SSVEP-Based Brain-Computer Interface Controlled Robotic Platform With Velocity Modulation

• Published in: IEEE transactions on neural systems and rehabilitation engineering Vol. 31; pp. 3448 - 3458
• Main Authors: Zhang, Yue, Qian, Kun, Xie, Sheng Quan, Shi, Chaoyang, Li, Jun, Zhang, Zhi-Qiang
• Format: Journal Article
• Language: English
• Published: New York IEEE 2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Bayesian analysis; Brain; Brain-computer interface (BCI); Brightness; Computer applications; Conditional probability; Electroencephalography; electroencephalography (EEG); Human-computer interface; Implants; Information transfer; Invasiveness; Manipulators; Probabilistic models; Robot arms; Robot control; robotic arm; Robotics; Robots; Signal classification; Statistical inference; steady-state visual evoked potential (SSVEP); Switches; Synchronism; Synchronization; Task analysis; Velocity; Velocity modulation; Visual evoked potentials; Visualization
• ISSN: 1534-4320; 1558-0210; 1558-0210
• DOI: 10.1109/TNSRE.2023.3308778

Steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs) have been extensively studied due to many benefits, such as non-invasiveness, high information transfer rate, and ease of use. SSVEP-based BCI has been investigated in various applications by projecting brain signals to robot control commands. However, the movement direction and speed are generally fixed and prescribed, neglecting the user's requirement for velocity changes during practical implementations. In this study, we proposed a velocity modulation method based on stimulus brightness for controlling the robotic arm in the SSVEP-based BCI system. A stimulation interface was designed, incorporating flickers, target and a cursor workspace. The synchronization of the cursor and robotic arm does not require the subject's eye switch between the stimuli and the robot. The feature vector consists of the characteristics of the signal and the classification result. Subsequently, the Gaussian mixture model (GMM) and Bayesian inference were used to calculate the posterior probabilities that the signal came from a high or low brightness flicker. A brain-actuated speed function was designed by incorporating the posterior probability difference. Finally, the historical velocity was considered to determine the final velocity. To demonstrate the effectiveness of the proposed method, online experiments, including single- and multi-target reaching tasks, were conducted. The extensive experimental results validated the feasibility of the proposed method in reducing reaching time and achieving proximity to the target.