Multimodal neuroelectric interface development.
We are developing electromyographic and electroencephalographic methods, which draw control signals for human-computer interfaces from the human nervous system. We have made progress in four areas: 1) real-time pattern recognition algorithms for decoding sequences of forearm muscle activity associated with control gestures; 2) signal-processing strategies for computer interfaces using electroencephalogram (EEG) signals; 3) a flexible computation framework for neuroelectric interface research; and d) noncontact sensors, which measure electromyogram or EEG signals without resistive contact to the body.
Trejo
Leonard J
LJ
NASA Ames Research Center, Moffett Field, CA 94035, USA. ltrejo@mail.arc.nasa.gov
Wheeler
Kevin R
KR
Jorgensen
Charles C
CC
Rosipal
Roman
R
Clanton
Sam T
ST
Matthews
Bryan
B
Hibbs
Andrew D
AD
Matthews
Robert
R
Krupka
Michael
M
D000328:Adult; D000401:Aircraft; D000465:Algorithms; D001921:Brain / Q000502:physiology; D003143:Communication Devices for People with Disabilities; D003196:Computer Graphics; D004569:Electroencephalography / Q000379:methods*; D004576:Electromyography / Q000379:methods*; D005071:Evoked Potentials / Q000502:physiology*; D005385:Fingers / Q000502:physiology; D005542:Forearm / Q000502:physiology; D006801:Humans; D008297:Male; D008875:Middle Aged; D009068:Movement / Q000502:physiology; D014584:User-Computer Interface