A BCI painting system using a hybrid control approach based on SSVEP and P300. (November 2022)

• Record Type: Journal Article
• Authors: Tang, Zhichuan; Wang, Xinyang; Wu, Jiayi; Ping, Yaqin; Guo, Xiaogang; Cui, Zhixuan
• Is Part Of: Computers in biology and medicine. Volume 150(2022)
• Publication Date: 2022-11
• Subjects: BCI painting System -- Hybrid control -- SSVEP -- P300 -- Artistic expression; Medicine -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; 610.285
• DOI: 10.1016/j.compbiomed.2022.106118
• Languages: English
• ISSNs: 0010-4825

Abstract

Abstract: Brain-computer interfaces (BCIs) can help people with disabilities to communicate with others, express themselves, and even create art. In this paper, a BCI painting system using a hybrid control approach based on steady-state visual evoked potential (SSVEP) and P300 was developed, which can enable simple painting through brain-controlled painting tools. The BCI painting system is composed of two parts: a hybrid stimulus interface and a hybrid electroencephalogram (EEG) signal processing module. The user selects the menus and tools through the SSVEP and P300 stimulus matrices, respectively, and the paintings are displayed in the canvas area of the hybrid stimulus interface in real time. Twenty subjects participated in this study. An offline training experiment was performed to construct the P300 and SSVEP recognition models for each subject; an online painting experiment, which included a copy-painting task and a free-painting task, was performed to evaluate the BCI painting system. The results of the online painting experiment showed that the average tool selection accuracy (88.92 ± 3.94%) of the BCI painting system using the hybrid stimulus interface was slightly higher than that of the traditional brain painting system based on the P300 stimulus interface; the average information transfer rate (ITR) (74.20 ± 5.28 bpm, 71.80 ± 5.15 bpm) in the copy-painting and free-painting tasks of the BCI painting system was significantly higher than that of the traditional brain painting system. Our BCI painting system can effectively help users express their artistic creativity and improve their painting efficiency, and can provide new methods and new ideas for developing BCI-controlled applications. Highlights: We develop a BCI painting system using a SSVEP/P300-based hybrid control approach. Tool selection accuracy of our system is higher than P300 brain painting system. Information transfer rate of our system is higher than P300 brain painting system. Interaction process and control mode of our system accord more with user's habits. Our system helps users express artistic creativity and improve painting efficiency.