An automatic method to generate ensemble averages of movement-related potentials for individuals with spinal cord injuries

TitleAn automatic method to generate ensemble averages of movement-related potentials for individuals with spinal cord injuries
Publication TypeConference Paper
Year of Publication2004
AuthorsBashashati, A., S. G. Mason, R. K. Ward, and G. E. Birch
Conference NameEngineering in Medicine and Biology Society, 2004. IEMBS '04. 26th Annual International Conference of the IEEE
Pagination4529 -4532
Date Publishedsep.
Keywordsbioelectric potentials, biomechanics, electroencephalogram, electroencephalography, ensemble averages, event related potentials, feature vectors, medical signal processing, movement-related potentials, muscle, muscle activity, neurophysiology, Parzen method, probability, probability density distribution, Spinal Cord Injuries, switch activation
Abstract

Ensemble averaging of the electroencephalogram is known to be a good tool for characterizing various event related potentials. An important part of ensemble averaging is to know the time reference that the signals should be averaged. In able-bodied individuals the muscle activity or switch activation is used to time-lock the averages. In people with spinal cord injuries who lack the ability to produce muscle activity, the expected time of the attempted movement based on an external cue can be used. This time is not accurate and can result in poor ensemble averages. A method that automatically detects the onset of the movement related potentials and use this knowledge to time-lock the averages is introduced. This method is based on the estimation of the probability density distribution of the feature vectors related to spontaneous EEG. To estimate the probability density function Parzen's method is used which is known to be as the most accurate method when large population of data is available. Preliminary experiments demonstrate the feasibility of the proposed method and show that the proposed method could generate ensemble averages closer to the averages with muscle activity knowledge than the method based on an external cue.

URLhttp://dx.doi.org/10.1109/IEMBS.2004.1404257
DOI10.1109/IEMBS.2004.1404257

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