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| An Asynchronous Control Strategy Based on Hybrid BCI |
| Xu Wei1,2, Zhao Yawei1,2, Qi Hongzhi1,2* |
1(College of Precision Instrument and Opto-Electronics Engineering, Tianjin University, Tianjin 300072, China)
2(Academy of Medical Engineering and Translational Medicine,Tianjin University, Tianjin 300072, China) |
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Abstract Asynchronous control problems of automatically identifying whether users are controlling the system is a focal point of research in BCI. Traditional paradigms induce EEG characteristics by distinguishing different instructions, causing the limitation of the information, so it is difficult to obtain ideal results of asynchronous identification. Aiming to resolve the problem, we developed a hybrid BCI system containingevent-related potential and steady-state visual evoked potential signals to promote the effect. In the study, we collected 19 channel EEG signals from 10 subjects under the control condition when the subjects used the BCI system normally and two idle conditions when subjects moved their eyes out of the screen and when they opened their eyes and rested. In order to identify the different conditions, we extracted the amplitude of the ERP and the correlation coefficient of SSVEP, then we estimated the posterior probability of the control state by ERP and SSVEP features, using a Bayesian method. Using 10 subjects' EEG features, we found out that the hybrid paradigm performed better than a single paradigm, because the differences under the two paradigms coexisted. We achieved the accuracy of 92.1% and an AUC of 0.98 in identifying the control condition and the idle condition. This study demonstrated that hybrid had the ability to improve asynchronous identifying and it was worthy of a further study and development.
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Received: 22 April 2019
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