Author(s): Zhang, XY (Zhang, Xinyi); Qiu, S (Qiu, Shuang); Zhang, YK (Zhang, Yukun); Wang, KN (Wang, Kangning); Wang, YJ (Wang, Yijun); He, HG (He, Huiguang)

Source: JOURNAL OF NEURAL ENGINEERING Volume: 19 Issue: 4 Article Number: 046027 DOI: 10.1088/1741-2552/ac823e Published: AUG 1 2022

Abstract: Objective. Steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs) have attracted increasing attention due to their high information transfer rate. To improve the performance of SSVEP detection, we propose a bidirectional Siamese correlation analysis (bi-SiamCA) model. Approach. In this model, an long short-term memory-based Siamese architecture is designed to measure the similarity between the SSVEP signal and the template in each frequency and obtain the probability that the SSVEP signal belongs to each frequency. Additionally, a maximize agreement module with a designed contrastive loss is adopted in the Siamese architecture to increase the similarity between the SSVEP signal and the reference signal in the same frequency. Moreover, a two-way signal processing mechanism is built to effectively integrate complementary information from two temporal directions of the input signals. Our model uses raw SSVEPs as inputs and can be trained end-to-end. Main results. Experimental results on a 40-class dataset and a 12-class dataset indicate that bi-SiamCA can significantly improve the classification accuracy compared with the prominent traditional and deep learning methods, especially under short data lengths. Feature visualizations show that the similarity between the SSVEP signal and the reference signal in the same frequency gradually improved in our model. Conclusion. The proposed bi-SiamCA model enhances the performance of SSVEP detection and outperforms the compared methods. Significance. Due to its high decoding accuracy under short signals, our approach has great potential to implement a high-speed SSVEP-based BCI.

Accession Number: WOS:000834589000001

PubMed ID: 35853437

Author Identifiers:

Author        Web of Science ResearcherID        ORCID Number

Wang, Kangning                  0000-0001-6345-5086

He, Huiguang                  0000-0002-0684-1711

ISSN: 1741-2560

eISSN: 1741-2552

Full Text: https://iopscience.iop.org/article/10.1088/1741-2552/ac823e