A Fast Recognition Method of SSVEP Signals Based on Time-Frequency Multiscale

A brain-computer interface based on Steady-State Visual Evoked Potential (SSVEP) has recently garnered considerable interest in human-computer cooperation. Nevertheless, SSVEP signals with short time windows suffer from a low signal-to-noise ratio and insufficient feature extraction. This study examines and extracts the SSVEP signal characteristics from three perspectives: frequency domain, time domain and spatial domain. The proposed method extracts the amplitude and phase feature information from a three-dimensional recalibrated feature matrix developed by incorporating the real part and the imaginary part information in the frequency domain. Subsequently, the model's representation ability is enhanced by training samples across multiple stimulus time window scales in the time domain. Finally, multiscale feature information in the channel space and frequency domain is extracted in parallel by using distinct scaled one-dimensional convolution kernels with. In this paper, experiments are conducted on two open datasets characterized by different visual stimulus frequencies and frequency intervals. The average accuracy and average information transfer rate at a time window of 1 s surpass the performance of existing methods.