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.

客观。滤波器组典型相关分析 (FBCCA) 是一种广泛使用的分类方法，在基于稳态视觉诱发电位 (SSVEP) 的脑机接口 (BCI) 中实现。然而，用于 SSVEP 识别问题的传统检测算法，包括 FBCCA，通常基于“固定窗口”策略。也就是说，这些算法总是分析固定长度的数据。本研究致力于通过设计一种新的动态窗口策略来提高基于 SSVEP 的 BCI 的性能，该策略可以自动找到最佳数据长度以实现更高的信息传输率 (ITR)。方法. “动态窗口”的主要目的是在保持高精度的同时最小化所需的数据长度。本研究通过softmax函数将FBCCA的相关系数投影到概率空间中，并建立了假设检验模型，该模型以风险函数作为对分类结果“可信度”的评价。为了评估该方法的优越性，采用固定数据长度的FBCCA（FBCCA-FW）和时空均衡动态窗口（STE-DW）进行比较。主要结果。14 名健康受试者的结果由 40 目标在线基于 SSVEP 的 BCI 拼写系统得出。结果表明，这种提出的方​​法在准确性和 ITR 方面明显优于 STE-DW 和 FBCCA-FW。意义. 本研究结合 FBCCA 和动态窗口策略的基本思想，提出了一种新的免训练动态优化算法，显着提高了基于 SSVEP 的在线 BCI 系统的性能。