Title :
Discreet Discrete Commands for Assistive and Neuroprosthetic Devices
Author :
Foldes, Stephen T. ; Taylor, Dawn M.
Author_Institution :
Dept. of Biomed. Eng., Case Western Reserve Univ., Cleveland, OH, USA
fDate :
6/1/2010 12:00:00 AM
Abstract :
Many new assistive devices are available for individuals paralyzed below the neck due to spinal cord injury. Severely paralyzed individuals must be able to command their complex assistive devices using remaining activity from the neck up. Electromyographic (EMG) sensors enable people to use contractions of head and neck muscles to generate multiple proportional command signals. Electroencephalographic (EEG) signals can also be used to generate commands for assistive device control by conveying information about imagined or attempted movements. Fully-implanted wireless biopotential detection systems are now being developed to reliably detect EMGs, EEGs, or a mixture of the two from recording electrodes implanted just under the skin or scalp thus eliminating the need for externally worn hardware on the head or face. This present study shows how novel patterns of jaw muscle contractions, detected via biopotential sensors on the scalp surface or implanted just under the scalp, can be used to generate reliable discrete EMG commands, which can be differentiated from patterns generated during normal activities, such as chewing. These jaw contractions can be detected with sensors already in place to detect other muscle- or brain-based command signals thus adding to the functionality of current device control systems.
Keywords :
biomedical electrodes; brain-computer interfaces; electroencephalography; electromyography; handicapped aids; medical signal processing; neurophysiology; prosthetics; EEG signal; biopotential sensors; brain-based command signals; complex assistive devices; discreet discrete commands; electroencephalographic signal; electromyographic sensors; fully-implanted wireless biopotential detection; head and neck muscles; multiple proportional command signal; muscle-based command signals; neuroprosthetic devices; recording electrodes; scalp; spinal cord injury; Brain–computer interface (BCI); classification; electroencephalography (EEG); electromyography (EMG); functional electrical stimulation (FES); Algorithms; Calibration; Cues; Data Interpretation, Statistical; Discriminant Analysis; Electroencephalography; Electromyography; Humans; Jaw; Mastication; Muscle, Skeletal; Neck Muscles; Neural Networks (Computer); Prostheses and Implants; Psychomotor Performance; Reproducibility of Results; Scalp; Self-Help Devices; Spinal Cord Injuries;
Journal_Title :
Neural Systems and Rehabilitation Engineering, IEEE Transactions on
DOI :
10.1109/TNSRE.2009.2033428