Classification of motor imagery tasks for brain-computer interface applications by means of two equivalent dipoles analysis.
We have developed a novel approach using source analysis for classifying motor imagery tasks. Two-equivalent-dipoles analysis was proposed to aid classification of motor imagery tasks for brain-computer interface (BCI) applications. By solving the electroencephalography (EEG) inverse problem of single trial data, it is found that the source analysis approach can aid classification of motor imagination of left- or right-hand movement without training. In four human subjects, an averaged accuracy of classification of 80% was achieved. The present study suggests the merits and feasibility of applying EEG inverse solutions to BCI applications from noninvasive EEG recordings.
Kamousi
Baharan
B
Department of Biomedical Engineering, University of Minnesota, Minneapolis, MN 55455, USA.
Liu
Zhongming
Z
He
Bin
B
D000200:Action Potentials; D000465:Algorithms; D001931:Brain Mapping / Q000379:methods*; D003198:Computer Simulation; D004569:Electroencephalography / Q000379:methods*; D005071:Evoked Potentials / Q000502:physiology*; D006801:Humans; D007092:Imagination / Q000502:physiology*; D008959:Models, Neurological; D009068:Movement / Q000502:physiology*; D010363:Pattern Recognition, Automated / Q000379:methods*; D013647:Task Performance and Analysis; D014584:User-Computer Interface