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

• Published in: IEEE journal of biomedical and health informatics Vol. 28; no. 11; pp. 6581 - 6593
• Main Authors: Liu, Jiawei, Wang, Ruimin, Yang, Yuankui, Zong, Yuan, Leng, Yue, Zheng, Wenming, Ge, Sheng
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.11.2024
• Subjects: Adult; Algorithms; Brain modeling; Brain-computer interface; Brain-Computer Interfaces; Calibration; Convolution; Correlation; Data models; domain generalization; Electroencephalography - classification; Electroencephalography - methods; Evoked Potentials, Visual - physiology; Feature extraction; Humans; Male; Signal Processing, Computer-Assisted; steady-state visual evoked potentials; transformer; Transformers
• ISSN: 2168-2194; 2168-2208; 2168-2208
• DOI: 10.1109/JBHI.2024.3454158

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.