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

LIN Yanfei; ZANG Boyu; GUO Rongxiao; LIU Zhiwen; GAO Xiaorong

doi:10.11999/JEIT210816

Volume 44 Issue 2

Feb. 2022

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Article Navigation > Journal of Electronics & Information Technology > 2022 > 44(2): 446-454

LIN Yanfei, ZANG Boyu, GUO Rongxiao, LIU Zhiwen, GAO Xiaorong. A Deep Learning Method for SSVEP Classification Based on Phase and Frequency Characteristics[J]. Journal of Electronics & Information Technology, 2022, 44(2): 446-454. doi: 10.11999/JEIT210816

Citation:

LIN Yanfei, ZANG Boyu, GUO Rongxiao, LIU Zhiwen, GAO Xiaorong. A Deep Learning Method for SSVEP Classification Based on Phase and Frequency Characteristics[J]. Journal of Electronics & Information Technology, 2022, 44(2): 446-454. doi: 10.11999/JEIT210816

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

doi: 10.11999/JEIT210816

1.
School of Information and Electronics, Beijing Institute of Technology, Beijing 100081, China
2.
School of Medicine, Tsinghua University, Beijing 100084, China

Funds: The National Natural Science Foundation of China (61601028, 61431007), Beijing Science and Technology Program (Z201100004420015)

Received Date: 2021-08-11
Accepted Date: 2022-01-20
Rev Recd Date: 2022-01-17

Available Online: 2022-01-21

Publish Date: 2022-02-25

Abstract

Abstract

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.
- Deep learning,
- Convolutional Neural Network (CNN),
- Steady-State Visual Evoked Potential (SSVEP)

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References(16)

References

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