Detection of Movement Related Cortical Potentials from Single Trial EEG Signals

Abstract

Movement-Related Cortical Potentials (MRCP) are signals that begin to appear approximately two seconds before the onset of voluntary movements and can be recorded with EEG. MRCP is an important sign that the movement will begin. Determining the movement intention before the action is extremely important information especially for real-time BCI systems. By using MRCP, Brain-Computer Interface (BCI) users' movement intention can be determined prior to the move and this sign can be used as a control signal. In this study, it was aimed to determine the movement and resting states with high accuracy with MRCP signals. Furthermore, the effects of filter cutoff frequencies, number of electrodes, and MRCP time interval window on the success of distinguishing movement/resting states in the preprocessing stage were investigated. For this purpose, Katz fractal dimension and nonlinear support vector machine methods were used in the feature extraction and classification stages, respectively. The proposed method was tested on the attempted hand and arm movements dataset containing EEG signals of 10 participants with spinal cord injury. Katz fractal dimension and support vector machines methods can determine movement and resting states with an average of 96.47% accuracy using MRCP signals. If the number of electrodes to be used in signal analysis was 3, 9 and 61, the obtained accuracy rates were determined as 83.71%, 90.67%, and 96.47%, respectively. The experimental results also showed that the filter cutoff frequencies used in the preprocessing had a significant effect on the accuracy.