Characteristic frequency detection of steady-state visual evoked potentials based on filter bank second-order underdamped tristable stochastic resonance

Abstract

Brain computer interface (BCI) system based on steady-state visual evoked potentials (SSVEP) has great potential for communication and control applications. The actual intention of the user is determined by detecting the characteristic frequency of the SSVEP signals. However, the artifacts and spontaneous electroencephalogram (EEG) signals in SSVEP signals will degrade the performance of characteristic frequency detection. How to improve detection performance is a hot research topic. In this study, an innovative filter bank second-order underdamped tristable stochastic resonance (FBSUTSR) method is proposed and evaluated to enhance the characteristic frequency detection of SSVEP signals. FBSUTSR method uses noise to enhance signal detection and effectively utilizes the harmonic components contained in the signal. At the same time, the second-order underdamping characteristic makes the system form a unique filtering effect. In this study, the performance of SSVEP signals characteristic frequency detection with FBSUTSR, second-order underdamped tristable stochastic resonance (SUTSR), underdamped second-order stochastic resonance (USSR) and canonical coefficient analysis (CCA) is compared and analyzed using the public dataset of 40 types of stimulation targets of 35 subjects. When the stimulus data length is 5 s, FBSUTSR method achieves excellent performance with an average accuracy of 97.35±3.97% and an average information transmission rate (ITR) of 60.58±4.76bit·min-1, which is significantly higher than the performance of other three methods. This study verifies that the proposed FBSUTSR method has prominent performance advantages in implementing SSVEP signals characteristic frequency detection. The BCI system based on SSVEP using FBSUTSR method has great development potential in communication and control.