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10.1088/1741-2560/11/3/035015 http://scihub22266oqcxt.onion/10.1088/1741-2560/11/3/035015 C4097188!4097188!24836588     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 231.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       J+Neural+Eng 2014 ; 11 (3): 035015 Nephropedia Template TP {{tp|p=24836588|t=2014. Direct classification of all American English phonemes using signals from functional speech motor cortex |pdf=|usr=}}{{24836588}} gab.com Text Direct classification of all American English phonemes using signals from functional speech motor cortex JO: J Neural Eng http://www.kidney.de/pget.php?&tw=1&pmid=24836588 #FOAvip Although brain-computer interfaces (BCIs) can be used in several different ways to restore communication, communicative BCI has not approached the rate or success of natural human speech. Electrocorticography (ECoG) has precise spatiotemporal resolution that enables recording of brain activity that is distributed over a wide area of cortex, such as during speech production. In this study, we investigated words that span the entire set of phonemes in the General American accent using ECoG with 4 subjects. We classified phonemes with up to 36% accuracy when classifying all phonemes and up to 63% accuracy for a single phoneme. Further, misclassified phonemes follow articulation organization described in phonology literature, aiding classification of whole words. Precise temporal alignment to phoneme onset was crucial for classification success. We identified specific spatiotemporal features that aid classification, which could guide future applications. Word identification was equivalent to information transfer rates as high as 3.0 bits/s (33.6 words/min), supporting pursuit of speech articulation for BCI control. Twit Text FOAVip Direct classification of all American English phonemes using signals from functional speech motor cortex ????J Neural Eng http://www.kidney.de/pget.php?&tw=1&pmid=24836588 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=24836588 #FOAvip English Wikipedia <ref>{{Cite pmid|24836588}}</ref> Direct classification of all American English phonemes using signals from functional speech motor cortex #MMPMID24836588 Mugler EM; Patton JL; Flint RD; Wright ZA; Schuele SU; Rosenow J; Shih JJ; Krusienski DJ; Slutzky MW J Neural Eng 2014[Jun]; 11 (3): 035015 PMID24836588show ga Although brain-computer interfaces (BCIs) can be used in several different ways to restore communication, communicative BCI has not approached the rate or success of natural human speech. Electrocorticography (ECoG) has precise spatiotemporal resolution that enables recording of brain activity that is distributed over a wide area of cortex, such as during speech production. In this study, we investigated words that span the entire set of phonemes in the General American accent using ECoG with 4 subjects. We classified phonemes with up to 36% accuracy when classifying all phonemes and up to 63% accuracy for a single phoneme. Further, misclassified phonemes follow articulation organization described in phonology literature, aiding classification of whole words. Precise temporal alignment to phoneme onset was crucial for classification success. We identified specific spatiotemporal features that aid classification, which could guide future applications. Word identification was equivalent to information transfer rates as high as 3.0 bits/s (33.6 words/min), supporting pursuit of speech articulation for BCI control. äDirect classification of all American English phonemes using signals f(epmc).pdf DeepDyve Pubget Overpricing