Classification of EEG (electroencephalographic) signals recorded dur- ing right and left motor imagery tasks is a technique for designing BCI (Brain- computer interfaces). In this paper, the regression tree is used to separate the right/left patterns that are extracted by ERD time courses. The regression tree is a statistical method to identify complex patterns without rigorous theoretical and distributional assumptions. The simulation result shows that the proposed BCI can provide satisfactory offline classification error rate and mutual information.
['Chiman Wong', 'Feng Wan']
Classification of Imagery Movement Tasks for Brain-Computer Interfaces Using Regression Tree