An Analysis of Deep Learning Models in SSVEP-Based BCI: A Survey
The brain–computer interface (BCI), which provides a new way for humans to directly communicate with robots without the involvement of the peripheral nervous system, has recently attracted much attention. Among all the BCI paradigms, BCIs based on steady-state visual evoked potentials (SSVEPs) have...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10046535/ https://www.ncbi.nlm.nih.gov/pubmed/36979293 http://dx.doi.org/10.3390/brainsci13030483 |
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author | Xu, Dongcen Tang, Fengzhen Li, Yiping Zhang, Qifeng Feng, Xisheng |
author_facet | Xu, Dongcen Tang, Fengzhen Li, Yiping Zhang, Qifeng Feng, Xisheng |
author_sort | Xu, Dongcen |
collection | PubMed |
description | The brain–computer interface (BCI), which provides a new way for humans to directly communicate with robots without the involvement of the peripheral nervous system, has recently attracted much attention. Among all the BCI paradigms, BCIs based on steady-state visual evoked potentials (SSVEPs) have the highest information transfer rate (ITR) and the shortest training time. Meanwhile, deep learning has provided an effective and feasible solution for solving complex classification problems in many fields, and many researchers have started to apply deep learning to classify SSVEP signals. However, the designs of deep learning models vary drastically. There are many hyper-parameters that influence the performance of the model in an unpredictable way. This study surveyed 31 deep learning models (2011–2023) that were used to classify SSVEP signals and analyzed their design aspects including model input, model structure, performance measure, etc. Most of the studies that were surveyed in this paper were published in 2021 and 2022. This survey is an up-to-date design guide for researchers who are interested in using deep learning models to classify SSVEP signals. |
format | Online Article Text |
id | pubmed-10046535 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-100465352023-03-29 An Analysis of Deep Learning Models in SSVEP-Based BCI: A Survey Xu, Dongcen Tang, Fengzhen Li, Yiping Zhang, Qifeng Feng, Xisheng Brain Sci Review The brain–computer interface (BCI), which provides a new way for humans to directly communicate with robots without the involvement of the peripheral nervous system, has recently attracted much attention. Among all the BCI paradigms, BCIs based on steady-state visual evoked potentials (SSVEPs) have the highest information transfer rate (ITR) and the shortest training time. Meanwhile, deep learning has provided an effective and feasible solution for solving complex classification problems in many fields, and many researchers have started to apply deep learning to classify SSVEP signals. However, the designs of deep learning models vary drastically. There are many hyper-parameters that influence the performance of the model in an unpredictable way. This study surveyed 31 deep learning models (2011–2023) that were used to classify SSVEP signals and analyzed their design aspects including model input, model structure, performance measure, etc. Most of the studies that were surveyed in this paper were published in 2021 and 2022. This survey is an up-to-date design guide for researchers who are interested in using deep learning models to classify SSVEP signals. MDPI 2023-03-13 /pmc/articles/PMC10046535/ /pubmed/36979293 http://dx.doi.org/10.3390/brainsci13030483 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Review Xu, Dongcen Tang, Fengzhen Li, Yiping Zhang, Qifeng Feng, Xisheng An Analysis of Deep Learning Models in SSVEP-Based BCI: A Survey |
title | An Analysis of Deep Learning Models in SSVEP-Based BCI: A Survey |
title_full | An Analysis of Deep Learning Models in SSVEP-Based BCI: A Survey |
title_fullStr | An Analysis of Deep Learning Models in SSVEP-Based BCI: A Survey |
title_full_unstemmed | An Analysis of Deep Learning Models in SSVEP-Based BCI: A Survey |
title_short | An Analysis of Deep Learning Models in SSVEP-Based BCI: A Survey |
title_sort | analysis of deep learning models in ssvep-based bci: a survey |
topic | Review |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10046535/ https://www.ncbi.nlm.nih.gov/pubmed/36979293 http://dx.doi.org/10.3390/brainsci13030483 |
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