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research
Defining brain–machine interface applications by matching interface performance with device requirements
Authors
Aggarwal
Ajiboye
+130 more
Axelrod
Bailey
Bayliss
Bayliss
Birbaumer
Birbaumer
Birbaumer
Blankertz
Bokil
Boksem
Buttfield
Card
Carmena
Chan
Chapin
Cheng
Cincotti
Citi
Corneil
Coyle
Crampton
Curran
De Silva
Donchin
Donoghue
Dornhege
Englehart
Fabiani
Farwell
Fetz
Fitts
Gao
Geng
Georgopoulos
Giuseppe Megali
Graimann
Graimann
Grauman
Guger
Hendy
Hinz
Hochberg
Humphrey
Hyrskykari
Inverso
Jacob
Kaper
Kauhanen
Kelly
Kelly
Kennedy
Kennedy
Kennedy
Kennedy
Kostov
Krebs
Kronegg
Kübler
Laubach
Lebedev
Leuthardt
Lorist
Luca Citi
Luca Rossini
MacKenzie
MacKenzie
Marg
Martina Marinelli
Mason
Mason
Mason
Maynard
McFarland
Meinicke
Micera
Middendorf
Millán
Millán
Moran
Mussa-Ivaldi
Müller-Putz
Müller-Putz
Navarro
Neumann
Neuper
Obermaier
Oh
Oliver Tonet
Paolo Dario
Paolo Maria Rossini
Perelmouter
Pfurtscheller
Pfurtscheller
Pfurtscheller
Plaisant
Rickert
Rossini
Rossini
Santhanam
Scherer
Sellers
Sellers
Serby
Serruya
Sutter
Tanaka
Taylor
Taylor
Tecchio
Tejima
Thulasidas
Thulasidas
Tomei
Urban
Vaughan
Vuckovic
Wang
Wang
Ward
Weiskopf
Wessberg
Wilson
Wolpaw
Wolpaw
Wolpaw
Wolpaw
Wolpaw
Xiao
Yoo
Zecca
Publication date
2 April 2007
Publisher
'Elsevier BV'
Doi
Cite
Abstract
Interaction with machines is mediated by human-machine interfaces (HMIs). Brain-machine interfaces (BMIs) are a particular class of HMIs and have so far been studied as a communication means for people who have little or no voluntary control of muscle activity. In this context, low-performing interfaces can be considered as prosthetic applications. On the other hand, for able-bodied users, a BMI would only be practical if conceived as an augmenting interface. In this paper, a method is introduced for pointing out effective combinations of interfaces and devices for creating real-world applications. First, devices for domotics, rehabilitation and assistive robotics, and their requirements, in terms of throughput and latency, are described. Second, HMIs are classified and their performance described, still in terms of throughput and latency. Then device requirements are matched with performance of available interfaces. Simple rehabilitation and domotics devices can be easily controlled by means of BMI technology. Prosthetic hands and wheelchairs are suitable applications but do not attain optimal interactivity. Regarding humanoid robotics, the head and the trunk can be controlled by means of BMIs, while other parts require too much throughput. Robotic arms, which have been controlled by means of cortical invasive interfaces in animal studies, could be the next frontier for non-invasive BMIs. Combining smart controllers with BMIs could improve interactivity and boost BMI applications. © 2007 Elsevier B.V. All rights reserved
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University of Essex Research Repository
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