A Transformer-based deep neural network model for SSVEP classification
- URL: http://arxiv.org/abs/2210.04172v1
- Date: Sun, 9 Oct 2022 05:28:35 GMT
- Title: A Transformer-based deep neural network model for SSVEP classification
- Authors: Jianbo Chen, Yangsong Zhang, Yudong Pan, Peng Xu, Cuntai Guan
- Abstract summary: We propose a deep learning model for SSVEP classification based on Transformer structure in inter-subject classification scenario.
Inspired by previous studies, the model adopts the frequency spectrum of SSVEP data as input, and explores the spectral and spatial domain information for classification.
The proposed models could achieve better results in terms of classification accuracy and information transfer rate, compared with other baseline methods.
- Score: 18.766260137886054
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Steady-state visual evoked potential (SSVEP) is one of the most commonly used
control signal 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 become urgent. In recent years,
developing the methods that can work in inter-subject classification scenario
has become a promising new direction. As the popular deep learning model
nowadays, Transformer has excellent performance and has been used in EEG signal
classification tasks. Therefore, in this study, we propose a deep learning
model for SSVEP classification based on Transformer structure in inter-subject
classification scenario, termed as SSVEPformer, which is the first application
of the transformer to the classification of SSVEP. Inspired by previous
studies, the model adopts the frequency spectrum of SSVEP data as input, and
explores the spectral and spatial domain information for classification.
Furthermore, to fully utilize the harmonic information, an extended SSVEPformer
based on the filter bank technology (FB-SSVEPformer) is proposed to further
improve the classification performance. Experiments were conducted using two
open datasets (Dataset 1: 10 subjects, 12-class task; Dataset 2: 35 subjects,
40-class task) in the inter-subject classification scenario. The experimental
results show that the proposed models could achieve better results in terms of
classification accuracy and information transfer rate, compared with other
baseline methods. The proposed model validates the feasibility of deep learning
models based on Transformer structure for SSVEP classification task, and could
serve as a potential model to alleviate the calibration procedure in the
practical application of SSVEP-based BCI systems.
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