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Convolutional Transformer-Based Cross Subject Model for SSVEP-Based BCI Classification | IEEE Journals & Magazine | IEEE Xplore

Convolutional Transformer-Based Cross Subject Model for SSVEP-Based BCI Classification


A convolutional transformer-based cross subject generalization model for SSVEP-based BCI classification.

Abstract:

Steady-state visual evoked potential (SSVEP) is a commonly used brain-computer interface (BCI) paradigm. The performance of cross-subject SSVEP classification has a stron...Show More

Abstract:

Steady-state visual evoked potential (SSVEP) is a commonly used brain-computer interface (BCI) paradigm. The performance of cross-subject SSVEP classification has a strong impact on SSVEP-BCI. This study designed a cross subject generalization SSVEP classification model based on an improved transformer structure that uses domain generalization (DG). The global receptive field of multi-head self-attention is used to learn the global generalized SSVEP temporal information across subjects. This is combined with a parallel local convolution module, designed to avoid oversmoothing the oscillation characteristics of temporal SSVEP data and better fit the feature. Moreover, to improve the cross-subject calibration-free SSVEP classification performance, an DG method named StableNet is combined with the proposed convolutional transformer structure to form the DG-Conformer method, which can eliminate spurious correlations between SSVEP discriminative information and background noise to improve cross-subject generalization. Experiments on two public datasets, Benchmark and BETA, demonstrated the outstanding performance of the proposed DG-Conformer compared with other calibration-free methods, FBCCA, tt-CCA, Compact-CNN, FB-tCNN, and SSVEPNet. Additionally, DG-Conformer outperforms the classic calibration-required algorithms eCCA, eTRCA and eSSCOR when calibration is used. An incomplete partial stimulus calibration scheme was also explored on the Benchmark dataset, and it was demonstrated to be a potential solution for further high-performance personalized SSVEP-BCI with quick calibration.
A convolutional transformer-based cross subject generalization model for SSVEP-based BCI classification.
Published in: IEEE Journal of Biomedical and Health Informatics ( Volume: 28, Issue: 11, November 2024)
Page(s): 6581 - 6593
Date of Publication: 03 September 2024

ISSN Information:

PubMed ID: 39226201

Funding Agency:


References

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