Name
Title | ||
|---|---|---|
Predicting hand orientation in reach-to-grasp tasks using neural activities from primary motor cortex. |
Abstract | ||
|---|---|---|
Hand orientation is an important control parameter during reach-to-grasp task. In this paper, we presented a study for predicting hand orientation of non-human primate by decoding neural activities from primary motor cortex (M1). A non-human primate subject was guided to do reaching and grasping tasks meanwhile neural activities were acquired by chronically implanted microelectrode arrays. A Support Vector Machines (SVMs) classifier has been trained for predicting three different hand orientations using these M1 neural activities. Different number of neurons were selected and analyzed; the classifying accuracy was 94.1% with 2 neurons and was 100% with 8 neurons. Data from highly event related neuron units contribute a lot to the accuracy of hand orientation prediction. These results indicate that three different hand orientations can be predicted accurately and effectively before the actual movements occurring with a small number of related neurons in M1. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/EMBC.2014.6943838 | EMBC |
Keywords | Field | DocType |
primary motor cortex,neurophysiology,biomedical electrodes,biomedical measurement,m1 neural activities,medical signal processing,hand orientation prediction,nonparametric statistics,svm,gait analysis,signal classification,brain,reach-to-grasp tasks,microelectrodes,chronically implanted microelectrode arrays,neural activities,support vector machines | Computer vision,GRASP,Computer science,Artificial intelligence,Primary motor cortex | Conference |
Volume | ISSN | Citations |
2014 | 1557-170X | 0 |
PageRank | References | Authors |
0.34 | 3 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Peng Zhang | 1 | 0 | 1.01 |
| Xuan Ma | 2 | 0 | 0.68 |
| Hailong Huang | 3 | 0 | 0.34 |
| Jiping He | 4 | 110 | 17.46 |