In this study, the effect of presentation rates on pupil dilation is investigated for target recognition in the Rapid Serial Visual Presentation (RSVP) paradigm. In this experiment, the RSVP paradigm with five different presentation rates, including 50, 80, 100, 150, and 200 ms, is designed. The pupillometry data of 15 subjects are collected and analyzed. The pupillometry results reveal that the peak and average amplitudes for pupil size and velocity at the 80-ms presentation rate are considerably higher than those at other presentation rates. The average amplitude of pupil acceleration at the 80-ms presentation rate is significantly higher than those at the other presentation rates. The latencies under 50- and 80-ms presentation rates are considerably lower than those of 100-, 150-, and 200-ms presentation rates. Additionally, no considerable differences are observed in the peak, average amplitude, and latency of pupil size, pupil velocity, and acceleration under 100-, 150-, and 200-ms presentation rates. These results reveal that with the increase in the presentation rate, pupil dilation first increases, then decreases, and later reaches saturation. The 80-ms presentation rate results in the largest point of pupil dilation. No correlation is observed between pupil dilation and recognition accuracy under the five presentation rates.

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.

This study explored methods for improving the performance of Steady-State Visual Evoked Potential (SSVEP)-based Brain-Computer Interfaces (BCI), and introduced a new analytical method to quantitatively analyze and reflect the characteristics of SSVEP. We focused on the effect of the pre-stimulation paradigm on the SSVEP dynamic models and the dynamic response process of SSVEP, and performed a comparative analysis of three pre-stimulus paradigms (black, gray, and white). Four dynamic models with different orders (second- and third-order) and with and without a zero point were used to fit the SSVEP envelope. The zero-pole analytical method was adopted to conduct quantitative analysis on the dynamic models, and the response characteristics of SSVEP were represented by zero-pole distribution characteristics. The results of this study indicated that the pre-stimulation paradigm affects the characteristics of SSVEP, and the dynamic models had good fitting abilities with SSVEPs under various types of pre-stimulation. Furthermore, the zero-pole characteristics of the models effectively characterize the damping coefficient, oscillation period, and other SSVEP characteristics. The comparison of zeros and poles indicated that the gray pre-stimulation condition corresponds to a lower damping coefficient, thus showing its potential to improve the performance of SSVEP-BCIs.