Visual tracking brain computer interface

Changxing Huang, Nanlin Shi, Yining Miao, Xiaogang Chen, Yijun Wang +1 more
11/21/2023
cs.HCcs.AIeess.SY

No official code found. Best implementation: Oser-1210/Visual_Tracking_BCI (12 stars, MIT, last commit 2024-01-24).

Abstract

Brain-computer interfaces (BCIs) offer a way to interact with computers without relying on physical movements. Non-invasive electroencephalography (EEG)-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 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.

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Code Implementations(1)

Oser-1210/Visual_Tracking_BCICommunity62%
MIT

The GitHub repository contains the online system code derived from the 'visual tracking brain-computer interface' research. This code encompasses fixed visual tracking tasks, random visual tracking tasks, painting application, and snake game.

121PythonJan 24, 20242 years agoMIT

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Cite this paper

@article{huang2023visual,
  title  = {Visual tracking brain computer interface},
  author = {Changxing Huang and Nanlin Shi and Yining Miao and Xiaogang Chen and Yijun Wang and Xiaorong Gao},
  year   = {2023},
  eprint = {2311.12592},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2311.12592v1}
}

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