title: Detection and classification of movement-related cortical potentials associated with task force and speed.
abstract: OBJECTIVE
  In this study, the objective was to detect movement intentions and extract different levels of force and speed of the intended movement from scalp electroencephalography (EEG). We then estimated the performance of the closed loop system.
  
  
  METHODS
  Cued movements were detected from continuous EEG recordings using a template of the initial phase of the movement-related cortical potential in 12 healthy subjects. The temporal features, extracted from the movement intention, were classified with an optimized support vector machine. The system performance was evaluated when combining detection with classification.
  
  
  RESULTS
  The system detected 81% of the movements and correctly classified 75 ± 9% and 80 ± 10% of these at the point of detection when varying the force and speed, respectively. When the detector was combined with the classifier, the system detected and correctly classified 64 ± 13% and 67 ± 13% of these movements. The system detected and incorrectly classified 21 ± 7% and 16 ± 9% of the movements. The movements were detected 317 ± 73 ms before the movement onset.
  
  
  CONCLUSIONS
  The results indicate that it is possible to detect movement intentions with limited latencies, and extract and classify different levels of force and speed, which may be combined with assistive technologies for patient-driven neurorehabilitation.
authors.affiliation: Department of Health Science and Technology, Center for Sensory-Motor Interaction, Aalborg University, Fredrik Bajers vej 7D, D2-212, 9220, Aalborg, Denmark.
authors.forename: Mads
authors.initials: M
authors.lastname: Jochumsen
authors.forename: Imran Khan
authors.initials: IK
authors.lastname: Niazi
authors.forename: Natalie
authors.initials: N
authors.lastname: Mrachacz-Kersting
authors.forename: Dario
authors.initials: D
authors.lastname: Farina
authors.forename: Kim
authors.initials: K
authors.lastname: Dremstrup
mesh_terms: D000328:Adult; D000704:Analysis of Variance; D002540:Cerebral Cortex / Q000502:physiology*; D003463:Cues; D004569:Electroencephalography / Q000145:classification* / Q000706:statistics & numerical data*; D004585:Electrooculography; D005260:Female; D006801:Humans; D008297:Male; D009068:Movement / Q000502:physiology*; D009119:Muscle Contraction / Q000502:physiology; D010465:Perception / Q000502:physiology*; D012372:ROC Curve; D015203:Reproducibility of Results; D012815:Signal Processing, Computer-Assisted; D060388:Support Vector Machine; D055815:Young Adult