A High-Rate Hybrid BCI System Based on High-Frequency SSVEP and sEMG
Cui, Hongyan; Chi, Xinyi; Wang, Lei; Chen, Xiaogang
Hybrid brain-computer interfaces (BCIs) that combine more than two modes are a recent trend in BCI research. Due to its high signal-to-noise ratio (SNR) and outstanding information transfer rate (ITR), steady-state visual evoked potential (SSVEP) has been widely used in combination with other brain/non-brain signal modalities, such as surface electromyography (sEMG). The mutual interference between SSVEP and sEMG is less pronounced. Furthermore, both SSVEP and sEMG have high SNR values, which facilitate accurate identification in a short time. Thus, hybrid BCI systems combining SSVEP and sEMG have received much attention in the BCI literature. However, most existing studies regarding hybrid BCIs based on SSVEP and sEMG adopt low-frequency visual stimuli to induce SSVEPs. The comfort of these systems needs further improvement to meet the practical application requirements. High-frequency stimuli that above 30 Hz have been reported to be more comfortable than low-frequency stimuli. Therefore, this paper attempts to build a novel hybrid BCI combining high-frequency SSVEP and sEMG signals for spelling applications. This paper combines a sixteen-target high frequency SSVEP-BCI and a four-command sEMG-based control mode to build a 64-target hybrid system. This demonstrates the advantage of the proposed hybridization in increasing the number of control commands. In this work, EEG and sEMG were obtained simultaneously from the scalp and skin surface of subjects, respectively. These two types of signals were analyzed independently and then combined to determine the target stimulus. Online results obtained from ten volunteers exhibited that the developed hybrid system obtained an average ITR of 159.12 bits/min. The ITR obtained in this study is much higher than that reported by existing related studies. These results exhibited the feasibility and effectiveness of fusing high-frequency SSVEP and sEMG towards improving the total BCI system performance. Furthermore, this proposed hybridization can be applied to other BCI system designs.
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