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Background and objectiveThe brain-computer interface (BCI) system based on steady-state visual evoked potentials (SSVEP) is expected to help disabled patients achieve alternative prosthetic hand assistance. However, the existing study still has some shortcomings in interaction aspects such as stimulus paradigm and control logic. The purpose of this study is to innovate the visual stimulus paradigm and asynchronous decoding/control strategy by integrating augmented reality technology, and propose an asynchronous pattern recognition algorithm, thereby improving the interaction logic and practical application capabilities of the prosthetic hand with the BCI system.MethodsAn asynchronous visual stimulus paradigm based on an augmented reality (AR) interface was proposed in this paper, in which there were 8 control modes, including Grasp, Put down, Pinch, Point, Fist, Palm push, Hold pen, and Initial. According to the attentional orienting characteristics of the paradigm, a novel asynchronous pattern recognition algorithm that combines center extended canonical correlation analysis and support vector machine (Center-ECCA-SVM) was proposed. Then, this study proposed an intelligent BCI system switch based on a deep learning object detection algorithm (YOLOv4) to improve the level of user interaction. Finally, two experiments were designed to test the performance of the brain-controlled prosthetic hand system and its practical performance in real scenarios.ResultsUnder the AR paradigm of this study, compared with the liquid crystal display (LCD) paradigm, the average SSVEP spectrum amplitude of multiple subjects increased by 17.41%, and the signal-noise ratio (SNR) increased by 3.52%. The average stimulus pattern recognition accuracy was 96.71 ± 3.91%, which was 2.62% higher than the LCD paradigm. Under the data analysis time of 2s, the Center-ECCA-SVM classifier obtained 94.66 ± 3.87% and 97.40 ± 2.78% asynchronous pattern recognition accuracy under the Normal metric and the Tolerant metric, respectively. And the YOLOv4-tiny model achieves a speed of 25.29fps and a 96.4% confidence in the prosthetic hand in real-time detection. Finally, the brain-controlled prosthetic hand helped the subjects to complete 4 kinds of daily life tasks in the real scene, and the time-consuming were all within an acceptable range, which verified the effectiveness and practicability of the system.ConclusionThis research is based on improving the user interaction level of the prosthetic hand with the BCI system, and has made improvements in the SSVEP paradigm, asynchronous pattern recognition, interaction, and control logic. Furthermore, it also provides support for BCI areas for alternative prosthetic control, and movement disorder rehabilitation programs.
Zhang et al. (Mon,) studied this question.