Adaptive Spatio-Temporal Filtering for Movement Related Potentials in EEG-Based Brain-Computer Interfaces
Adaptive Spatio-Temporal Filtering for Movement Related Potentials in EEG-Based Brain-Computer Interfaces
复制标题
基于脑电图的大脑计算机接口中运动相关电位的自适应时空过滤
DOI：
10.1109/tnsre.2014.2315717
复制
发表时间：
2014-07-01
影响因子：
4.9
通讯作者：
McFarland, Dennis J.
中科院分区：
文献类型：
--
作者：
Lu, Jun;Xie, Kan;McFarland, Dennis J.
运动相关电位（MRPs）是许多基于脑电（EEG）的脑机接口（BCI）的特征。MRP特征提取是具有挑战性的，因为EEG是有噪声的并且在受试者之间变化。以前的研究使用空间和时空滤波方法来处理这些问题。然而，它们没有优化时间信息，或者当训练数据有限并且特征空间是高维时可能容易过拟合。此外，大多数研究手动选择数据窗口和低通频率。我们提出了一种自适应时空（AST）滤波方法，在低维空间中更准确地建模MRPs。AST通过采用高斯核来构造低通时频滤波器和线性岭回归（LRR）算法来计算空间滤波器来自动优化所有参数。最佳参数同时寻求通过最小化留一交叉验证误差，通过梯度下降。使用来自12个个体的四个BCI数据集，我们比较了AST滤波器与两种流行方法的性能：判别空间模式滤波器和正则化时空滤波器。结果表明，我们的AST过滤器可以作出更准确的预测，是计算上可行的。
Movement related potentials (MRPs) are used as features in many brain-computer interfaces (BCIs) based on electroencephalogram (EEG). MRP feature extraction is challenging since EEG is noisy and varies between subjects. Previous studies used spatial and spatio-temporal filtering methods to deal with these problems. However, they did not optimize temporal information or may have been susceptible to overfitting when training data are limited and the feature space is of high dimension. Furthermore, most of these studies manually select data windows and low-pass frequencies. We propose an adaptive spatio-temporal (AST) filtering method to model MRPs more accurately in lower dimensional space. AST automatically optimizes all parameters by employing a Gaussian kernel to construct a low-pass time-frequency filter and a linear ridge regression (LRR) algorithm to compute a spatial filter. Optimal parameters are simultaneously sought by minimizing leave-one-out cross-validation error through gradient descent. Using four BCI datasets from 12 individuals, we compare the performances of AST filter to two popular methods: the discriminant spatial pattern filter and regularized spatio-temporal filter. The results demonstrate that our AST filter can make more accurate predictions and is computationally feasible.