Journal
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
Volume 34, Issue 8, Pages 4096-4105Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TNNLS.2021.3118468
Keywords
Time measurement; Training; Task analysis; Correlation; Visualization; Steady-state; Linear programming; Brain-computer interface(BCI); similarity measurement; steady-state visual evoked potential (SSVEP); task-related component analysis (TRCA); time filter
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The steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI) has been widely studied due to its advantages in training time, recognition performance, and information transmission rate. This article introduces a novel time filter and similarity measurement methods based on task-related component analysis (TRCA) to improve the detection ability of SSVEPs. Experimental results demonstrate that the proposed methods outperform existing methods and show promising potential for SSVEP detection.
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.
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