Classifying directions in continuous arm movement from EEG signals

EEG based upper limb rehabilitation has limitation on the control commands of neuro-prosthetics cannot deal with human's real movements. To resolve this problem, it is important to know about neural correlation of the directions of arm movement. Previous studies classified the directions of arm movement, using center-out task, only including y-z-axis movement. In this research, 4 subjects participated in experiment and the movement of their right arm in infinity shape (∞) divided into six part of symbol. Moreover, we used Common Spatial Pattern (CSP) algorithm to extract finer feature of EEG signal and Linear Discriminant Analysis (LDA) method to classify directions of movement. The result states that, average of classification accuracy was 74% and standard derivation was 0.08. In the topographical map at the center of infinity shape, we could observe the divided image of left and right side of the brain and FC3, F7 and C3 channels included most information about directions of movement. By the result of this study, we can confirm the possibility of controlling neuro-prosthetics and evidence of neurological basis of the arm movement.