TY - GEN
T1 - A study of predicting movement intentions in various spatial reaching tasks from M1 neural activities
AU - Ma, Xuan
AU - Zhang, Peng
AU - Huang, Hailong
AU - He, Jiping
N1 - Publisher Copyright:
© 2014 IEEE.
PY - 2014/11/2
Y1 - 2014/11/2
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84929497687&partnerID=8YFLogxK
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U2 - 10.1109/EMBC.2014.6944171
DO - 10.1109/EMBC.2014.6944171
M3 - Conference contribution
C2 - 25570539
AN - SCOPUS:84929497687
T3 - 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2014
SP - 2666
EP - 2669
BT - 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2014
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2014
Y2 - 26 August 2014 through 30 August 2014
ER -