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.
authors.forename: Peng
authors.initials: P
authors.lastname: Zhang
authors.forename: Xuan
authors.initials: X
authors.lastname: Ma
authors.forename: Hailong
authors.initials: H
authors.lastname: Huang
authors.forename: Jiping
authors.initials: J
authors.lastname: He
mesh_terms: D000818:Animals; D006225:Hand / Q000502:physiology*; D018737:Hand Strength / Q000502:physiology; D009044:Motor Cortex / Q000502:physiology*; D009949:Orientation / Q000502:physiology*; D011323:Primates / Q000502:physiology*; D011597:Psychomotor Performance / Q000502:physiology*; D060388:Support Vector Machine; D013647:Task Performance and Analysis