title: Hybrid Brain-Computer Interface (BCI) based on the EEG and EOG signals.
abstract: Recently, the integration of different electrophysiological signals into an electroencephalogram (EEG) has become an effective approach to improve the practicality of brain-computer interface (BCI) systems, referred to as hybrid BCIs. In this paper, a hybrid BCI was designed by combining an EEG with electrocardiograph (EOG) signals and tested using a target selection experiment. Gaze direction from the EOG and the event-related (de)synchronization (ERD/ERS) induced by motor imagery from the EEG were simultaneously detected as the output of the BCI system. The target selection mechanism was based on the synthesis of the gaze direction and ERD activity. When an ERD activity was detected, the target corresponding to the gaze direction was selected; without ERD activity, no target was selected, even when a subjects gaze was directed at the target. With this mechanism, the operation of the BCI system is more flexible and voluntary. The accuracy and completion time of the target selection tasks during the online testing were 89.3% and 2.4 seconds, respectively. These results show the feasibility and practicality of this hybrid BCI system, which can potentially be used in the rehabilitation of disabled individuals.
authors.affiliation: Department of Automatic Control, College of Mechatronics and Automation, National University of Defense Technology, 410073, Changsha, Hunan, People's Republic of China. E-mail: jiangjun@nudt.edu.cn.
authors.forename: Jun
authors.initials: J
authors.lastname: Jiang
authors.affiliation: Department of Automatic Control, College of Mechatronics and Automation, National University of Defense Technology, 410073, Changsha, Hunan, People's Republic of China. E-mail: jiangjun@nudt.edu.cn.
authors.forename: Zongtan
authors.initials: Z
authors.lastname: Zhou
authors.affiliation: Department of Automatic Control, College of Mechatronics and Automation, National University of Defense Technology, 410073, Changsha, Hunan, People's Republic of China. E-mail: jiangjun@nudt.edu.cn.
authors.forename: Erwei
authors.initials: E
authors.lastname: Yin
authors.affiliation: Department of Automatic Control, College of Mechatronics and Automation, National University of Defense Technology, 410073, Changsha, Hunan, People's Republic of China. E-mail: jiangjun@nudt.edu.cn.
authors.forename: Yang
authors.initials: Y
authors.lastname: Yu
authors.affiliation: Department of Automatic Control, College of Mechatronics and Automation, National University of Defense Technology, 410073, Changsha, Hunan, People's Republic of China. E-mail: jiangjun@nudt.edu.cn.
authors.forename: Dewen
authors.initials: D
authors.lastname: Hu
mesh_terms: D000465:Algorithms; D062207:Brain-Computer Interfaces; D004569:Electroencephalography / Q000379:methods*; D004585:Electrooculography / Q000379:methods*; D019054:Evoked Potentials, Motor / Q000502:physiology*; D005133:Eye Movements / Q000502:physiology; D005403:Fixation, Ocular / Q000502:physiology*; D006801:Humans; D007092:Imagination / Q000502:physiology*; D009044:Motor Cortex / Q000502:physiology*; D009068:Movement / Q000502:physiology; D010363:Pattern Recognition, Automated / Q000379:methods; D013647:Task Performance and Analysis
keywords: EEG; EOG; event-related (de)synchronization; hybrid brain computer interface; target selection