Steady-state visual evoked potentials-based 2D continuous control of brain-computer interface

Two-dimensional continuous control based on brain-computer interface (BCI) systems is becoming a research hotspot. However, due to the discreteness in BCI signal decoding, achieving smooth 2D continuous control is often challenging. To address this issue, this paper proposes an innovative 2D continuous control method that converts three discrete commands into continuous ones and validates it through a real-time online BCI system. We used dry electrodes for signal acquisition and applied the Canonical Correlation Analysis (CCA) algorithm for electroencephalography (EEG) decoding. Two simulation experiments were designed: obstacle avoidance for unmanned vehicles and trajectory tracking for unmanned vehicles, to verify the effectiveness of the 2D continuous control method. By analyzing experimental data from three subjects and multiple evaluation metrics, we comprehensively and thoroughly assessed the proposed method.