Simultaneous Classification of Multiple Motor Imagery and P300 for Increase in Output Information of Brain-computer Interface

Brain-Computer Interface (BCI) is a system to obtain information from the brain signal to control computers. P300 and motor imagery task of Electroencephalogram (EEG) are mainly used features for BCI. However, BCI with P300 classifies only two states and features of motor imagery task are too obscure to be classified easily. Therefore, we propose a method to increase the number of classified states with high accuracy by mixed signal processing for P300 and motor imaginary task. BCI using P300 and motor imaginary task is going to have more bit rate than conventional BCI. We design a experiment which gives 4 classes data as control, P300, and P300 during motor imagery of right or left hand. First, we confirm that P300 appear during motor imagery task. In addition, we examine the best method for feature extraction. Finally, we classify 4 classes by multi-class Support Vector Machines, and show the efficacy of mixed signal which contain P300 and motor imagery.