title: Hand movement decoding by phase-locking low frequency EEG signals.
abstract: Being noninvasive, low-risk and inexpensive, EEG is a promising methodology in the application of human Brain Computer Interface (BCI) to help those with motor dysfunctions. Here we employed a center-out task paradigm to study the decoding of hand velocity in the EEG recording. We tested the hypothesis using a linear regression model and found a significant correlation between velocity and the low-pass filtered EEG signal (<2 Hz). The low-pass filtered EEG was not only tuned to the direction but also phase-locked to the amplitude of velocity. This suggests an EEG form of the neuronal population vector theory, which is considered to encode limb kinematic information, and provides a new method of BCI implementation.
authors.affiliation: Department of Biomedical Engineering, University of Minnesota, Minneapolis, MN 55455, USA. liux0736@umn.edu
authors.forename: Jiaen
authors.initials: J
authors.lastname: Liu
authors.forename: Christopher
authors.initials: C
authors.lastname: Perdoni
authors.forename: Bin
authors.initials: B
authors.lastname: He
mesh_terms: D000328:Adult; D000465:Algorithms; D001696:Biomechanical Phenomena; D001921:Brain / Q000473:pathology*; D004566:Electrodes; D004569:Electroencephalography / Q000379:methods*; D004867:Equipment Design; D005121:Extremities / Q000473:pathology; D005260:Female; D006225:Hand / Q000502:physiology*; D006801:Humans; D008297:Male; D015233:Models, Statistical; D009068:Movement; D009474:Neurons / Q000473:pathology; D012044:Regression Analysis; D012656:Self-Help Devices; D012815:Signal Processing, Computer-Assisted; D013997:Time Factors; D014584:User-Computer Interface