A large dataset for VEP based brain-computer interfaces employing narrow-band code modulation and frequency-phase modulation

Objective Brain-computer interfaces (BCIs) realize the information transmission between the brain and the external world. Visual evoked potential (VEP) based BCIs have gained widespread attention in the field of multi-instruction interaction attributed to their high information transfer rate (ITR). However, improving the IT R and the practicability is challenging for existing VEP based BCIs due to factors such as the encoding efficiency and the calibration time. To address this issue, this study proposed a new encoding method employing narrow-band random sequences and provided a large dataset for VEP based brain-computer interfaces.