Paper Info
Title
Bimodal Brain-Machine Interface For Motor Control Of Robotic Prosthetic
Abstract
We are working on mapping multi-channel neural spike data, recorded from multiple cortical areas of an owl monkey, to corresponding 3d monkey arm positions. In earlier work on this mapping task, we observed that continuous fiunction approximators (such as artificial neural networks) have difficulty in jointly estimating 3d arm positions for two distinct cases - namely, when the monkey's arm is stationary and when it is moving. Therefore, we propose a multiple-model approach that first classifies neural spike data into two classes, corresponding to two states of the moneky's arm: (1) stationary and (2) moving. Then, the output of this classifier is used as a gating mechanism for subsequent continuous models, with one model per class. In this paper, we first motivate and discuss, our approach. Next, we present encouraging results for the classifier stage, based on hidden Markov models (HMMs), and also for the entire bimodal mapping system. Finally, we conclude with a discussion of the results and suggest future avenues of research.
Year
DOI
Venue
2003
10.1109/IROS.2003.1249716
IROS 2003: PROCEEDINGS OF THE 2003 IEEE/RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS, VOLS 1-4
Keywords
Field
DocType
neural nets,neurophysiology,user interfaces,function approximation,artificial neural network,hidden markov models,motor control,brain machine interface,hidden markov model,biocontrol
Computer vision,Continuous function,Neurophysiology,Pattern recognition,Computer science,Brain–computer interface,Motor control,Artificial intelligence,Classifier (linguistics),Artificial neural network,Hidden Markov model,User interface
Conference
Citations 
PageRank 
References 
4
1.15
2
Authors
7
Name
Order
Citations
PageRank
Shalom Darmanjian1164.00
Sung-Phil Kim211825.14
Michael C. Nechyba310314.60
Scott Morrison441.15
José C Príncipe567358.97
Johan Wessberg69516.51
Miguel A. L. Nicolelis715034.62