IEEE Trans Biomed Eng. 2024 Apr;71(4):1319-1331. doi: 10.1109/TBME.2023.3333435. Epub 2024 Mar 20.
Spatial filtering and template matching-based steady-state visually evoked potentials (SSVEP) identification methods usually underperform in SSVEP identification with small-sample-size calibration data, especially when a single trial of data is available for each stimulation frequency.
In contrast to the state-of-the-art task-related component analysis (TRCA)-based methods, which construct spatial filters and SSVEP templates based on the inter-trial task-related components in SSVEP, this study proposes a method called periodically repeated component analysis (PRCA), which constructs spatial filters to maximize the reproducibility across periods and constructs synthetic SSVEP templates by replicating the periodically repeated components (PRCs). We also introduced PRCs into two improved variants of TRCA. Performance evaluation was conducted in a self-collected 16-target dataset, a public 40-target dataset, and an online experiment.
The proposed methods show significant performance improvements with less training data and can achieve comparable performance to the baseline methods with 5 trials by using 2 or 3 training trials. Using a single trial of calibration data for each frequency, the PRCA-based methods achieved the highest average accuracies of over 95% and 90% with a data length of 1 s and maximum average information transfer rates (ITR) of 198.8±57.3 bits/min and 191.2±48.1 bits/min for the two datasets, respectively. Averaged online accuracy of 94.00 ± 7.35% and ITR of 139.73±21.04 bits/min were achieved with 0.5-s calibration data per frequency.
Our results demonstrate the effectiveness and robustness of PRCA-based methods for SSVEP identification with reduced calibration effort and suggest its potential for practical applications in SSVEP-BCIs.
基于空间滤波和模板匹配的稳态视觉诱发电位（SSVEP）识别方法在小样本量校准数据的 SSVEP 识别中表现通常不佳，尤其是在每个刺激频率仅有单个试次数据可用的情况下。
与基于任务相关成分分析（TRCA）的最新方法不同，该方法基于 SSVEP 中的跨试次任务相关成分构建空间滤波器和 SSVEP 模板，本研究提出了一种称为周期性重复成分分析（PRCA）的方法，该方法构建空间滤波器以最大限度地提高跨周期的可重复性，并通过复制周期性重复成分（PRCs）构建合成 SSVEP 模板。我们还将 PRCs 引入到 TRCA 的两个改进变体中。性能评估在我们自行收集的 16 目标数据集、公共的 40 目标数据集和在线实验中进行。
与基线方法相比，所提出的方法在使用较少的训练数据时表现出显著的性能提升，并且可以在使用 2 或 3 个训练试次的情况下，实现与使用 5 个试次的基线方法相当的性能。对于每个频率仅使用单个试次的校准数据，基于 PRCA 的方法分别在两个数据集上实现了超过 95%和 90%的平均最高精度，数据长度为 1 s 时的最大平均信息传输率（ITR）分别为 198.8±57.3 bits/min 和 191.2±48.1 bits/min。在每个频率使用 0.5 s 的校准数据时，在线平均精度为 94.00 ± 7.35%，ITR 为 139.73±21.04 bits/min。
我们的结果证明了 PRCA 方法在减少校准工作量的情况下进行 SSVEP 识别的有效性和鲁棒性，并表明其在 SSVEP-BCI 中的实际应用潜力。
