Tonet, Oliver and Marinelli, Martina and Citi, Luca and Rossini, Paolo Maria and Rossini, Luca and Megali, Giuseppe and Dario, Paolo (2008) Defining brain–machine interface applications by matching interface performance with device requirements. Journal of Neuroscience Methods, 167 (1). pp. 91-104. DOI https://doi.org/10.1016/j.jneumeth.2007.03.015
Tonet, Oliver and Marinelli, Martina and Citi, Luca and Rossini, Paolo Maria and Rossini, Luca and Megali, Giuseppe and Dario, Paolo (2008) Defining brain–machine interface applications by matching interface performance with device requirements. Journal of Neuroscience Methods, 167 (1). pp. 91-104. DOI https://doi.org/10.1016/j.jneumeth.2007.03.015
Tonet, Oliver and Marinelli, Martina and Citi, Luca and Rossini, Paolo Maria and Rossini, Luca and Megali, Giuseppe and Dario, Paolo (2008) Defining brain–machine interface applications by matching interface performance with device requirements. Journal of Neuroscience Methods, 167 (1). pp. 91-104. DOI https://doi.org/10.1016/j.jneumeth.2007.03.015
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.
Item Type: | Article |
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Uncontrolled Keywords: | brain-computer interface; brain-machine interface; human-machine interface; hybrid bilonic system; throughput; information transfer rate |
Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science R Medicine > RC Internal medicine > RC0321 Neuroscience. Biological psychiatry. Neuropsychiatry |
Divisions: | Faculty of Science and Health Faculty of Science and Health > Computer Science and Electronic Engineering, School of |
SWORD Depositor: | Unnamed user with email elements@essex.ac.uk |
Depositing User: | Unnamed user with email elements@essex.ac.uk |
Date Deposited: | 04 Apr 2014 14:06 |
Last Modified: | 30 Oct 2024 17:07 |
URI: | http://repository.essex.ac.uk/id/eprint/8793 |
Available files
Filename: Tonet2008DefBMIApplicMergingIntPerfDevicRequir.pdf