A Hybrid Brain-Computer Interface System Based on Motor Imageries and Eye-Blinking

This paper focuses on the online implementation of a hybrid brain computer interface (BCI) involving electroculogram (EOG) and electroencephalogram (EEG) of motor imagery (MI). The hybrid BCI system comprises of modules of eye-blinking detection, ICA spatial filter, zero-training classifier and cursor movement controlling. Eye-blinking information contained in EOG signal was achieved for locating EEG segments related to motor imageries. Then, independent component analysis (ICA) was applied to the filtered EEG data to yield the motor-related potentials, whose features were fed into a zero-training classifier. Finally, the classification results regarding the types of moving imagination were transferred into commands to control the cursor moving along a predesigned path shown on the computer screen. Four subjects attended the online BCI tests, the average moving accuracy reached 84.56% for all tests, and the response time was about 4.13 trials/min. The experimental results demonstrate that the hybrid MIBCI system in this study is feasible for the real-time control of peripheral devices.