title: Detecting and classifying movement-related cortical potentials associated with hand movements in healthy subjects and stroke patients from single-electrode, single-trial EEG.
abstract: OBJECTIVE
  To detect movement intention from executed and imaginary palmar grasps in healthy subjects and attempted executions in stroke patients using one EEG channel. Moreover, movement force and speed were also decoded.
  
  
  METHODS
  Fifteen healthy subjects performed motor execution and imagination of four types of palmar grasps. In addition, five stroke patients attempted to perform the same movements. The movements were detected from the continuous EEG using a single electrode/channel overlying the cortical representation of the hand. Four features were extracted from the EEG signal and classified with a support vector machine (SVM) to decode the level of force and speed associated with the movement. The system performance was evaluated based on both detection and classification.
  
  
  RESULTS
  ∼ 75% of all movements (executed, imaginary and attempted) were detected 100 ms before the onset of the movement. ∼ 60% of the movements were correctly classified according to the intended level of force and speed. When detection and classification were combined, ∼ 45% of the movements were correctly detected and classified in both the healthy and stroke subjects, although the performance was slightly better in healthy subjects.
  
  
  CONCLUSIONS
  The results indicate that it is possible to use a single EEG channel for detecting movement intentions that may be combined with assistive technologies. The simple setup may lead to a smoother transition from laboratory tests to the clinic.
authors: Center for Sensory-Motor Interaction, Department of Health Science and Technology, Aalborg University, Denmark.
authors: Mads
authors: M
authors: Jochumsen
authors: Imran Khan
authors: IK
authors: Niazi
authors: Denise
authors: D
authors: Taylor
authors: Dario
authors: D
authors: Farina
authors: Kim
authors: K
authors: Dremstrup
mesh_terms: D000328:Adult; D000368:Aged; D000465:Algorithms; D062207:Brain-Computer Interfaces; D004569:Electroencephalography / Q000295:instrumentation / Q000379:methods*; D019054:Evoked Potentials, Motor; D005260:Female; D006225:Hand / Q000503:physiopathology*; D006801:Humans; D007092:Imagination; D000069550:Machine Learning; D008297:Male; D008875:Middle Aged; D009044:Motor Cortex / Q000503:physiopathology*; D009068:Movement; D010363:Pattern Recognition, Automated / Q000379:methods; D012016:Reference Values; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D020521:Stroke / Q000150:complications / Q000503:physiopathology*