SSVEP Transient Feature Extraction and Rapid Recognition Method Based on Bistable Stochastic Resonance

Steady-state Visual Evoked Potential, SSVEP), as the most commonly used communication paradigm for non-implantable Brain-Computer Interface (BCI), boasts the advantages of unnecessity of training, noise immunity and periodicity. The traditional SSVEP extraction methods can effectively identify the target frequency contained in original EEG, however, the required data length usually lasts a few seconds. In this paper, bistable stochastic resonance (BSR) is applied to SSVEP extraction. BSR is very sensitive to amplitude mutation and frequency fluctuation of the input signal, making the output difference can be used for the detection of the target frequency. The processing results illustrate that the proposed method not only has a high recognition accuracy, but also effectively shortens the recognition time, thus improving the calculating speed. Therefore, SSVEP extraction based on BSR has a higher information transmission rate (ITR), which is more suitable for the real-time BCI system.