There have been many attempts to design brain-computer interfaces (BCIs) for wheelchair control based on steady state visual evoked potential (SSVEP), event-related desynchronization/synchronization (ERD/ERS) during motor imagery (MI) tasks, P300 evoked potential, and some hybrid signals. However, those BCI systems cannot implement the wheelchair navigation flexibly and effectively. In this paper, we propose a hybrid BCI scheme based on two-class MI and four-class SSVEP tasks. It cannot only provide multi-degree control for its user, but also allow the user implement the different types of commands in parallel. In order for the subject to learn the hybrid mental strategies effectively, we design a visual and auditory cues and feedback-based training paradigm. Furthermore, an algorithm based on entropy of classification probabilities is proposed to detect intentional control (IC) state for hybrid tasks, and ensure that multi-degree control commands are accurately and quickly generated. The experiment results attest to the efficiency and flexibility of the hybrid BCI for wheelchair control in the real-world.
Brain Computer Interface (BCI) speller is a typical BCI-based application to help paralyzed patients express their thoughts. This paper proposed a novel motor imagery based BCI speller with Oct-o-spell paradigm for word input. Furthermore, an intelligent input method was used for improving the performance of the BCI speller. For the English word spelling experiment, we compared synchronous control with previous asynchronous control under the same experimental condition. There were no significant differences between these two control methods in the classification accuracy, information transmission rate (ITR) or letters per minute (LPM). And the accuracy rates of over 70% validated the feasibility for these two control strategies. It was indicated that MI-based synchronous control protocol was feasible for BCI speller. And the efficiency of the predictive text entry (PTE) mode was superior to that of the Non-PTE mode.
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