Original title: High-performance cVEP-BCI under minimal calibration
Authors: Yining Miao, Nanlin Shi, Changxing Huang, Yonghao Song, Xiaogang Chen, Yijun Wang, Xiaorong Gao
The article explores improving brain-computer interfaces (BCIs) using visual modulation with minimal setup. Code-modulated visual evoked potential-based BCIs (cVEP-BCIs) using broad white noise offer speed and flexibility but usually need extensive calibration, limiting their efficiency. Commonly, they achieve around 100 bits per minute (bpm), slower than other BCIs achieving over 200 bpm. To enhance cVEP-BCI with minimal setup, researchers devised a short, less-than-a-minute calibration involving flickering images. Using this data, they developed two methods to create cVEP patterns. These methods boosted performance, achieving an impressive 250 bpm—comparable to top-performing BCIs. Essentially, their work improved cVEP-BCIs using minimal setup, potentially making them more practical and user-friendly, advancing their usefulness in real-world applications.
Original article: https://arxiv.org/abs/2311.11596