Incorporating Neighboring Stimuli Data for Enhanced SSVEP-Based BCIs

• Published in: IEEE transactions on instrumentation and measurement Vol. 71; p. 1
• Main Authors: Huang, Jiayang, Yang, Pengfei, Xiong, Bang, Wang, Quan, Wan, Bo, Ruan, Ziling, Yang, Keyi, Zhang, Zhi-Qiang
• Format: Journal Article
• Language: English
• Published: New York IEEE 2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Brain-computer interfaces (BCIs); Correlation; Data mining; Datasets; Electroencephalography; Information transfer; neighboring stimuli; Object detection; Performance evaluation; spatial filter; Spatial filtering; steady-state visual evoked potential (SSVEP); Stimuli; Target detection; Target recognition; Training; Training data; Visualization
• ISSN: 0018-9456; 1557-9662
• DOI: 10.1109/TIM.2022.3219497

Various spatial filters have been proposed to enhance the target identification performance of steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs). The current methods only extract the target-related information from the corresponding stimulus to learn the spatial filter parameter. However, the SSVEP data from neighboring stimuli also contain frequency information of the target stimulus, which could be utilized to further improve the target identification performance. In this paper, we propose a new method incorporating SSVEPs from the neighboring stimuli to strengthen the target-related frequency information. First, The spatial filter is obtained by maximizing the summation of covariances of SSVEP data corresponding to the target and its neighboring stimuli. Then the correlation features between spatially filtered templates and test data are calculated for target detection. For the performance evaluation, we implemented the offline experiment using the 40-class benchmark dataset from 35 subjects and the 12-target self-collected dataset from 11 subjects. Compared with the state-of-art spatial filtering methods, the proposed method showed superiority in classification accuracy and information transfer rate (ITR). The comparison results demonstrate the effectiveness of the proposed spatial filter for target identification in SSVEP-based BCIs.