Abstract
Generally, Steady-State Visually Evoked Potentials (SSVEP) has widely recognized advantages, like being easy to use, requiring little user training [1], while Motor Imagery (MI) is not easy to introduce for some subjects. This work introduces a hybrid brain-computer interface (BCI) combines MI and SSVEP strategies - such an approach allows us to improve performance and universality of the system, and also the number of EEG electrodes from 32 to 3 in central area can increase the efficiency of EEG preprocessing to design an effective and easy way to use hybrid BCI system. In this study the Common Spatial Pattern (CSP) algorithm was introduced as a feature extraction method, which provides a high accuracy in event-related synchronization/desynchronization (ERS/ERD)-based BCL The four most common classifiers (KNNC, PARZENDC, LDC, SVC) were used for accuracy estimation. Results show that support vector classifier (SVC) and K-nearest-neighbor (KNN) classifier provide better performance than others, and it is possible to reach the same good accuracy using 3-channel (C3, Cz, C4) hybrid BCI system, as with usual 32-channel system.
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