IEEE Trans Neural Syst Rehabil Eng. 2024;32:1606-1615. doi: 10.1109/TNSRE.2024.3387283. Epub 2024 Apr 18.
Steady-state visual evoked potential (SSVEP), one of the most popular electroencephalography (EEG)-based brain-computer interface (BCI) paradigms, can achieve high performance using calibration-based recognition algorithms. As calibration-based recognition algorithms are time-consuming to collect calibration data, the least-squares transformation (LST) has been used to reduce the calibration effort for SSVEP-based BCI. However, the transformation matrices constructed by current LST methods are not precise enough, resulting in large differences between the transformed data and the real data of the target subject. This ultimately leads to the constructed spatial filters and reference templates not being effective enough. To address these issues, this paper proposes multi-stimulus LST with online adaptation scheme (ms-LST-OA).
The proposed ms-LST-OA consists of two parts. Firstly, to improve the precision of the transformation matrices, we propose the multi-stimulus LST (ms-LST) using cross-stimulus learning scheme as the cross-subject data transformation method. The ms-LST uses the data from neighboring stimuli to construct a higher precision transformation matrix for each stimulus to reduce the differences between transformed data and real data. Secondly, to further optimize the constructed spatial filters and reference templates, we use an online adaptation scheme to learn more features of the EEG signals of the target subject through an iterative process trial-by-trial.
ms-LST-OA performance was measured for three datasets (Benchmark Dataset, BETA Dataset, and UCSD Dataset). Using few calibration data, the ITR of ms-LST-OA achieved 210.01±10.10 bits/min, 172.31±7.26 bits/min, and 139.04±14.90 bits/min for all three datasets, respectively.
Using ms-LST-OA can reduce calibration effort for SSVEP-based BCIs.
稳态视觉诱发电位（SSVEP）是最受欢迎的基于脑电图（EEG）的脑机接口（BCI）范式之一，使用基于校准的识别算法可以实现高性能。由于基于校准的识别算法在收集校准数据时耗时较长，因此已使用最小二乘变换（LST）来减少基于 SSVEP 的 BCI 的校准工作量。然而，当前 LST 方法构建的变换矩阵不够精确，导致变换后的数据与目标对象的真实数据之间存在较大差异。这最终导致构建的空间滤波器和参考模板不够有效。为了解决这些问题，本文提出了具有在线自适应方案的多刺激 LST（ms-LST-OA）。
所提出的 ms-LST-OA 由两部分组成。首先，为了提高变换矩阵的精度，我们提出了使用跨刺激学习方案的多刺激 LST（ms-LST）作为跨主体数据变换方法。ms-LST 使用来自相邻刺激的数据为每个刺激构建更精确的变换矩阵，以减少变换后数据与真实数据之间的差异。其次，为了进一步优化构建的空间滤波器和参考模板，我们使用在线自适应方案通过迭代过程逐试学习目标对象 EEG 信号的更多特征。
ms-LST-OA 的性能在三个数据集（基准数据集、BETA 数据集和 UCSD 数据集）中进行了测量。使用少量校准数据，ms-LST-OA 的 ITR 分别达到了 210.01±10.10 bits/min、172.31±7.26 bits/min 和 139.04±14.90 bits/min。
使用 ms-LST-OA 可以减少基于 SSVEP 的 BCI 的校准工作量。
