Effects of Different Viewing Angles and Visual Stimuli on the Performance of MI Brain-Computer Interfaces

Abstract

In this study, a paradigm is proposed for 45° viewing angle and changing stimuli-based motor imagery brain-computer interface (MI-BCI). The viewing angle from the first-person perspective can better activate mirror neurons, which can improve the process of converting participants’ subjective motive intentions into operational controls. BCI performance is influenced by factors such as user attention and rhythmic visual stimulation has been shown to modulate visual attention. 18 participants completed the two-factor MI task of three viewing angles and two visual stimulus types. The results showed that the classification accuracy was found to be 76% with a 45°viewing angle and changing stimuli, which has a positive impact on the BCI's performance as well as the participants’ focus of attention during the tasks and reduces their level of visual fatigue. The findings of this study offer helpful direction for creating future interface paradigms for MI-BCI that are more effective and user-friendly.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

The data presented in this study are available on request from the corresponding author.

Additional information

Funding

This work was supported by the [National Natural Science Foundation of China] under Grant [number 72001202; [Fundamental Research Funds for the Central Universities of China] under Grant [number 2020ZDPY0308]; and [the Graduate Innovation Program of China University of Mining and Technology] under Grant [number 2022WLJCRCZL298].

Notes on contributors

Yuxin Bai

Yuxin Bai is a graduate student majoring in design in China University of Mining and Technology. Her research directions are brain-computer interface, human-computer interaction and cognition.

Jiang Shao

Jiang Shao, is an assistant professor. He teaches in the School of Architecture & Design at China University of Mining and Technology. His main research fields are brain-computer interface, digital interface human-computer interaction, information visualization, intelligent industrial product modeling design, and design cognitive psychology.

Ying Zhang

Ying Zhang is a graduate student majoring in design in China University of Mining and Technology. Her research directions are human-computer interaction and cognition.

Jun Yao

Jun Yao is a professor. He is the associate dean of the School of Architecture & Design, China University of Mining and Technology. And he is mainly engaged in the teaching and research of industrial design and theory.

Fangyuan Tian

Fangyuan Tian is studying for a doctorate in the School of Safety Science and Engineering, Xi’an University of Science and Technology.

Chengqi Xue

Chengqi Xue is a professor. He teaches in the School of Mechanical Engineering at Southeast University.