Use my Search Websuite to scan PubMed, PMCentral, Journal Hosts and Journal Archives, FullText. Kick-your-searchterm to multiple Engines kick-your-query now !>

A dictionary by aggregated review articles of nephrology, medicine and the life sciences Your one-stop-run pathway from word to the immediate pdf of peer-reviewed on-topic knowledge.

10.1088/1741-2560/13/2/026017 http://scihub22266oqcxt.onion/10.1088/1741-2560/13/2/026017 C4875758!4875758!26863276     free     free     free Deprecated: Implicit conversion from float 231.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 231.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 231.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 231.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 231.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 231.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 265.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 265.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       J+Neural+Eng 2016 ; 13 (2): 026017 Nephropedia Template TP {{tp|p=26863276|t=2016. Individual Finger Control of the Modular Prosthetic Limb using High-Density Electrocorticography in a Human Subject |pdf=|usr=}}{{26863276}} gab.com Text Individual Finger Control of the Modular Prosthetic Limb using High-Density Electrocorticography in a Human Subject JO: J Neural Eng http://www.kidney.de/pget.php?&tw=1&pmid=26863276 #FOAvip Objective: We used native sensorimotor representations of fingers in a brain-machine interface to achieve immediate online control of individual prosthetic fingers. Approach: Using high gamma responses recorded with a high-density ECoG array, we rapidly mapped the functional anatomy of cued finger movements. We used these cortical maps to select ECoG electrodes for a hierarchical linear discriminant analysis classification scheme to predict: 1) if any finger was moving, and, if so, 2) which digit was moving. To account for sensory feedback, we also mapped the spatiotemporal activation elicited by vibrotactile stimulation. Finally, we used this prediction framework to provide immediate online control over individual fingers of the Johns Hopkins University Applied Physics Laboratory (JHU/APL) Modular Prosthetic Limb (MPL). Main Results: The balanced classification accuracy for detection of movements during the online control session was 92% (chance: 50%). At the onset of movement, finger classification was 76% (chance: 20%), and 88% (chance: 25%) if the pinky and ring finger movements were coupled. Balanced accuracy of fully flexing the cued finger was 64%, and 77% had we combined pinky and ring commands. Offline decoding yielded a peak finger decoding accuracy of 96.5% (chance: 20%) when using an optimized selection of electrodes. Offline analysis demonstrated significant finger-specific activations throughout sensorimotor cortex. Activations either prior to movement onset or during sensory feedback led to discriminable finger control. Significance: Our results demonstrate the ability of ECoG-based BMIs to leverage the native functional anatomy of sensorimotor cortical populations to immediately control individual finger movements in real time. Twit Text FOAVip Individual Finger Control of the Modular Prosthetic Limb using High-Density Electrocorticography in a Human Subject ????J Neural Eng http://www.kidney.de/pget.php?&tw=1&pmid=26863276 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=26863276 #FOAvip English Wikipedia <ref>{{Cite pmid|26863276}}</ref> Individual Finger Control of the Modular Prosthetic Limb using High-Density Electrocorticography in a Human Subject #MMPMID26863276 Hotson G; McMullen DP; Fifer MS; Johannes MS; Katyal KD; Para MP; Armiger R; Anderson WS; Thakor NV; Wester BA; Crone NE J Neural Eng 2016[Apr]; 13 (2): 026017 PMID26863276show ga Objective: We used native sensorimotor representations of fingers in a brain-machine interface to achieve immediate online control of individual prosthetic fingers. Approach: Using high gamma responses recorded with a high-density ECoG array, we rapidly mapped the functional anatomy of cued finger movements. We used these cortical maps to select ECoG electrodes for a hierarchical linear discriminant analysis classification scheme to predict: 1) if any finger was moving, and, if so, 2) which digit was moving. To account for sensory feedback, we also mapped the spatiotemporal activation elicited by vibrotactile stimulation. Finally, we used this prediction framework to provide immediate online control over individual fingers of the Johns Hopkins University Applied Physics Laboratory (JHU/APL) Modular Prosthetic Limb (MPL). Main Results: The balanced classification accuracy for detection of movements during the online control session was 92% (chance: 50%). At the onset of movement, finger classification was 76% (chance: 20%), and 88% (chance: 25%) if the pinky and ring finger movements were coupled. Balanced accuracy of fully flexing the cued finger was 64%, and 77% had we combined pinky and ring commands. Offline decoding yielded a peak finger decoding accuracy of 96.5% (chance: 20%) when using an optimized selection of electrodes. Offline analysis demonstrated significant finger-specific activations throughout sensorimotor cortex. Activations either prior to movement onset or during sensory feedback led to discriminable finger control. Significance: Our results demonstrate the ability of ECoG-based BMIs to leverage the native functional anatomy of sensorimotor cortical populations to immediately control individual finger movements in real time. äIndividual Finger Control of the Modular Prosthetic Limb using High-De(epmc).pdf DeepDyve Pubget Overpricing