Abstract
This study presents a novel hybrid interface based on both electroencephalography (EEG) and eye movement. The detection of combination EEG with eye movement provides a new means of communication for patients whose muscular damage are unable to communicate. And this method can translate some brain responses into actions. In this paper, based on the motor imagery, event related synchronization/desychronization (ERS/ERD) were tested by using time-frequency spectrum and brain topographic mapping. A features extraction algorithm is proposed based on common spatial pattern (CSP), then the support vector machine (SVM) were carried out to classificate data. An EEG recording device integrated with an eye tracker can be complementary to attain improved performance and a better efficiency. The eye movement signals (via eye tracker of Tobbi) and EEG signals of ERS/ERD are as the input of hybrid BCI system simultaneously while subjects follow movement of the arrows in each direction. The recognition accuracy of the entire system reaches to 86.1%. The results showed that the proposed method was efficient in the classification accuracy.
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Yang, J., Hao, Y., Bai, D., Jiang, Y., Yokoi, H. (2017). Exploration of a Hybrid Design Based on EEG and Eye Movement. In: Huang, Y., Wu, H., Liu, H., Yin, Z. (eds) Intelligent Robotics and Applications. ICIRA 2017. Lecture Notes in Computer Science(), vol 10462. Springer, Cham. https://doi.org/10.1007/978-3-319-65289-4_20
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DOI: https://doi.org/10.1007/978-3-319-65289-4_20
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