A survey of deep learning-based classification methods for steady-state visual evoked potentials

Abstract

Purpose

Steady-state visual evoked potential (SSVEP) based BCI has attracted great interests owing to the high information transfer rate (ITR) and little training requirement. The performance of SSVEP-based BCI heavily depends on the classification methods. Deep Learning (DL) technology provides an alternative avenue for the data classification in SSVEP-based BCI, and has received increasing interests in recent years. This review aimed to summarize the progress of DL-based classification methods for SSVEP data over the past decade.

Materials and method

The literature was searched and selected based on the research topics of DL and SSVEP. We categorized these methods into four classes, i.e., traditional neural network structures-based DL methods, traditional frequency recognition methods inspiring DL methods, attention mechanisms-based DL models, and transfer learning technology-based DL methods, and generative model-based recognition method. Moreover, we analyzed the current challenges and presented future research opportunities.

Conclusions

This study provides a systematic description of the current development status on DL-based SSVEP classification methods, and sheds insight on future researches.

Keywords:

• Deep learning (DL)
• brain-computer interface (BCI)
• steady-state visual evoked potential (SSVEP)
• frequency recognition

Author contributions

Y.Z., Y.P. conceived and designed this paper. Y.P., Y.Z., and J.C. wrote the paper.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported in part by the National Natural Science Foundation of China under Grant No.[62076209].

Notes on contributors

Yudong Pan

Yudong Pan received the B.E. degree from the Yichun University. He is currently pursuing the M.E. degree with the Southwest University of Science and Technology, China. His research interests include brain–computer interface (BCI), steady-state visual evoked potential (SSVEP), and generative adversarial network (GAN).

Yangsong Zhang

Yangsong Zhang received the Ph.D. degree in signal and information processing from the School of Life Science and Technology, University of Electronic Science and Technology of China, in 2013. He is currently a Professor with the School of Computer Science and Technology, Southwest University of Science and Technology, China. His research interests include brain–computer interface, deep learning, machine learning, medical imaging processing, etc.