title: Reconstructing hand kinematics during reach to grasp movements from electroencephalographic signals.
abstract: With continued research on brain machine interfaces (BMIs), it is now possible to control prosthetic arm position in space to a high degree of accuracy. However, a reliable decoder to infer the dexterous movements of fingers from brain activity during a natural grasping motion is still to be demonstrated. Here, we present a methodology to accurately predict and reconstruct natural hand kinematics from non-invasively recorded scalp electroencephalographic (EEG) signals during object grasping movements. The high performance of our decoder is attributed to a combination of the correct input space (time-domain amplitude modulation of delta-band smoothed EEG signals) and an optimal subset of EEG electrodes selected using a genetic algorithm. Trajectories of the joint angles were reconstructed for metacarpo-phalangeal (MCP) joints of the fingers as well as the carpo-metacarpal (CMC) and MCP joints of the thumb. High decoding accuracy (Pearson's correlation coefficient, r) between the predicted and observed trajectories (r = 0.76 ± 0.01; averaged across joints) indicate that this technique may be suitable for use with a closed-loop real-time BMI to control grasping motion in prosthetics with high degrees of freedom. This demonstrates the first successful decoding of hand pre-shaping kinematics from noninvasive neural signals.
authors.affiliation: Graduate Program in Neuroscience and Cognitive Science, and Department of Kinesiology, University of Maryland, College Park, MD 20742, USA. agashe@umd.edu
authors.forename: Harshavardhan A
authors.initials: HA
authors.lastname: Agashe
authors.forename: José L
authors.initials: JL
authors.lastname: Contreras-Vidal
mesh_terms: D000465:Algorithms; D004569:Electroencephalography / Q000379:methods*; D019054:Evoked Potentials, Motor / Q000502:physiology*; D006225:Hand / Q000502:physiology*; D018737:Hand Strength; D006801:Humans; D009044:Motor Cortex / Q000502:physiology*; D009068:Movement / Q000502:physiology*; D013647:Task Performance and Analysis