Reconstruction of reaching movement trajectories using electrocorticographic signals in humans

• Published in: PloS one 2017-09, Vol.12 (9), p.e0182542-e0182542
• Main Authors: Talakoub, Omid, Marquez-Chin, Cesar, Popovic, Milos R, Navarro, Jessie, Fonoff, Erich T, Hamani, Clement, Wong, Willy
• Format: Article
• Language: English
• Subjects: Adult; Arm; Arm - physiology; Biology and Life Sciences; Biomechanical Phenomena; Biomedical engineering; Biomedical materials; Brain; Brain-Computer Interfaces; Computer engineering; Cortex (motor); Electrocorticography; Electrodes; Electrodes, Implanted; Electroencephalography; Electromyography; Engineering and Technology; Female; Frequencies; Health care networks; Hospitals; Humans; Kinematics; Laboratories; Linear Models; Male; Medicine and Health Sciences; Middle Aged; Motor cortex; Motor Cortex - physiology; Movement; Movement - physiology; Neurosurgery; Physical Sciences; Physiological aspects; Prostheses; Regression models; Rehabilitation; Research and Analysis Methods; Robotics; Studies; Velocity
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0182542

In this study, we used electrocorticographic (ECoG) signals to extract the onset of arm movement as well as the velocity of the hand as a function of time. ECoG recordings were obtained from three individuals while they performed reaching tasks in the left, right and forward directions. The ECoG electrodes were placed over the motor cortex contralateral to the moving arm. Movement onset was detected from gamma activity with near perfect accuracy (> 98%), and a multiple linear regression model was used to predict the trajectory of the reaching task in three-dimensional space with an accuracy exceeding 85%. An adaptive selection of frequency bands was used for movement classification and prediction. This demonstrates the efficacy of developing a real-time brain-machine interface for arm movements with as few as eight ECoG electrodes.