title: Decoding hand movement velocity from electroencephalogram signals during a drawing task.
abstract: BACKGROUND
  Decoding neural activities associated with limb movements is the key of motor prosthesis control. So far, most of these studies have been based on invasive approaches. Nevertheless, a few researchers have decoded kinematic parameters of single hand in non-invasive ways such as magnetoencephalogram (MEG) and electroencephalogram (EEG). Regarding these EEG studies, center-out reaching tasks have been employed. Yet whether hand velocity can be decoded using EEG recorded during a self-routed drawing task is unclear.
  
  
  METHODS
  Here we collected whole-scalp EEG data of five subjects during a sequential 4-directional drawing task, and employed spatial filtering algorithms to extract the amplitude and power features of EEG in multiple frequency bands. From these features, we reconstructed hand movement velocity by Kalman filtering and a smoothing algorithm.
  
  
  RESULTS
  The average Pearson correlation coefficients between the measured and the decoded velocities are 0.37 for the horizontal dimension and 0.24 for the vertical dimension. The channels on motor, posterior parietal and occipital areas are most involved for the decoding of hand velocity. By comparing the decoding performance of the features from different frequency bands, we found that not only slow potentials in 0.1-4 Hz band but also oscillatory rhythms in 24-28 Hz band may carry the information of hand velocity.
  
  
  CONCLUSIONS
  These results provide another support to neural control of motor prosthesis based on EEG signals and proper decoding methods.
authors.affiliation: Center for Brain-Computer Interfaces and Brain Information Processing, College of Automation Science and Engineering, South China University of Technology, Wushan Road, Guangzhou, PR China.
authors.forename: Jun
authors.initials: J
authors.lastname: Lv
authors.forename: Yuanqing
authors.initials: Y
authors.lastname: Li
authors.forename: Zhenghui
authors.initials: Z
authors.lastname: Gu
mesh_terms: D000465:Algorithms; D001154:Art; D016002:Discriminant Analysis; D004569:Electroencephalography / Q000379:methods*; D006225:Hand / Q000502:physiology*; D006801:Humans; D008297:Male; D009043:Motor Activity / Q000502:physiology*; D012535:Scalp; D012815:Signal Processing, Computer-Assisted