基于相频特性的稳态视觉诱发电位深度学习分类模型

针对现有深度学习分类方法对稳态视觉诱发电位相位与频率信息利用不充分的问题,该文提出一种用于稳态视觉诱发电位(SSVEP)分类的卷积神经网络模型.该模型以经过快速傅里叶变换后的复向量作为输入,首先对各个导联的实部向量和虚部向量进行卷积,学习相位信息;随后引入空间注意力机制,对判别频率信息进行增强;然后使用2维卷积和最大池...

A Deep Learning Method for SSVEP Classification Based on Phase and Frequency Characteristics

A deep learning method for Steady-State Visual Evoked Potential (SSVEP) classification is proposed to solve the problem that phase and frequency information are not fully used in existing deep learning models. First, the proposed model uses complex vectors of fast Fourier transform as input and operates convolution on real and imaginary vectors to learn phase information, and then utilizes the spatial attention module to enhance discriminative frequency information. Next, two-dimensional convolution and max pooling are used to extract further spatial and frequency features. Finally, fully connected layers are utilized to classify. The accuracy of proposed model can reach 81.21% in the case of cross subject, and the accuracy can be further improved to 83.17% by adding the standard sinusoidal signal templates to the training set. The results show that the proposed model achieves better performance than canonical correlation analysis algorithm.