
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.
FOS: Computer and information sciences, Computer Science - Artificial Intelligence, Science, Q, Computer Science - Human-Computer Interaction, Bioengineering, Systems and Control (eess.SY), Electrical Engineering and Systems Science - Systems and Control, Article, Human-Computer Interaction (cs.HC), Biological sciences, Artificial Intelligence (cs.AI), FOS: Electrical engineering, electronic engineering, information engineering, Applied sciences
FOS: Computer and information sciences, Computer Science - Artificial Intelligence, Science, Q, Computer Science - Human-Computer Interaction, Bioengineering, Systems and Control (eess.SY), Electrical Engineering and Systems Science - Systems and Control, Article, Human-Computer Interaction (cs.HC), Biological sciences, Artificial Intelligence (cs.AI), FOS: Electrical engineering, electronic engineering, information engineering, Applied sciences
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| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Top 10% |
