Visual tracking brain-computer interface

Changxing Huang; Nanlin Shi; Yining Miao; Xiaogang Chen; Yijun Wang; Xiaorong Gao

doi:10.1016/j.isci.2024.109376

Visual tracking brain-computer interface

iScience. 2024 Mar 2;27(4):109376. doi: 10.1016/j.isci.2024.109376. eCollection 2024 Apr 19.

Authors

Changxing Huang¹, Nanlin Shi¹, Yining Miao¹, Xiaogang Chen², Yijun Wang³, Xiaorong Gao¹

Affiliations

¹ Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing 100084, China.
² Institute of Biomedical Engineering, Chinese Academy of Medical Sciences and Peking Union Medical College, Tianjin 300192, China.
³ State Key Laboratory on Integrated Optoelectronics, Institute of Semiconductors, Chinese Academy of Sciences Beijing, Beijing 100083, China.

Abstract

Brain-computer interfaces (BCIs) offer a way to interact with computers without relying on physical movements. Non-invasive electroencephalography-based visual BCIs, known for efficient speed and calibration ease, face limitations in continuous tasks due to discrete stimulus design and decoding methods. To achieve continuous control, we implemented a novel spatial encoding stimulus paradigm and devised a corresponding projection method to enable continuous modulation of decoded velocity. Subsequently, we conducted experiments involving 17 participants and achieved Fitt's information transfer rate (ITR) of 0.55 bps for the fixed tracking task and 0.37 bps for the random tracking task. The proposed BCI with a high Fitt's ITR was then integrated into two applications, including painting and gaming. In conclusion, this study proposed a visual BCI based-control method to go beyond discrete commands, allowing natural continuous control based on neural activity.

Keywords: Applied sciences; Bioengineering; Biological sciences.