Journal ArticleDOI
Brain-computer interfaces in neurological rehabilitation.
Janis J. Daly,Jonathan R. Wolpaw +1 more
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TLDR
Non-invasive, electroencephalogram (EEG)-based brain-computer interface technologies can be used to control a computer cursor or a limb orthosis, for word processing and accessing the internet, and for other functions such as environmental control or entertainment.Abstract:
Summary Recent advances in analysis of brain signals, training patients to control these signals, and improved computing capabilities have enabled people with severe motor disabilities to use their brain signals for communication and control of objects in their environment, thereby bypassing their impaired neuromuscular system. Non-invasive, electroencephalogram (EEG)-based brain–computer interface (BCI) technologies can be used to control a computer cursor or a limb orthosis, for word processing and accessing the internet, and for other functions such as environmental control or entertainment. By re-establishing some independence, BCI technologies can substantially improve the lives of people with devastating neurological disorders such as advanced amyotrophic lateral sclerosis. BCI technology might also restore more effective motor control to people after stroke or other traumatic brain disorders by helping to guide activity-dependent brain plasticity by use of EEG brain signals to indicate to the patient the current state of brain activity and to enable the user to subsequently lower abnormal activity. Alternatively, by use of brain signals to supplement impaired muscle control, BCIs might increase the efficacy of a rehabilitation protocol and thus improve muscle control for the patient.read more
Citations
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Journal ArticleDOI
Deep learning with convolutional neural networks for EEG decoding and visualization.
Robin Tibor Schirrmeister,Jost Tobias Springenberg,Lukas D. J. Fiederer,Martin Glasstetter,Katharina Eggensperger,Michael Tangermann,Frank Hutter,Wolfram Burgard,Tonio Ball +8 more
TL;DR: This study shows how to design and train convolutional neural networks to decode task‐related information from the raw EEG without handcrafted features and highlights the potential of deep ConvNets combined with advanced visualization techniques for EEG‐based brain mapping.
Journal ArticleDOI
Brain Computer Interfaces, a Review
TL;DR: The state-of-the-art of BCIs are reviewed, looking at the different steps that form a standard BCI: signal acquisition, preprocessing or signal enhancement, feature extraction, classification and the control interface.
Journal ArticleDOI
Review of control strategies for robotic movement training after neurologic injury
TL;DR: There is increasing interest in using robotic devices to assist in movement training following neurologic injuries such as stroke and spinal cord injury, and this review summarizes techniques for implementing assistive strategies, including impedance-, counterbalance-, and EMG- based controllers, as well as adaptive controllers that modify control parameters based on ongoing participant performance.
Journal ArticleDOI
Combining Brain-Computer Interfaces and Assistive Technologies: State-of-the-Art and Challenges.
José del R. Millán,Rüdiger Rupp,Gernot Müller-Putz,Rod Murray-Smith,Claudio Giugliemma,Michael Tangermann,Carmen Vidaurre,Febo Cincotti,Andrea Kübler,Robert Leeb,Christa Neuper,Klaus-Robert Müller,Donatella Mattia +12 more
TL;DR: This paper focuses on the prospect of improving the lives of countless disabled individuals through a combination of BCI technology with existing assistive technologies (AT) and identifies four application areas where disabled individuals could greatly benefit from advancements inBCI technology, namely, “Communication and Control”, ‘Motor Substitution’, ”Entertainment” and “Motor Recovery”.
Journal ArticleDOI
Brain–machine interface in chronic stroke rehabilitation: A controlled study
Ander Ramos-Murguialday,Doris Broetz,Massimiliano Rea,Leonhard Läer,Ozge Yilmaz,Fabricio Lima Brasil,Giulia Liberati,Marco Curado,Eliana Garcia-Cossio,Alexandros Vyziotis,Woosang Cho,Manuel Agostini,Ernesto Soares,Surjo R. Soekadar,Andrea Caria,Leonardo G. Cohen,Niels Birbaumer +16 more
TL;DR: Evaluated efficacy of daily brain–machine interface (BMI) training to increase the hypothesized beneficial effects of physiotherapy alone in patients with severe paresis in a double‐blind sham‐controlled design proof of concept study.
References
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Journal ArticleDOI
Brain-computer interfaces for communication and control.
Jonathan R. Wolpaw,Jonathan R. Wolpaw,Niels Birbaumer,Niels Birbaumer,Dennis J. McFarland,Gert Pfurtscheller,Theresa M. Vaughan +6 more
TL;DR: With adequate recognition and effective engagement of all issues, BCI systems could eventually provide an important new communication and control option for those with motor disabilities and might also give those without disabilities a supplementary control channel or a control channel useful in special circumstances.
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Über das Elektrenkephalogramm des Menschen
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Neuronal ensemble control of prosthetic devices by a human with tetraplegia
Leigh R. Hochberg,Leigh R. Hochberg,Mijail D. Serruya,Gerhard Friehs,Gerhard Friehs,Jon A. Mukand,Jon A. Mukand,Maryam Saleh,Abraham H. Caplan,Almut Branner,David Chen,Richard D. Penn,John P. Donoghue +12 more
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Journal ArticleDOI
Talking off the top of your head: toward a mental prosthesis utilizing event-related brain potentials
TL;DR: The analyses suggest that this communication channel can be operated accurately at the rate of 0.20 bits/sec, which means that subjects can communicate 12.0 bits, or 2.3 characters, per min.
Journal ArticleDOI
Neurophysiological mechanisms underlying the understanding and imitation of action.
TL;DR: Evidence for the existence of a system, the 'mirror system', that seems to serve this mapping function in primates and humans is discussed, and its implications for the understanding and imitation of action are explored.