Title
A study of predicting movement intentions in various spatial reaching tasks from M1 neural activities.
Abstract
Understanding how M1 neurons innervate flexible coordinated upper limb reaching and grasping is important for BMI systems that attempt to reproduce the same actions. In this paper, we presented a study for exploring M1 neuronal activities while a non-human primate subject was guided to finish different visual cued spatial reaching and grasping tasks. By applying various configurations of target objects in the experiment paradigm, we can make thorough investigations on how neural ensemble activities represented subjects' intentions in different task-related time stages when target objects' properties, including shape, position, orientation, varied. Extracted neuron units were categorized according to their event related attributes. The prediction of subjects' movement intentions was completed with a support vector machine (SVM) based method and a simulated on-line test was performed to illustrate the validation of the proposed method. The results showed that, by M1 neural ensemble spike train signals, correct prediction of subject's intentions can be generated in certain time intervals before the movements were actually executed.
Year
DOI
Venue
2014
10.1109/EMBC.2014.6944171
EMBC
Keywords
Field
DocType
spatial reaching tasks,visual cued spatial reaching,subject movement intentions,m1 neuron innervate flexible coordinated upper limb grasping,bmi systems,task-related time stages,visual cued spatial grasping,neurophysiology,user interfaces,m1 neural activities,medical signal processing,m1 neural ensemble spike train signals,m1 neuron innervate flexible coordinated upper limb reaching,svm,movement intention prediction,support vector machine,neuron units,bioelectric phenomena,spike train signals,simulated on-line test,nonhuman primate subject,support vector machines
Computer science,Artificial intelligence
Conference
Volume
ISSN
Citations 
2014
1557-170X
0
PageRank 
References 
Authors
0.34
2
4
Name
Order
Citations
PageRank
Xuan Ma100.68
Peng Zhang201.01
Hailong Huang300.34
Jiping He411017.46