Robust Similarity Measurement Based on a Novel Time Filter for SSVEPs Detection

• Published in: IEEE transaction on neural networks and learning systems Vol. 34; no. 8; pp. 4096 - 4105
• Main Authors: Jin, Jing, Wang, Zhiqiang, Xu, Ren, Liu, Chang, Wang, Xingyu, Cichocki, Andrzej
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.08.2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Brain; Brain–computer interface(BCI); Computer applications; Correlation; Correlation coefficient; Correlation coefficients; Human-computer interface; Implants; Linear programming; Measurement methods; Objective function; Robustness; Similarity; similarity measurement; Singular value decomposition; Spatial filtering; Steady-state; steady-state visual evoked potential (SSVEP); Task analysis; task-related component analysis (TRCA); time filter; Time measurement; Training; Visual evoked potentials; Visualization
• ISSN: 2162-237X; 2162-2388; 2162-2388
• DOI: 10.1109/TNNLS.2021.3118468

The steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI) has received extensive attention in research for the less training time, excellent recognition performance, and high information translate rate. At present, most of the powerful SSVEPs detection methods are similarity measurements based on spatial filters and Pearson's correlation coefficient. Among them, the task-related component analysis (TRCA)-based method and its variant, the ensemble TRCA (eTRCA)-based method, are two methods with high performance and great potential. However, they have a defect, that is, they can only suppress certain kinds of noise, but not more general noises. To solve this problem, a novel time filter was designed by introducing the temporally local weighting into the objective function of the TRCA-based method and using the singular value decomposition. Based on this, the time filter and (e)TRCA-based similarity measurement methods were proposed, which can perform a robust similarity measure to enhance the detection ability of SSVEPs. A benchmark dataset recorded from 35 subjects was used to evaluate the proposed methods and compare them with the (e)TRCA-based methods. The results indicated that the proposed methods performed significantly better than the (e)TRCA-based methods. Therefore, it is believed that the proposed time filter and the similarity measurement methods have promising potential for SSVEPs detection.