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Andrew H. Fagg
Researcher at University of Oklahoma
Publications - 96
Citations - 3720
Andrew H. Fagg is an academic researcher from University of Oklahoma. The author has contributed to research in topics: GRASP & Robot. The author has an hindex of 29, co-authored 92 publications receiving 3563 citations. Previous affiliations of Andrew H. Fagg include University of Southern California & University of Massachusetts Amherst.
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Journal ArticleDOI
Modeling parietal-premotor interactions in primate control of grasping
Andrew H. Fagg,Michael A. Arbib +1 more
TL;DR: The FARS model of the cortical involvement in grasping is presented, a model which focuses on the interaction between anterior intra-parietal area (AIP) and premotor area F5, and demonstrates not only that posterior parietal cortex is a network of interacting subsystems, but also that it functions through a pattern of "cooperative computation" with a multiplicity of other brain regions.
Journal ArticleDOI
Functional Anatomy of Pointing and Grasping in Humans
TL;DR: A cortical system for "pragmatic' manipulation of simple neutral objects is defined in this functional-anatomic study and consistent anatomic landmarks from MRI scans could be identified to locate sensorimotor, ventral SMA, and SII blood flow increases.
Proceedings ArticleDOI
Wearable computers as packet transport mechanisms in highly-partitioned ad-hoc networks
TL;DR: This work proposes a general framework of agent movement and communication in which mobile computers physically carry packets across network partitions, and proposes algorithms that exploit the relative position of stationary devices and non-randonmess in the movement of mobile agents in the network.
Journal ArticleDOI
Dorsal Premotor Cortex and Conditional Movement Selection: A PET Functional Mapping Study
TL;DR: Dorsal premotor cortex and conditional movement selection: a PET functional mapping study and Positron emission tomog...
Journal ArticleDOI
Incorporating Feedback from Multiple Sensory Modalities Enhances Brain–Machine Interface Control
TL;DR: It is demonstrated for the first time that kinesthetic feedback can be used together with vision to significantly improve control of a cursor driven by neural activity of the primary motor cortex and provide the groundwork for augmenting cortically controlled BMIs with multiple forms of natural or surrogate sensory feedback.