Brain-computer interface based on steady-state visual evoked potential has been widely studied due to the high information transfer rate, little user training, and wide subject applicability. However, there are also disadvantages such as visual discomfort and “BCI illiteracy.” To address these problems, this study proposes to use low-frequency stimulations, which can simultaneously elicit visual evoked potential and pupillary response to construct a hybrid BCI system. Classification accuracy was calculated using supervised and unsupervised methods, respectively, and the hybrid accuracy was obtained (...) using a decision fusion method to combine the information of VEP and PR. Online experimental results from 10 subjects showed that the averaged accuracy was 94.90 ± 2.34% for the supervised method and 91.88 ± 3.68% for the unsupervised method, which correspond to the ITR of 64.35 ± 3.07 bits/min and 33.19 ± 2.38 bpm, respectively. Notably, the hybrid method achieved higher accuracy and ITR than that of VEP and PR for most subjects, especially for the short data length. Together with the subjects’ feedback on user experience, these results indicate that the proposed h-BCI with the low-frequency stimulation paradigm is more comfortable and favorable than the traditional SSVEP-BCI paradigm using the alpha frequency range. (shrink)

This paper investigated how implicit and explicit knowledge is reflected in event-related potentials in sequence learning. ERPs were recorded during a serial reaction time task. The results showed that there were greater RT benefits for standard compared with deviant stimuli later than early on, indicating sequence learning. After training, more standard triplets were generated under inclusion than exclusion tests and more standard triplets under exclusion than chance level, indicating that participants acquired both explicit and implicit knowledge. However, deviant targets elicited (...) enhanced N2 and P3 components for targets with explicit knowledge but a larger N2 effect for targets with implicit knowledge, revealing that implicit knowledge expresses itself in relatively early components and explicit knowledge in additional P3 components. The results help resolve current debate about the neural substrates supporting implicit and explicit learning. (shrink)

The application study of robot control based brain-computer interface not only helps to promote the practicality of BCI but also helps to promote the advancement of robot technology, which is of great significance. Among the many obstacles, the importability of the stimulator brings much inconvenience to the robot control task. In this study, augmented reality technology was employed as the visual stimulator of steady-state visual evoked potential -BCI and the robot walking experiment in the maze was designed to testify the (...) applicability of the AR-BCI system. The online experiment was designed to complete the robot maze walking task and the robot walking commands were sent out by BCI system, in which human intentions were decoded by Filter Bank Canonical Correlation Analysis algorithm. The results showed that all the 12 subjects could complete the robot walking task in the maze, which verified the feasibility of the AR-SSVEP-NAO system. This study provided an application demonstration for the robot control base on brain–computer interface, and further provided a new method for the future portable BCI system. (shrink)