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10.1007/s10827-015-0566-4 http://scihub22266oqcxt.onion/10.1007/s10827-015-0566-4 C4568020!4568020!26142906     free     free     free Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       J+Comput+Neurosci 2015 ; 39 (2): 107-18 Nephropedia Template TP {{tp|p=26142906|t=2015. Recasting brain-machine interface design from a physical control system perspective |pdf=|usr=}}{{26142906}} gab.com Text Recasting brain-machine interface design from a physical control system perspective JO: J Comput Neurosci http://www.kidney.de/pget.php?&tw=1&pmid=26142906 #FOAvip With the goal of improving the quality of life for people suffering from various motor control disorders, brain-machine interfaces provide direct neural control of prosthetic devices by translating neural signals into control signals. These systems act by reading motor intent signals directly from the brain and using them to control, for example, the movement of a cursor on a computer screen. Over the past two decades, much attention has been devoted to the decoding problem: how should recorded neural activity be translated into the movement of the cursor? Most approaches have focused on this problem from an estimation standpoint, i.e., decoders are designed to return the best estimate of motor intent possible, under various sets of assumptions about how the recorded neural signals represent motor intent. Here we recast the decoder design problem from a physical control system perspective, and investigate how various classes of decoders lead to different types of physical systems for the subject to control. This framework leads to new interpretations of why certain types of decoders have been shown to perform better than others. These results have implications for understanding how motor neurons are recruited to perform various tasks, and may lend insight into the brain?s ability to conceptualize artificial systems. Twit Text FOAVip Recasting brain-machine interface design from a physical control system perspective ????J Comput Neurosci http://www.kidney.de/pget.php?&tw=1&pmid=26142906 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=26142906 #FOAvip English Wikipedia <ref>{{Cite pmid|26142906}}</ref> Recasting brain-machine interface design from a physical control system perspective #MMPMID26142906 Zhang Y; Chase SM J Comput Neurosci 2015[]; 39 (2): 107-18 PMID26142906show ga With the goal of improving the quality of life for people suffering from various motor control disorders, brain-machine interfaces provide direct neural control of prosthetic devices by translating neural signals into control signals. These systems act by reading motor intent signals directly from the brain and using them to control, for example, the movement of a cursor on a computer screen. Over the past two decades, much attention has been devoted to the decoding problem: how should recorded neural activity be translated into the movement of the cursor? Most approaches have focused on this problem from an estimation standpoint, i.e., decoders are designed to return the best estimate of motor intent possible, under various sets of assumptions about how the recorded neural signals represent motor intent. Here we recast the decoder design problem from a physical control system perspective, and investigate how various classes of decoders lead to different types of physical systems for the subject to control. This framework leads to new interpretations of why certain types of decoders have been shown to perform better than others. These results have implications for understanding how motor neurons are recruited to perform various tasks, and may lend insight into the brain?s ability to conceptualize artificial systems. äRecasting brain-machine interface design from a physical control syste(epmc).pdf DeepDyve Pubget Overpricing