IEEE Trans Biomed Eng. 2023 Jun;70(6):1775-1785. doi: 10.1109/TBME.2022.3227036. Epub 2023 May 19.
Currently, ensemble task-related component analysis (eTRCA) and task discriminative component analysis (TDCA) are the state-of-the-art algorithms for steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs). However, training the BCIs requires multiple calibration trials. With insufficient calibration data, the accuracy of the BCI will degrade, or even become invalid with only one calibration trial. However, collecting a large amount of electroencephalography (EEG) data for calibration is a time-consuming and laborious process, which hinders the practical use of eTRCA and TDCA.
This study proposed a novel method, namely Source Aliasing Matrix Estimation (SAME), to augment the calibration data for SSVEP-BCIs. SAME could generate artificial EEG trials with the featured SSVEPs. Its effectiveness was evaluated using two public datasets (i.e., Benchmark, BETA).
When combined with SAME, both eTRCA and TDCA had significantly improved performance with a limited number of calibration data. Specifically, SAME increased the average accuracy of eTRCA and TDCA by about 12% and 3%, respectively, with as few as two calibration trials. Notably, SAME enabled eTRCA and TDCA to work well with a single calibration trial, achieving an average accuracy >90% for the Benchmark dataset and >70% for the BETA dataset with 1-second EEG.
SAME is an effective method for SSVEP-BCIs to augment the calibration data, thereby significantly enhancing the performance of eTRCA and TDCA.
We propose a new data-augmentation method that is compatible with the state-of-the-art algorithms of SSVEP-based BCIs. It can significantly reduce the efforts required to calibrate SSVEP-BCIs, which is promising for the development of practical BCIs.
目前，基于稳态视觉诱发电位（SSVEP）的脑-机接口（BCI）的主流算法是集成任务相关成分分析（eTRCA）和任务判别成分分析（TDCA）。然而，训练 BCI 需要多次校准试验。如果校准数据不足，BCI 的准确性将会下降，甚至在仅有一次校准试验的情况下变得无效。然而，为了校准而收集大量的脑电图（EEG）数据是一个耗时且费力的过程，这阻碍了 eTRCA 和 TDCA 的实际应用。
本研究提出了一种新的方法，即源混淆矩阵估计（SAME），用于扩充 SSVEP-BCI 的校准数据。SAME 可以用具有特征 SSVEPs 的人工 EEG 试验来生成。我们使用两个公共数据集（即 Benchmark 和 BETA）来评估 SAME 的有效性。
当与 SAME 结合使用时，eTRCA 和 TDCA 在有限的校准数据下的性能都有显著提高。具体来说，SAME 使 eTRCA 和 TDCA 的平均准确性分别提高了约 12%和 3%，而仅使用两个校准试验。值得注意的是，SAME 使得 eTRCA 和 TDCA 可以在单个校准试验下正常工作，在 Benchmark 数据集上的平均准确率超过 90%，在 BETA 数据集上的平均准确率超过 70%，EEG 时间为 1 秒。
SAME 是一种用于扩充 SSVEP-BCI 校准数据的有效方法，从而显著提高了 eTRCA 和 TDCA 的性能。
我们提出了一种新的数据增强方法，与基于 SSVEP 的 BCI 的最新算法兼容。它可以显著减少校准 SSVEP-BCI 所需的工作，这对于实用的 BCI 的发展具有广阔的前景。
