Multi-scale noise transfer and feature frequency detection in SSVEP based on FitzHugh–Nagumo neuron system

Abstract

Objective. The steady-state visual evoked potential (SSVEP) is one of the most commonly used control signals for brain–computer interfaces (BCIs) due to its excellent interactive potential, such as high tolerance to noises and robust performance across users. In addition, it has a stable cycle, obvious characteristics and minimal training requirements. However, the SSVEP is extremely weak and companied with strong and multi-scale noise, resulting in a poor signal-to-noise ratio in practice. Common algorithms for classification are based on the principle of template matching and spatial filtering, which cannot obtain satisfied performance of SSVEP detection under the multi-scale noise. Therefore, using linear methods to extract SSVEP with obvious nonlinear and non-stationary characteristics, the useful signal will be attenuated or lost. Approach. To address this issue, two novel frameworks based on a two-dimensional nonlinear FitzHugh–Nagumo (FHN) neuron system are proposed to extract feature frequency of SSVEP. Results. In order to evaluate the effectiveness of the proposed methods, this research recruit 22 subjects to participate the experiment. Experimental results show that nonlinear FHN neuron model can force the energy of noise to be transferred into SSVEP and hence amplifying the amplitude of the target frequency. Compared with the traditional methods, the FHN and FHNCCA methods can achieve higher classification accuracy and faster processing speed, which effectively improves the information transmission rate of SSVEP-based BCI.