Name
Abstract | ||
|---|---|---|
Motor cortical local field potentials (LFPs) have been successfully used to decode both kinematics and kinetics of arm movement. For future clinically viable prostheses, however, brain activity decoders will have to generalize well under a wide spectrum of behavioral conditions. This property has not yet been demonstrated clearly. Here, we provide evidence for the first time, that an LFP-based electromyogram (EMG) decoder can generalize reasonably well across two different types of behavior. We implanted intracortical microelectrode arrays in the primary motor (M1) and ventral pre-motor (PMv) cortices of a rhesus macaque, and recorded LFP and EMG activity from arm and hand muscles of the contralateral forelimb during a two-dimensional (2-D) centre-out isometric wrist torque task (TT), and during free reach and grasp behavior (FB). Selected temporal and spectral features of the LFP signals were used to train EMG decoders using data from both types of behavior separately. We assessed the decoding performance for both within-and across-task cases. The average achieved generalization score was 65 +/- 20%, while in many cases individual scores reached 100%. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/EMBC.2014.6943917 | 2014 36TH ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY (EMBC) |
Keywords | Field | DocType |
feature extraction,local field potentials,decoding,kinematics,learning artificial intelligence,neuroscience,neurophysiology,biomechanics,feature selection,microelectrodes | Kinematics,Computer science,Brain activity and meditation,Artificial intelligence,Forelimb,Biomechanics,Isometric exercise,Computer vision,Neurophysiology,Pattern recognition,Electromyography,Speech recognition,Local field potential | Conference |
Volume | ISSN | Citations |
2014 | 1557-170X | 0 |
PageRank | References | Authors |
0.34 | 3 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Agamemnon Krasoulis | 1 | 3 | 2.86 |
| Thomas M Hall | 2 | 0 | 0.34 |
| Sethu Vijayakumar | 3 | 1465 | 144.22 |
| A Jackson | 4 | 30 | 4.43 |
| Kianoush Nazarpour | 5 | 3 | 1.27 |