A Fast Recognition Method of SSVEP Signals Based on Time-Frequency Multiscale

WANG Xiaotian; CUI Xinyu; LIANG Shuo; CHEN Chao

doi:10.11999/JEIT221496

Volume 45 Issue 8

Aug. 2023

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Article Navigation > Journal of Electronics & Information Technology > 2023 > 45(8): 2788-2795

WANG Xiaotian, CUI Xinyu, LIANG Shuo, CHEN Chao. A Fast Recognition Method of SSVEP Signals Based on Time-Frequency Multiscale[J]. Journal of Electronics & Information Technology, 2023, 45(8): 2788-2795. doi: 10.11999/JEIT221496

Citation:

WANG Xiaotian, CUI Xinyu, LIANG Shuo, CHEN Chao. A Fast Recognition Method of SSVEP Signals Based on Time-Frequency Multiscale[J]. Journal of Electronics & Information Technology, 2023, 45(8): 2788-2795. doi: 10.11999/JEIT221496

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A Fast Recognition Method of SSVEP Signals Based on Time-Frequency Multiscale

doi: 10.11999/JEIT221496

1.
School of Artificial Intelligence, Xidian University, Xi’an 710071, China
2.
Guangzhou Institute of Technology, Xidian University, Guangzhou 510555, China
3.
Key Laboratory of Complex System Control Theory and Application, Tianjin University of Technology, Tianjin 300384, China

Funds: The National Natural Science Foundation of China (62293483, 61976169, 62176201), The National Key Research and Development Project of China (2019YFA0706604, 2022YFF1202500, 2022YFF1202501)

Received Date: 2022-12-01
Rev Recd Date: 2023-05-15

Available Online: 2023-05-22

Publish Date: 2023-08-21

Abstract

Abstract

A brain-computer interface based on Steady-State Visual Evoked Potential (SSVEP) has recently garnered considerable interest in human-computer cooperation. Nevertheless, SSVEP signals with short time windows suffer from a low signal-to-noise ratio and insufficient feature extraction. This study examines and extracts the SSVEP signal characteristics from three perspectives: frequency domain, time domain and spatial domain. The proposed method extracts the amplitude and phase feature information from a three-dimensional recalibrated feature matrix developed by incorporating the real part and the imaginary part information in the frequency domain. Subsequently, the model’s representation ability is enhanced by training samples across multiple stimulus time window scales in the time domain. Finally, multiscale feature information in the channel space and frequency domain is extracted in parallel by using distinct scaled one-dimensional convolution kernels with. In this paper, experiments are conducted on two open datasets characterized by different visual stimulus frequencies and frequency intervals. The average accuracy and average information transfer rate at a time window of 1 s surpass the performance of existing methods.
- Steady-State Visual Evoked Potential (SSVEP),
- Brain-computer interface,
- Multiscale feature

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References(18)

References

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