Analysis of SSVEP frequency-spatial characteristics based on EhythmNet and its application in identity recognition

The frequency space characteristic is an important characteristic of steady-state visually evoked potential (SSVEP). The frequency space characteristic of traditional SSVEP is generally obtained by analyzing the power spectrum of EEG of scalp electrodes, but it is vulnerable to noise interference and data quality. This article proposes a new SSVEP frequency space feature analysis method. The proposed method consists of two main parts: 1) A shallow convolutional neural network, called EhythmNet in this paper, is designed for EEG rhythm analysis; 2) Based on multi channels EEG data obtained from a set of stimulus frequencies, single channel EEG of SSVEP dominant electrodes (such as Oz) was used as the training data for EhythmNet, and data from other channels were used as the test set to obtain the recognition rate of each channel, namely single channel recognition rate (SCA). The experimental results indicate that SCAs can accurately reflect the spatial distribution of SSVEP in the scalp electrode, and it is found that the spatial distribution of SCA has good individual stability and differences between individuals. In order to verify this feature, an identity recognition test based on SCA was conducted in the article, and more than 98.5% of the recognition results were achieved.