Name
Title | ||
|---|---|---|
Extraction Of Control Signals From A Mixture Of Source Activity In The Peripheral Nerve |
Abstract | ||
|---|---|---|
Extracting physiological signals to control external devices such as prosthetics is a field of research that offers great hope for patients suffering from disabilities. In this paper, we present an algorithm for isolating control signals from peripheral nerve cuff recordings. The algorithm is able to extract individual control signals from a mixture of source signal activity while maximizing SNR and minimizing cross-talk between the control signals. Based on fast independent component analysis FICA and an adaptation of Champagne, the proposed algorithm is tested against previously published results obtained using beamforming techniques in an acute preparation of rabbits. Preliminary results demonstrate an improvement in performance. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1109/EMBC.2012.6346588 | 2012 ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY (EMBC) |
Keywords | Field | DocType |
deconvolution,neurophysiology,independent component analysis | Peripheral,Beamforming,Computer vision,Neurophysiology,Computer science,Nerve cuff,Deconvolution,Electronic engineering,Speech recognition,Independent component analysis,Artificial intelligence | Conference |
Volume | ISSN | Citations |
2012 | 1557-170X | 0 |
PageRank | References | Authors |
0.34 | 2 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Y Tang | 1 | 0 | 0.34 |
| Brian Wodlinger | 2 | 1 | 1.72 |
| D M Durand | 3 | 0 | 0.34 |