Although notable progress has been made in the study of Steady-State Visual Evoked Potential (SSVEP)-based Brain-Computer Interface (BCI), several factors that limit the practical applications of BCIs still exist. One of these factors is the importability of the stimulator. In this study, Augmented Reality (AR) technology was introduced to present the visual stimuli of SSVEP-BCI, while the robot grasping experiment was designed to verify the applicability of the AR-BCI system. The offline experiment was designed to determine the best stimulus time, while the online experiment was used to complete the robot grasping task. The offline experiment revealed that better information transfer rate performance could be achieved when the stimulation time is 2 s. Results of the online experiment indicate that all 12 subjects could control the robot to complete the robot grasping task, which indicates the applicability of the AR-SSVEP-humanoid robot (NAO) system. This study verified the reliability of the AR-BCI system and indicated the applicability of the AR-SSVEP-NAO system in robot grasping tasks.

Steady-state visual evoked potential (SSVEP)-based brain- computer interfaces (BCIs) have been widely studied. Considerable progress has been made in the aspects of stimulus coding, electroencephalogram processing, and recognition algorithms to enhance system performance. The properties of SSVEP have been demonstrated to be highly sensitive to stimulus luminance. However, thus far, there have been very few reports on the impact of background luminance on the system performance of SSVEP- based BCIs. This study investigated the impact of stimulus background luminance on SSVEPs. Specifically, this study compared two types of background luminance, i.e., (1) black luminance [red, green, blue (rgb): (0, 0, 0)] and (2) gray luminance [rgb: (128, 128, 128)], and determined their effect on the classification performance of SSVEPs at the stimulus frequencies of 9, 11, 13, and 15 Hz. The offline results from nine healthy subjects showed that compared with the gray background luminance, the black background luminance induced larger SSVEP amplitude and larger signal-to- noise ratio, resulting in a better classification accuracy. These results suggest that the background luminance of visual stimulus has a considerable effect on the SSVEP and therefore has a potential to improve the BCI performance.

In general, a large amount of training data can effectively improve the classification performance of the Steady-State Visually Evoked Potential (SSVEP)-based Brain-Computer Interface (BCI) system. However, it will prolong the training time and considerably restrict the practicality of the system. This study proposed a SSVEP nonlinear signal model based on the Volterra filter, which could reconstruct stable reference signals using relatively small number of training targets by transfer learning, thereby reducing the training cost of SSVEP-BCI. Moreover, this study designed a transfer-extended Canonical Correlation Analysis (t-eCCA) method based on the model to achieve cross-target transfer. As a result, in a single-target SSVEP experiment with 16 stimulus frequencies, t-eCCA obtained an average accuracy of 86.96% $\mathrm{\pm }$12.87% across 12 subjects using only half of the calibration time, which exhibited no significant difference from the representative training classification algorithms, namely, extended canonical correlation analysis (88.32% $\mathrm{\pm }$13.97%) and task-related component analysis (88.92% $\mathrm{\pm }$14.44%), and was significantly higher than that of the classic non-training algorithms, namely, Canonical Correlation Analysis (CCA) as well as filter-bank CCA. Results showed that the proposed cross-target transfer algorithm t-eCCA could fully utilize the information about the targets and its stimulus frequencies and effectively reduce the training time of SSVEP-BCI.