Motor imagery classification by means of source analysis for brain-computer interface applications

J Neural Eng. 2004 Sep;1(3):135-41. doi: 10.1088/1741-2560/1/3/002. Epub 2004 Aug 31.


We report a pilot study of performing classification of motor imagery for brain-computer interface applications, by means of source analysis of scalp-recorded EEGs. Independent component analysis (ICA) was used as a spatio-temporal filter extracting signal components relevant to left or right motor imagery (MI) tasks. Source analysis methods including equivalent dipole analysis and cortical current density imaging were applied to reconstruct equivalent neural sources corresponding to MI, and classification was performed based on the inverse solutions. The classification was considered correct if the equivalent source was found over the motor cortex in the corresponding hemisphere. A classification rate of about 80% was achieved in the human subject studied using both the equivalent dipole analysis and the cortical current density imaging analysis. The present promising results suggest that the source analysis approach could manifest a clearer picture on the cortical activity, and thus facilitate the classification of MI tasks from scalp EEGs.

Publication types

  • Evaluation Study
  • Research Support, U.S. Gov't, Non-P.H.S.
  • Research Support, U.S. Gov't, P.H.S.
  • Validation Study

MeSH terms

  • Algorithms
  • Brain Mapping / methods
  • Communication Aids for Disabled*
  • Diagnosis, Computer-Assisted / methods
  • Electroencephalography / methods*
  • Evoked Potentials, Motor*
  • Humans
  • Imagination*
  • Models, Neurological
  • Motor Cortex / physiopathology*
  • Pattern Recognition, Automated / methods*
  • Principal Component Analysis
  • User-Computer Interface*