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

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.