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In brain computer interface (BCI) research, electroencephalography (EEG) is the most widely used method due to its noninvasiveness, high temporal resolution and portability. Most of the EEG-based BCI studies are aimed at developing methodologies for signal processing, feature extraction and classification. In this study, an experimental EEG study was carried out with six subjects performing imagery mental and motor tasks. We present a  multi-class EEG decoding with a novel pairwise output coding method of EEGs to improve the performance of self-induced BCI systems. This method involves an augmented one-versus-one multiclass classification with less time and reduced number of electrodes. Furthermore, a train repetition number is introduced in the training step to optimize the data selection. The difference among right and left hemispheres is also searched. Finally, the difference between experienced and novice subjects is also observed. The experimental results have demonstrated that, the use of proposed classification algorithm produces high classification accuracies (98%) with nine channels. Reduced numbers of channels (four channels) have 100% accuracies for mental tasks and 87% accuracies for motor tasks with Support Vector Machines (SVM). The classification accuracies are quite high though the proposed one-versus-one technique worked well compared to the classical method. The results would be promising for a real-time study.

an international journal of optimization and control: theories and applications/e-an international journal of optimization and control: theories and applications

A pairwise output coding method for multi-class EEG classification of a self-induced BCI