A transformer-based deep neural network model for SSVEP classification

Steady-state visual evoked potential (SSVEP) is one of the most commonly used control signals in the brain-computer interface (BCI) systems. However, the conventional spatial filtering methods for SSVEP classification highly depend on the subject-specific calibration data. The need for the methods t...

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Main Authors: Chen, Jianbo, Zhang, Yangsong, Pan, Yudong, Xu, Peng, Guan, Cuntai
Other Authors: School of Computer Science and Engineering
Format: Article
Language:English
Published: 2023
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Online Access:https://hdl.handle.net/10356/172792
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-1727922023-12-20T04:12:59Z A transformer-based deep neural network model for SSVEP classification Chen, Jianbo Zhang, Yangsong Pan, Yudong Xu, Peng Guan, Cuntai School of Computer Science and Engineering Engineering::Computer science and engineering Brain–Computer Interface Steady-State Visual Evoked Potential Steady-state visual evoked potential (SSVEP) is one of the most commonly used control signals in the brain-computer interface (BCI) systems. However, the conventional spatial filtering methods for SSVEP classification highly depend on the subject-specific calibration data. The need for the methods that can alleviate the demand for the calibration data becomes urgent. In recent years, developing the methods that can work in inter-subject scenario has become a promising new direction. As a popular deep learning model nowadays, Transformer has been used in EEG signal classification tasks owing to its excellent performance. Therefore, in this study, we proposed a deep learning model for SSVEP classification based on Transformer architecture in inter-subject scenario, termed as SSVEPformer, which was the first application of Transformer on the SSVEP classification. Inspired by previous studies, we adopted the complex spectrum features of SSVEP data as the model input, which could enable the model to simultaneously explore the spectral and spatial information for classification. Furthermore, to fully utilize the harmonic information, an extended SSVEPformer based on the filter bank technology (FB-SSVEPformer) was proposed to improve the classification performance. Experiments were conducted using two open datasets (Dataset 1: 10 subjects, 12 targets; Dataset 2: 35 subjects, 40 targets). The experimental results show that the proposed models could achieve better results in terms of classification accuracy and information transfer rate than other baseline methods. The proposed models validate the feasibility of deep learning models based on Transformer architecture for SSVEP data classification, and could serve as potential models to alleviate the calibration procedure in the practical application of SSVEP-based BCI systems. This work was supported in part by the National Natural Science Foundation of China under Grant No. 62076209 and No. 61871423. 2023-12-20T04:12:59Z 2023-12-20T04:12:59Z 2023 Journal Article Chen, J., Zhang, Y., Pan, Y., Xu, P. & Guan, C. (2023). A transformer-based deep neural network model for SSVEP classification. Neural Networks, 164, 521-534. https://dx.doi.org/10.1016/j.neunet.2023.04.045 0893-6080 https://hdl.handle.net/10356/172792 10.1016/j.neunet.2023.04.045 37209444 2-s2.0-85159755865 164 521 534 en Neural Networks © 2023 Elsevier Ltd. All rights reserved.
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic Engineering::Computer science and engineering
Brain–Computer Interface
Steady-State Visual Evoked Potential
spellingShingle Engineering::Computer science and engineering
Brain–Computer Interface
Steady-State Visual Evoked Potential
Chen, Jianbo
Zhang, Yangsong
Pan, Yudong
Xu, Peng
Guan, Cuntai
A transformer-based deep neural network model for SSVEP classification
description Steady-state visual evoked potential (SSVEP) is one of the most commonly used control signals in the brain-computer interface (BCI) systems. However, the conventional spatial filtering methods for SSVEP classification highly depend on the subject-specific calibration data. The need for the methods that can alleviate the demand for the calibration data becomes urgent. In recent years, developing the methods that can work in inter-subject scenario has become a promising new direction. As a popular deep learning model nowadays, Transformer has been used in EEG signal classification tasks owing to its excellent performance. Therefore, in this study, we proposed a deep learning model for SSVEP classification based on Transformer architecture in inter-subject scenario, termed as SSVEPformer, which was the first application of Transformer on the SSVEP classification. Inspired by previous studies, we adopted the complex spectrum features of SSVEP data as the model input, which could enable the model to simultaneously explore the spectral and spatial information for classification. Furthermore, to fully utilize the harmonic information, an extended SSVEPformer based on the filter bank technology (FB-SSVEPformer) was proposed to improve the classification performance. Experiments were conducted using two open datasets (Dataset 1: 10 subjects, 12 targets; Dataset 2: 35 subjects, 40 targets). The experimental results show that the proposed models could achieve better results in terms of classification accuracy and information transfer rate than other baseline methods. The proposed models validate the feasibility of deep learning models based on Transformer architecture for SSVEP data classification, and could serve as potential models to alleviate the calibration procedure in the practical application of SSVEP-based BCI systems.
author2 School of Computer Science and Engineering
author_facet School of Computer Science and Engineering
Chen, Jianbo
Zhang, Yangsong
Pan, Yudong
Xu, Peng
Guan, Cuntai
format Article
author Chen, Jianbo
Zhang, Yangsong
Pan, Yudong
Xu, Peng
Guan, Cuntai
author_sort Chen, Jianbo
title A transformer-based deep neural network model for SSVEP classification
title_short A transformer-based deep neural network model for SSVEP classification
title_full A transformer-based deep neural network model for SSVEP classification
title_fullStr A transformer-based deep neural network model for SSVEP classification
title_full_unstemmed A transformer-based deep neural network model for SSVEP classification
title_sort transformer-based deep neural network model for ssvep classification
publishDate 2023
url https://hdl.handle.net/10356/172792
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