Title :
An artificial neural network approach to predicting arm movements from ECoG
Author :
Cornwell, A.S. ; Kirsch, R.F. ; Burgess, R.C.
Author_Institution :
Dept. of Biomedical Eng., Case Western Reserve Univ., Cleveland, OH, USA
Abstract :
There are three specific aims. First, demonstrate the practicality of using an artificial neural network based approach to correlate these cortical signals with actual and imagined arm movements. Second, to identify areas of the cortical surface that provide the most useful command information. Third, quantify the information content and information transfer rate of the signals obtained from the subdural grids relative to a set of relevant arm movements. This work presents progress toward these aims.
Keywords :
bioelectric phenomena; biomechanics; neural nets; neurophysiology; arm movement prediction; artificial neural network; cortical signals; electrocorticogram; information content; information transfer rate; Artificial neural networks; Biological neural networks; Biomedical engineering; Electrical stimulation; Electrodes; Nervous system; Neural prosthesis; Neuromuscular stimulation; Signal processing; Sternum; Artificial Neural Networks; Brain Machine Interface; Funtional Electrical Stimulation;
Conference_Titel :
Engineering in Medicine and Biology Society, 2004. IEMBS '04. 26th Annual International Conference of the IEEE
Conference_Location :
San Francisco, CA
Print_ISBN :
0-7803-8439-3
DOI :
10.1109/IEMBS.2004.1404182