OBJECTIVE: Paralyzed patients may benefit from restoration of movement afforded by prosthetics controlled by electrocorticography (ECoG). Although ECoG shows promising results in human volunteers, it is unclear whether ECoG signals recorded from chronically paralyzed patients provide sufficient motor information, and if they do, whether they can be applied to control a prosthetic. METHODS: We recorded ECoG signals from sensorimotor cortices of 12 patients while they executed or attempted to execute 3 to 5 simple hand and elbow movements. Sensorimotor function was severely impaired in 3 patients due to peripheral nervous system lesion or amputation, moderately impaired due to central nervous system lesions sparing the cortex in 4 patients, and normal in 5 patients. Time frequency and decoding analyses were performed with the patients' ECoG signals. RESULTS: In all patients, the high gamma power (80-150 Hz) of the ECoG signals during movements was clearly responsive to movement types and provided the best information for classifying different movement types. The classification performance was significantly better than chance in all patients, although differences between ECoG power modulations during different movement types were significantly less in patients with severely impaired motor function. In the impaired patients, cortical representations tended to overlap each other. Finally, using the classification method in real time, a moderately impaired patient and 3 nonparalyzed patients successfully controlled a prosthetic arm. INTERPRETATION: ECoG signals appear useful for prosthetic arm control and may provide clinically feasible motor restoration for patients with paralysis but no injury of the sensorimotor cortex.
OBJECTIVE:Paralyzedpatients may benefit from restoration of movement afforded by prosthetics controlled by electrocorticography (ECoG). Although ECoG shows promising results in human volunteers, it is unclear whether ECoG signals recorded from chronically paralyzedpatients provide sufficient motor information, and if they do, whether they can be applied to control a prosthetic. METHODS: We recorded ECoG signals from sensorimotor cortices of 12 patients while they executed or attempted to execute 3 to 5 simple hand and elbow movements. Sensorimotor function was severely impaired in 3 patients due to peripheral nervous system lesion or amputation, moderately impaired due to central nervous system lesions sparing the cortex in 4 patients, and normal in 5 patients. Time frequency and decoding analyses were performed with the patients' ECoG signals. RESULTS: In all patients, the high gamma power (80-150 Hz) of the ECoG signals during movements was clearly responsive to movement types and provided the best information for classifying different movement types. The classification performance was significantly better than chance in all patients, although differences between ECoG power modulations during different movement types were significantly less in patients with severely impaired motor function. In the impaired patients, cortical representations tended to overlap each other. Finally, using the classification method in real time, a moderately impaired patient and 3 nonparalyzed patients successfully controlled a prosthetic arm. INTERPRETATION: ECoG signals appear useful for prosthetic arm control and may provide clinically feasible motor restoration for patients with paralysis but no injury of the sensorimotor cortex.
Authors: Thomas J Oxley; Nicholas L Opie; Sam E John; Gil S Rind; Stephen M Ronayne; Tracey L Wheeler; Jack W Judy; Alan J McDonald; Anthony Dornom; Timothy J H Lovell; Christopher Steward; David J Garrett; Bradford A Moffat; Elaine H Lui; Nawaf Yassi; Bruce C V Campbell; Yan T Wong; Kate E Fox; Ewan S Nurse; Iwan E Bennett; Sébastien H Bauquier; Kishan A Liyanage; Nicole R van der Nagel; Piero Perucca; Arman Ahnood; Katherine P Gill; Bernard Yan; Leonid Churilov; Christopher R French; Patricia M Desmond; Malcolm K Horne; Lynette Kiers; Steven Prawer; Stephen M Davis; Anthony N Burkitt; Peter J Mitchell; David B Grayden; Clive N May; Terence J O'Brien Journal: Nat Biotechnol Date: 2016-02-08 Impact factor: 54.908
Authors: Tessy M Thomas; Daniel N Candrea; Matthew S Fifer; David P McMullen; William S Anderson; Nitish V Thakor; Nathan E Crone Journal: IEEE Trans Neural Syst Rehabil Eng Date: 2019-01-07 Impact factor: 3.802
Authors: Alan D Degenhart; James Eles; Richard Dum; Jessica L Mischel; Ivan Smalianchuk; Bridget Endler; Robin C Ashmore; Elizabeth C Tyler-Kabara; Nicholas G Hatsopoulos; Wei Wang; Aaron P Batista; X Tracy Cui Journal: J Neural Eng Date: 2016-06-28 Impact factor: 5.379
Authors: David P McMullen; Guy Hotson; Kapil D Katyal; Brock A Wester; Matthew S Fifer; Timothy G McGee; Andrew Harris; Matthew S Johannes; R Jacob Vogelstein; Alan D Ravitz; William S Anderson; Nitish V Thakor; Nathan E Crone Journal: IEEE Trans Neural Syst Rehabil Eng Date: 2013-12-12 Impact factor: 3.802
Authors: Jennifer L Collinger; Michael A Kryger; Richard Barbara; Timothy Betler; Kristen Bowsher; Elke H P Brown; Samuel T Clanton; Alan D Degenhart; Stephen T Foldes; Robert A Gaunt; Ferenc E Gyulai; Elizabeth A Harchick; Deborah Harrington; John B Helder; Timothy Hemmes; Matthew S Johannes; Kapil D Katyal; Geoffrey S F Ling; Angus J C McMorland; Karina Palko; Matthew P Para; Janet Scheuermann; Andrew B Schwartz; Elizabeth R Skidmore; Florian Solzbacher; Anita V Srikameswaran; Dennis P Swanson; Scott Swetz; Elizabeth C Tyler-Kabara; Meel Velliste; Wei Wang; Douglas J Weber; Brian Wodlinger; Michael L Boninger Journal: Clin Transl Sci Date: 2013-08-27 Impact factor: 4.689
Authors: Jane E Huggins; Christoph Guger; Brendan Allison; Charles W Anderson; Aaron Batista; Anne-Marie A-M Brouwer; Clemens Brunner; Ricardo Chavarriaga; Melanie Fried-Oken; Aysegul Gunduz; Disha Gupta; Andrea Kübler; Robert Leeb; Fabien Lotte; Lee E Miller; Gernot Müller-Putz; Tomasz Rutkowski; Michael Tangermann; David Edward Thompson Journal: Brain Comput Interfaces (Abingdon) Date: 2014-01