268631 DOI: 10.1109/embc.2016.7591015] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
As hybrid, passive brain-computer interface systems become more advanced, it is important to grow our understanding of how to produce generalizable pattern classifiers of physiological data. One of the most difficult problems in applying machine learning algorithms to these data types is nonstationarity, which can evolve over the course of hours and days, and is more susceptible to changes resulting from complex cognitive function in comparison to simple, stimulus-based processes. This nonstationarity, referenced as day-to-day variability, results in the inability of many learning algorithms to generalize to new data. In previous work, we have shown that increasing the number of unique testing sessions used to form a learning set can improve the accuracy of classifying mental workload in a binary state paradigm. While this result was very promising, we did not address whether the additional discriminability was the result of a larger learning set or the uniqueness contributed by the testing sessions being spread over multiple days. Further, the simulation task used in this prior analysis was low-fidelity with respect to the task it attempted to model; whether these methods extend to more realistic task simulation environments has not been comparatively investigated. In this work, we compare these previous results to a second study, with a similar multi-day paradigm, that required participants to perform a more realistic simulation task. Comparative analysis of these two studies reveals that the improved generalization of the multi-day learning set is attributable, in large part, to the uniqueness of the multi-day paradigm. Further, this multi-day effect was also observed in the higher fidelity simulation study. These results help to validate the use of the multi-day learning set approach for improving overall system classification accuracy. Future studies should consider the use of multi-day designs for improving generalizability over other interesting dimensions.
Collapse
|
134
|
Cui C, Bian GB, Hou ZG, Zhao J, Zhou H. A Multimodal Framework Based on Integration of Cortical and Muscular Activities for Decoding Human Intentions About Lower Limb Motions. IEEE TRANSACTIONS ON BIOMEDICAL CIRCUITS AND SYSTEMS 2017; 11:889-899. [PMID: 28727560 DOI: 10.1109/tbcas.2017.2699189] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/07/2023]
Abstract
In this study, a multimodal fusion framework based on three different modal biosignals is developed to recognize human intentions related to lower limb multi-joint motions which commonly appear in daily life. Electroencephalogram (EEG), electromyogram (EMG) and mechanomyogram (MMG) signals were simultaneously recorded from twelve subjects while performing nine lower limb multi-joint motions. These multimodal data are used as the inputs of the fusion framework for identification of different motion intentions. Twelve fusion techniques are evaluated in this framework and a large number of comparative experiments are carried out. The results show that a support vector machine-based three-modal fusion scheme can achieve average accuracies of 98.61%, 97.78% and 96.85%, respectively, under three different data division forms. Furthermore, the relevant statistical tests reveal that this fusion scheme brings significant accuracy improvement in comparison with the cases of two-modal fusion or only a single modality. These promising results indicate the potential of the multimodal fusion framework for facilitating the future development of human-robot interaction for lower limb rehabilitation.
Collapse
|
135
|
Characterizing Computer Access Using a One-Channel EEG Wireless Sensor. SENSORS 2017; 17:s17071525. [PMID: 28661425 PMCID: PMC5539498 DOI: 10.3390/s17071525] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/01/2017] [Revised: 06/22/2017] [Accepted: 06/26/2017] [Indexed: 11/16/2022]
Abstract
This work studies the feasibility of using mental attention to access a computer. Brain activity was measured with an electrode placed at the Fp1 position and the reference on the left ear; seven normally developed people and three subjects with cerebral palsy (CP) took part in the experimentation. They were asked to keep their attention high and low for as long as possible during several trials. We recorded attention levels and power bands conveyed by the sensor, but only the first was used for feedback purposes. All of the information was statistically analyzed to find the most significant parameters and a classifier based on linear discriminant analysis (LDA) was also set up. In addition, 60% of the participants were potential users of this technology with an accuracy of over 70%. Including power bands in the classifier did not improve the accuracy in discriminating between the two attentional states. For most people, the best results were obtained by using only the attention indicator in classification. Tiredness was higher in the group with disabilities (2.7 in a scale of 3) than in the other (1.5 in the same scale); and modulating the attention to access a communication board requires that it does not contain many pictograms (between 4 and 7) on screen and has a scanning period of a relatively high tscan≈ 10 s. The information transfer rate (ITR) is similar to the one obtained by other brain computer interfaces (BCI), like those based on sensorimotor rhythms (SMR) or slow cortical potentials (SCP), and makes it suitable as an eye-gaze independent BCI.
Collapse
|
136
|
Ron-Angevin R, Velasco-Álvarez F, Fernández-Rodríguez Á, Díaz-Estrella A, Blanca-Mena MJ, Vizcaíno-Martín FJ. Brain-Computer Interface application: auditory serial interface to control a two-class motor-imagery-based wheelchair. J Neuroeng Rehabil 2017; 14:49. [PMID: 28558741 PMCID: PMC5450066 DOI: 10.1186/s12984-017-0261-y] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2016] [Accepted: 05/22/2017] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Certain diseases affect brain areas that control the movements of the patients' body, thereby limiting their autonomy and communication capacity. Research in the field of Brain-Computer Interfaces aims to provide patients with an alternative communication channel not based on muscular activity, but on the processing of brain signals. Through these systems, subjects can control external devices such as spellers to communicate, robotic prostheses to restore limb movements, or domotic systems. The present work focus on the non-muscular control of a robotic wheelchair. METHOD A proposal to control a wheelchair through a Brain-Computer Interface based on the discrimination of only two mental tasks is presented in this study. The wheelchair displacement is performed with discrete movements. The control signals used are sensorimotor rhythms modulated through a right-hand motor imagery task or mental idle state. The peculiarity of the control system is that it is based on a serial auditory interface that provides the user with four navigation commands. The use of two mental tasks to select commands may facilitate control and reduce error rates compared to other endogenous control systems for wheelchairs. RESULTS Seventeen subjects initially participated in the study; nine of them completed the three sessions of the proposed protocol. After the first calibration session, seven subjects were discarded due to a low control of their electroencephalographic signals; nine out of ten subjects controlled a virtual wheelchair during the second session; these same nine subjects achieved a medium accuracy level above 0.83 on the real wheelchair control session. CONCLUSION The results suggest that more extensive training with the proposed control system can be an effective and safe option that will allow the displacement of a wheelchair in a controlled environment for potential users suffering from some types of motor neuron diseases.
Collapse
Affiliation(s)
- Ricardo Ron-Angevin
- Department of Electronic Technology, University of Málaga, 29071 Málaga, Spain
| | | | | | | | - María José Blanca-Mena
- Department of Psychobiology and Methodology of Behavioral Sciences, University of Málaga, 29071 Málaga, Spain
| | | |
Collapse
|
137
|
Käthner I, Halder S, Hintermüller C, Espinosa A, Guger C, Miralles F, Vargiu E, Dauwalder S, Rafael-Palou X, Solà M, Daly JM, Armstrong E, Martin S, Kübler A. A Multifunctional Brain-Computer Interface Intended for Home Use: An Evaluation with Healthy Participants and Potential End Users with Dry and Gel-Based Electrodes. Front Neurosci 2017; 11:286. [PMID: 28588442 PMCID: PMC5439234 DOI: 10.3389/fnins.2017.00286] [Citation(s) in RCA: 35] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2017] [Accepted: 05/03/2017] [Indexed: 11/23/2022] Open
Abstract
Current brain-computer interface (BCIs) software is often tailored to the needs of scientists and technicians and therefore complex to allow for versatile use. To facilitate home use of BCIs a multifunctional P300 BCI with a graphical user interface intended for non-expert set-up and control was designed and implemented. The system includes applications for spelling, web access, entertainment, artistic expression and environmental control. In addition to new software, it also includes new hardware for the recording of electroencephalogram (EEG) signals. The EEG system consists of a small and wireless amplifier attached to a cap that can be equipped with gel-based or dry contact electrodes. The system was systematically evaluated with a healthy sample, and targeted end users of BCI technology, i.e., people with a varying degree of motor impairment tested the BCI in a series of individual case studies. Usability was assessed in terms of effectiveness, efficiency and satisfaction. Feedback of users was gathered with structured questionnaires. Two groups of healthy participants completed an experimental protocol with the gel-based and the dry contact electrodes (N = 10 each). The results demonstrated that all healthy participants gained control over the system and achieved satisfactory to high accuracies with both gel-based and dry electrodes (average error rates of 6 and 13%). Average satisfaction ratings were high, but certain aspects of the system such as the wearing comfort of the dry electrodes and design of the cap, and speed (in both groups) were criticized by some participants. Six potential end users tested the system during supervised sessions. The achieved accuracies varied greatly from no control to high control with accuracies comparable to that of healthy volunteers. Satisfaction ratings of the two end-users that gained control of the system were lower as compared to healthy participants. The advantages and disadvantages of the BCI and its applications are discussed and suggestions are presented for improvements to pave the way for user friendly BCIs intended to be used as assistive technology by persons with severe paralysis.
Collapse
Affiliation(s)
- Ivo Käthner
- Institute of Psychology, University of WürzburgWürzburg, Germany
| | - Sebastian Halder
- Institute of Psychology, University of WürzburgWürzburg, Germany
| | | | | | | | - Felip Miralles
- eHealth Unit, Eurecat - Technology Center of CataloniaBarcelona, Spain
| | - Eloisa Vargiu
- eHealth Unit, Eurecat - Technology Center of CataloniaBarcelona, Spain
| | - Stefan Dauwalder
- eHealth Unit, Eurecat - Technology Center of CataloniaBarcelona, Spain
| | | | - Marc Solà
- eHealth Unit, Eurecat - Technology Center of CataloniaBarcelona, Spain
| | | | | | | | - Andrea Kübler
- Institute of Psychology, University of WürzburgWürzburg, Germany
| |
Collapse
|
138
|
Bhattacharyya S, Konar A, Tibarewala DN, Hayashibe M. A Generic Transferable EEG Decoder for Online Detection of Error Potential in Target Selection. Front Neurosci 2017; 11:226. [PMID: 28512396 PMCID: PMC5411431 DOI: 10.3389/fnins.2017.00226] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2016] [Accepted: 04/04/2017] [Indexed: 11/13/2022] Open
Abstract
Reliable detection of error from electroencephalography (EEG) signals as feedback while performing a discrete target selection task across sessions and subjects has a huge scope in real-time rehabilitative application of Brain-computer Interfacing (BCI). Error Related Potentials (ErrP) are EEG signals which occur when the participant observes an erroneous feedback from the system. ErrP holds significance in such closed-loop system, as BCI is prone to error and we need an effective method of systematic error detection as feedback for correction. In this paper, we have proposed a novel scheme for online detection of error feedback directly from the EEG signal in a transferable environment (i.e., across sessions and across subjects). For this purpose, we have used a P300-speller dataset available on a BCI competition website. The task involves the subject to select a letter of a word which is followed by a feedback period. The feedback period displays the letter selected and, if the selection is wrong, the subject perceives it by the generation of ErrP signal. Our proposed system is designed to detect ErrP present in the EEG from new independent datasets, not involved in its training. Thus, the decoder is trained using EEG features of 16 subjects for single-trial classification and tested on 10 independent subjects. The decoder designed for this task is an ensemble of linear discriminant analysis, quadratic discriminant analysis, and logistic regression classifier. The performance of the decoder is evaluated using accuracy, F1-score, and Area Under the Curve metric and the results obtained is 73.97, 83.53, and 73.18%, respectively.
Collapse
Affiliation(s)
| | - Amit Konar
- Department of Electronics and Telecommunication Engineering, Jadavpur UniveristyKolkata, India
| | - D N Tibarewala
- School of Bioscience and Engineering, Jadavpur UniveristyKolkata, India
| | | |
Collapse
|
139
|
Myrden A, Chau T. A Passive EEG-BCI for Single-Trial Detection of Changes in Mental State. IEEE Trans Neural Syst Rehabil Eng 2017; 25:345-356. [DOI: 10.1109/tnsre.2016.2641956] [Citation(s) in RCA: 51] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
|
140
|
Marchesotti S, Martuzzi R, Schurger A, Blefari ML, Del Millán JR, Bleuler H, Blanke O. Cortical and subcortical mechanisms of brain-machine interfaces. Hum Brain Mapp 2017; 38:2971-2989. [PMID: 28321973 DOI: 10.1002/hbm.23566] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2016] [Revised: 02/28/2017] [Accepted: 03/03/2017] [Indexed: 01/06/2023] Open
Abstract
Technical advances in the field of Brain-Machine Interfaces (BMIs) enable users to control a variety of external devices such as robotic arms, wheelchairs, virtual entities and communication systems through the decoding of brain signals in real time. Most BMI systems sample activity from restricted brain regions, typically the motor and premotor cortex, with limited spatial resolution. Despite the growing number of applications, the cortical and subcortical systems involved in BMI control are currently unknown at the whole-brain level. Here, we provide a comprehensive and detailed report of the areas active during on-line BMI control. We recorded functional magnetic resonance imaging (fMRI) data while participants controlled an EEG-based BMI inside the scanner. We identified the regions activated during BMI control and how they overlap with those involved in motor imagery (without any BMI control). In addition, we investigated which regions reflect the subjective sense of controlling a BMI, the sense of agency for BMI-actions. Our data revealed an extended cortical-subcortical network involved in operating a motor-imagery BMI. This includes not only sensorimotor regions but also the posterior parietal cortex, the insula and the lateral occipital cortex. Interestingly, the basal ganglia and the anterior cingulate cortex were involved in the subjective sense of controlling the BMI. These results inform basic neuroscience by showing that the mechanisms of BMI control extend beyond sensorimotor cortices. This knowledge may be useful for the development of BMIs that offer a more natural and embodied feeling of control for the user. Hum Brain Mapp 38:2971-2989, 2017. © 2017 Wiley Periodicals, Inc.
Collapse
Affiliation(s)
- Silvia Marchesotti
- Laboratory of Cognitive Neuroscience, School of Life Sciences, Ecole Polytechnique Fédérale de Lausanne, Geneva, Switzerland.,Center for Neuroprosthetics, Ecole Polytechnique Fédérale de Lausanne, Geneva, Switzerland.,Laboratory of Robotic Systems, School of Engineering, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Roberto Martuzzi
- Laboratory of Cognitive Neuroscience, School of Life Sciences, Ecole Polytechnique Fédérale de Lausanne, Geneva, Switzerland.,Center for Neuroprosthetics, Ecole Polytechnique Fédérale de Lausanne, Geneva, Switzerland.,Fondation Campus Biotech Geneva, Geneva, Switzerland
| | - Aaron Schurger
- Laboratory of Cognitive Neuroscience, School of Life Sciences, Ecole Polytechnique Fédérale de Lausanne, Geneva, Switzerland.,Center for Neuroprosthetics, Ecole Polytechnique Fédérale de Lausanne, Geneva, Switzerland.,Defitech Chair in Brain-Machine Interface, School of Engineering, Ecole Polytechnique Fédérale de Lausanne, Geneva, Switzerland.,Cognitive Neuroimaging Unit, NeuroSpin Research Center, INSERM, Gif-Sur-Yvette, France
| | - Maria Laura Blefari
- Laboratory of Cognitive Neuroscience, School of Life Sciences, Ecole Polytechnique Fédérale de Lausanne, Geneva, Switzerland.,Center for Neuroprosthetics, Ecole Polytechnique Fédérale de Lausanne, Geneva, Switzerland.,Defitech Chair in Brain-Machine Interface, School of Engineering, Ecole Polytechnique Fédérale de Lausanne, Geneva, Switzerland
| | - José R Del Millán
- Center for Neuroprosthetics, Ecole Polytechnique Fédérale de Lausanne, Geneva, Switzerland.,Defitech Chair in Brain-Machine Interface, School of Engineering, Ecole Polytechnique Fédérale de Lausanne, Geneva, Switzerland
| | - Hannes Bleuler
- Laboratory of Robotic Systems, School of Engineering, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Olaf Blanke
- Laboratory of Cognitive Neuroscience, School of Life Sciences, Ecole Polytechnique Fédérale de Lausanne, Geneva, Switzerland.,Center for Neuroprosthetics, Ecole Polytechnique Fédérale de Lausanne, Geneva, Switzerland.,Department of Neurology, University Hospital, Geneva, Switzerland
| |
Collapse
|
141
|
Mercado L, Rodriguez-Linan A, Torres-Trevino LM, Quiroz G. Hybrid BCI approach to control an artificial tibio-femoral joint. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2017; 2016:2760-2763. [PMID: 28268891 DOI: 10.1109/embc.2016.7591302] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
Brain-Computer Interfaces (BCIs) for disabled people should allow them to use their remaining functionalities as control possibilities. BCIs connect the brain with external devices to perform the volition or intent of movement, regardless if that individual is unable to perform the task due to body impairments. In this work we fuse electromyographic (EMG) with electroencephalographic (EEG) activity in a framework called "Hybrid-BCI" (hBCI) approach to control the movement of a simulated tibio-femoral joint. Two mathematical models of a tibio-femoral joint are used to emulate the kinematic and dynamic behavior of the knee. The interest is to reproduce different velocities of the human gait cycle. The EEG signals are used to classify the user intent, which are the velocity changes, meanwhile the superficial EMG signals are used to estimate the amplitude of such intent. A multi-level controller is used to solve the trajectory tracking problem involved. The lower level consists of an individual controller for each model, it solves the tracking of the desired trajectory even considering different velocities of the human gait cycle. The mid-level uses a combination of a logical operator and a finite state machine for the switching between models. Finally, the highest level consists in a support vector machine to classify the desired activity.
Collapse
|
142
|
Enriquez-Geppert S, Huster RJ, Herrmann CS. EEG-Neurofeedback as a Tool to Modulate Cognition and Behavior: A Review Tutorial. Front Hum Neurosci 2017; 11:51. [PMID: 28275344 PMCID: PMC5319996 DOI: 10.3389/fnhum.2017.00051] [Citation(s) in RCA: 114] [Impact Index Per Article: 16.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2016] [Accepted: 01/23/2017] [Indexed: 01/02/2023] Open
Abstract
Neurofeedback is attracting renewed interest as a method to self-regulate one’s own brain activity to directly alter the underlying neural mechanisms of cognition and behavior. It not only promises new avenues as a method for cognitive enhancement in healthy subjects, but also as a therapeutic tool. In the current article, we present a review tutorial discussing key aspects relevant to the development of electroencephalography (EEG) neurofeedback studies. In addition, the putative mechanisms underlying neurofeedback learning are considered. We highlight both aspects relevant for the practical application of neurofeedback as well as rather theoretical considerations related to the development of new generation protocols. Important characteristics regarding the set-up of a neurofeedback protocol are outlined in a step-by-step way. All these practical and theoretical considerations are illustrated based on a protocol and results of a frontal-midline theta up-regulation training for the improvement of executive functions. Not least, assessment criteria for the validation of neurofeedback studies as well as general guidelines for the evaluation of training efficacy are discussed.
Collapse
Affiliation(s)
- Stefanie Enriquez-Geppert
- Department of Clinical and Developmental Neuropsychology, Faculty of Behavioural and Social Sciences, University of Groningen Groningen, Netherlands
| | - René J Huster
- Department of Psychology, Faculty of Social Sciences, University of Oslo Oslo, Norway
| | - Christoph S Herrmann
- Experimental Psychology Laboratory, Department of Psychology, Faculty VI Medical and Health Sciences, University of Oldenburg Oldenburg, Germany
| |
Collapse
|
143
|
Úbeda A, Azorín JM, Chavarriaga R, R Millán JD. Classification of upper limb center-out reaching tasks by means of EEG-based continuous decoding techniques. J Neuroeng Rehabil 2017; 14:9. [PMID: 28143603 PMCID: PMC5286813 DOI: 10.1186/s12984-017-0219-0] [Citation(s) in RCA: 35] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2016] [Accepted: 01/17/2017] [Indexed: 11/18/2022] Open
Abstract
Background One of the current challenges in brain-machine interfacing is to characterize and decode upper limb kinematics from brain signals, e.g. to control a prosthetic device. Recent research work states that it is possible to do so based on low frequency EEG components. However, the validity of these results is still a matter of discussion. In this paper, we assess the feasibility of decoding upper limb kinematics from EEG signals in center-out reaching tasks during passive and active movements. Methods The decoding of arm movement was performed using a multidimensional linear regression. Passive movements were analyzed using the same methodology to study the influence of proprioceptive sensory feedback in the decoding. Finally, we evaluated the possible advantages of classifying reaching targets, instead of continuous trajectories. Results The results showed that arm movement decoding was significantly above chance levels. The results also indicated that EEG slow cortical potentials carry significant information to decode active center-out movements. The classification of reached targets allowed obtaining the same conclusions with a very high accuracy. Additionally, the low decoding performance obtained from passive movements suggests that discriminant modulations of low-frequency neural activity are mainly related to the execution of movement while proprioceptive feedback is not sufficient to decode upper limb kinematics. Conclusions This paper contributes to the assessment of feasibility of using linear regression methods to decode upper limb kinematics from EEG signals. From our findings, it can be concluded that low frequency bands concentrate most of the information extracted from upper limb kinematics decoding and that decoding performance of active movements is above chance levels and mainly related to the activation of cortical motor areas. We also show that the classification of reached targets from decoding approaches may be a more suitable real-time methodology than a direct decoding of hand position.
Collapse
Affiliation(s)
- Andrés Úbeda
- Brain-Machine Interface Systems Lab, Miguel Hernández University, Av. de la Universidad, S/N, Elche, 03202, Spain
| | - José M Azorín
- Brain-Machine Interface Systems Lab, Miguel Hernández University, Av. de la Universidad, S/N, Elche, 03202, Spain
| | - Ricardo Chavarriaga
- Defitech Chair in Brain-Machine Interface (CNBI), École Polytechnique Fédérale de Lausanne (EPFL), Chemin des Mines 9, Geneva, CH-1202, Switzerland.
| | - José Del R Millán
- Defitech Chair in Brain-Machine Interface (CNBI), École Polytechnique Fédérale de Lausanne (EPFL), Chemin des Mines 9, Geneva, CH-1202, Switzerland
| |
Collapse
|
144
|
Mehrabi N, Sharif Razavian R, Ghannadi B, McPhee J. Predictive Simulation of Reaching Moving Targets Using Nonlinear Model Predictive Control. Front Comput Neurosci 2017; 10:143. [PMID: 28133449 PMCID: PMC5233688 DOI: 10.3389/fncom.2016.00143] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2016] [Accepted: 12/20/2016] [Indexed: 12/02/2022] Open
Abstract
This article investigates the application of optimal feedback control to trajectory planning in voluntary human arm movements. A nonlinear model predictive controller (NMPC) with a finite prediction horizon was used as the optimal feedback controller to predict the hand trajectory planning and execution of planar reaching tasks. The NMPC is completely predictive, and motion tracking or electromyography data are not required to obtain the limb trajectories. To present this concept, a two degree of freedom musculoskeletal planar arm model actuated by three pairs of antagonist muscles was used to simulate the human arm dynamics. This study is based on the assumption that the nervous system minimizes the muscular effort during goal-directed movements. The effects of prediction horizon length on the trajectory, velocity profile, and muscle activities of a reaching task are presented. The NMPC predictions of the hand trajectory to reach fixed and moving targets are in good agreement with the trajectories found by dynamic optimization and those from experiments. However, the hand velocity and muscle activations predicted by NMPC did not agree as well with experiments or with those found from dynamic optimization.
Collapse
Affiliation(s)
- Naser Mehrabi
- Systems Design Engineering, University of Waterloo Waterloo, ON, Canada
| | | | - Borna Ghannadi
- Systems Design Engineering, University of Waterloo Waterloo, ON, Canada
| | - John McPhee
- Systems Design Engineering, University of Waterloo Waterloo, ON, Canada
| |
Collapse
|
145
|
Müller-Putz GR, Plank P, Stadlbauer B, Statthaler K, Uroko JB. 15 Years of Evolution of Non-Invasive EEG-Based Methods for Restoring Hand & Arm Function with Motor Neuroprosthetics in Individuals with High Spinal Cord Injury: A Review of Graz BCI Research. ACTA ACUST UNITED AC 2017. [DOI: 10.4236/jbise.2017.106024] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
|
146
|
Kinney-Lang E, Auyeung B, Escudero J. Expanding the (kaleido)scope: exploring current literature trends for translating electroencephalography (EEG) based brain–computer interfaces for motor rehabilitation in children. J Neural Eng 2016; 13:061002. [DOI: 10.1088/1741-2560/13/6/061002] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/20/2023]
|
147
|
Fernández-Rodríguez Á, Velasco-Álvarez F, Ron-Angevin R. Review of real brain-controlled wheelchairs. J Neural Eng 2016; 13:061001. [PMID: 27739401 DOI: 10.1088/1741-2560/13/6/061001] [Citation(s) in RCA: 45] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
This paper presents a review of the state of the art regarding wheelchairs driven by a brain-computer interface. Using a brain-controlled wheelchair (BCW), disabled users could handle a wheelchair through their brain activity, granting autonomy to move through an experimental environment. A classification is established, based on the characteristics of the BCW, such as the type of electroencephalographic signal used, the navigation system employed by the wheelchair, the task for the participants, or the metrics used to evaluate the performance. Furthermore, these factors are compared according to the type of signal used, in order to clarify the differences among them. Finally, the trend of current research in this field is discussed, as well as the challenges that should be solved in the future.
Collapse
|
148
|
Scherer R, Faller J, Opisso E, Costa U, Steyrl D, Muller-Putz GR. Bring mental activity into action! An enhanced online co-adaptive brain-computer interface training protocol. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2016; 2015:2323-6. [PMID: 26736758 DOI: 10.1109/embc.2015.7318858] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
Non-stationarity and inherent variability of the noninvasive electroencephalogram (EEG) makes robust recognition of spontaneous EEG patterns challenging. Reliable modulation of EEG patterns that a BCI can robustly detect is a skill that users must learn. In this paper, we present a novel online co-adaptive BCI training paradigm. The system autonomously screens users for their ability to modulate EEG patterns in a predictive way and adapts its model parameters online. Results of a supporting study in seven first-time BCI users with disability are very encouraging. Three of 7 users achieved online accuracy > 70% for 2-class BCI control after 24 minutes of training. Online performance in 6 of 7 users was significantly higher than chance level. Online control was based on one single bipolar EEG channel. Beta band activity carried most discriminant information. Our fully automatic co-adaptive online approach allows to evaluate whether user benefit from current BCI technology within a reasonable timescale.
Collapse
|
149
|
Kim KT, Suk HI, Lee SW. Commanding a Brain-Controlled Wheelchair Using Steady-State Somatosensory Evoked Potentials. IEEE Trans Neural Syst Rehabil Eng 2016; 26:654-665. [PMID: 27514060 DOI: 10.1109/tnsre.2016.2597854] [Citation(s) in RCA: 42] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
In this work, we propose a novel brain-controlled wheelchair, one of the major applications of brain-machine interfaces (BMIs), that allows an individual with mobility impairments to perform daily living activities independently. Specifically, we propose to use a steady-state somatosensory evoked potential (SSSEP) paradigm, which elicits brain responses to tactile stimulation of specific frequencies, for a user's intention to control a wheelchair. In our system, a user had three possible commands by concentrating on one of three vibration stimuli, which were attached to the left-hand, right-hand, and right-foot, to selectively control the wheelchair. The three stimuli were associated with three wheelchair commands: turn-left, turn-right, and move-forward. From a machine learning perspective, we also devise a novel feature representation by combining spatial and spectral characteristics of brain signals. In order to validate the effectiveness of the proposed SSSEP-based system, we considered two different tasks: 1) a simple obstacle-avoidance task within a limited time and; 2) a driving task along the predefined trajectory of about 40 m length, where there were a narrow pathway, a door, and obstacles. In both experiments, we recruited 12 subjects and compared the average time of motor imagery (MI) and SSSEP-based controls to complete the task. With the SSSEP-based control, all subjects successfully completed the task without making any collision while four subjects failed it with MI-based control. It is also noteworthy that in terms of the average time to complete the task, the SSSEP-based control outperformed the MI-based control. In the other more challenging task, all subjects successfully reached the target location.
Collapse
|
150
|
López-Larraz E, Trincado-Alonso F, Rajasekaran V, Pérez-Nombela S, Del-Ama AJ, Aranda J, Minguez J, Gil-Agudo A, Montesano L. Control of an Ambulatory Exoskeleton with a Brain-Machine Interface for Spinal Cord Injury Gait Rehabilitation. Front Neurosci 2016; 10:359. [PMID: 27536214 PMCID: PMC4971110 DOI: 10.3389/fnins.2016.00359] [Citation(s) in RCA: 62] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2016] [Accepted: 07/19/2016] [Indexed: 12/11/2022] Open
Abstract
The closed-loop control of rehabilitative technologies by neural commands has shown a great potential to improve motor recovery in patients suffering from paralysis. Brain-machine interfaces (BMI) can be used as a natural control method for such technologies. BMI provides a continuous association between the brain activity and peripheral stimulation, with the potential to induce plastic changes in the nervous system. Paraplegic patients, and especially the ones with incomplete injuries, constitute a potential target population to be rehabilitated with brain-controlled robotic systems, as they may improve their gait function after the reinforcement of their spared intact neural pathways. This paper proposes a closed-loop BMI system to control an ambulatory exoskeleton-without any weight or balance support-for gait rehabilitation of incomplete spinal cord injury (SCI) patients. The integrated system was validated with three healthy subjects, and its viability in a clinical scenario was tested with four SCI patients. Using a cue-guided paradigm, the electroencephalographic signals of the subjects were used to decode their gait intention and to trigger the movements of the exoskeleton. We designed a protocol with a special emphasis on safety, as patients with poor balance were required to stand and walk. We continuously monitored their fatigue and exertion level, and conducted usability and user-satisfaction tests after the experiments. The results show that, for the three healthy subjects, 84.44 ± 14.56% of the trials were correctly decoded. Three out of four patients performed at least one successful BMI session, with an average performance of 77.6 1 ± 14.72%. The shared control strategy implemented (i.e., the exoskeleton could only move during specific periods of time) was effective in preventing unexpected movements during periods in which patients were asked to relax. On average, 55.22 ± 16.69% and 40.45 ± 16.98% of the trials (for healthy subjects and patients, respectively) would have suffered from unexpected activations (i.e., false positives) without the proposed control strategy. All the patients showed low exertion and fatigue levels during the performance of the experiments. This paper constitutes a proof-of-concept study to validate the feasibility of a BMI to control an ambulatory exoskeleton by patients with incomplete paraplegia (i.e., patients with good prognosis for gait rehabilitation).
Collapse
Affiliation(s)
- Eduardo López-Larraz
- Departamento de Informática e Ingeniería de Sistemas, University of ZaragozaZaragoza, Spain; Instituto de Investigación en Ingeniería de Aragón (I3A)Zaragoza, Spain
| | | | - Vijaykumar Rajasekaran
- Institute for Bioengineering of Catalunya, Universitat Politécnica de Catalunya Barcelona, Spain
| | - Soraya Pérez-Nombela
- Biomechanics and Technical Aids Unit, National Hospital for Spinal Cord Injury Toledo, Spain
| | - Antonio J Del-Ama
- Biomechanics and Technical Aids Unit, National Hospital for Spinal Cord Injury Toledo, Spain
| | - Joan Aranda
- Institute for Bioengineering of Catalunya, Universitat Politécnica de Catalunya Barcelona, Spain
| | - Javier Minguez
- Departamento de Informática e Ingeniería de Sistemas, University of ZaragozaZaragoza, Spain; Instituto de Investigación en Ingeniería de Aragón (I3A)Zaragoza, Spain; Bit & Brain TechnologiesZaragoza, Spain
| | - Angel Gil-Agudo
- Biomechanics and Technical Aids Unit, National Hospital for Spinal Cord Injury Toledo, Spain
| | - Luis Montesano
- Departamento de Informática e Ingeniería de Sistemas, University of ZaragozaZaragoza, Spain; Instituto de Investigación en Ingeniería de Aragón (I3A)Zaragoza, Spain
| |
Collapse
|