Author(s): Chen, YH (Chen, Yonghao); Yang, C (Yang, Chen); Chen, XG (Chen, Xiaogang); Wang, YJ (Wang, Yijun); Gao, XR (Gao, Xiaorong)

Source: JOURNAL OF NEURAL ENGINEERING Volume: 18 Issue: 3 Article Number: 036007 DOI: 10.1088/1741-2552/ab914e Published: JUN 2021

Abstract: Objective. Filter bank canonical correlation analysis (FBCCA) is a widely-used classification approach implemented in steady-state visual evoked potential (SSVEP)-based brain computer interfaces (BCIs). However, conventional detection algorithms for SSVEP recognition problems, including the FBCCA, were usually based on 'fixed window' strategy. That's to say, these algorithms always analyze data with fixed length. This study devoted to enhance the performance of SSVEP-based BCIs by designing a new dynamic window strategy which automatically finds an optimal data length to achieve higher information transfer rate (ITR). Approach. The main purpose of 'dynamic window' is to minimize the required data length while maintaining high accuracy. This study projected the correlation coefficients of FBCCA into probability space by softmax function and built a hypothesis testing model, which took risk function as evaluation of classification result's 'credibility'. In order to evaluate the superiority of this approach, FBCCA with fixed data length (FBCCA-FW) and spatial temporal equalization dynamic window (STE-DW) were implemented for comparison. Main results. Fourteen healthy subjects' results were concluded by a 40-target online SSVEP-based BCI speller system. The results suggest that this proposed approach significantly outperforms STE-DW and FBCCA-FW in terms of accuracy and ITR. Significance. By incorporating the fundamental ideas of FBCCA and dynamic window strategy, this study proposed a new training-free dynamical optimization algorithm, which significantly improved the performance of online SSVEP-based BCI systems.

Accession Number: WOS:000626880200001

PubMed ID: 32380480

Author Identifiers:

Author        Web of Science ResearcherID        ORCID Number

Chen, Xiaogang                  0000-0002-5334-1728

Chen, Yonghao                  0000-0002-8788-830X

ISSN: 1741-2560

eISSN: 1741-2552

Full Text: https://iopscience.iop.org/article/10.1088/1741-2552/ab914e