RSTFC: A Novel Algorithm for Spatio-Temporal Filtering and Classification of Single-Trial EEG
RSTFC: A Novel Algorithm for Spatio-Temporal Filtering and Classification of Single-Trial EEG
复制标题
RSTFC：单次试验脑电图时空过滤和分类的新算法
DOI：
10.1109/tnnls.2015.2402694
复制
发表时间：
2015-12-01
影响因子：
10.4
通讯作者：
Wu, Wei
中科院分区：
文献类型：
--
作者：
Qi, Feifei;Li, Yuanqing;Wu, Wei
学习最优时空滤波器是单次脑电（EEG）特征提取的关键。挑战是控制学习算法的复杂性，以便减轻维数灾难，并获得计算效率以促进在线应用，例如，脑机接口（BCI）。为了解决这些障碍，本文提出了一种新的算法，称为正则化时空滤波和分类（RSTFC），单次试验EEG分类。RSTFC由两个模块组成。在特征提取模块中，提出了一种l（2）-正则化算法对脑电信号进行有监督的时空滤波。与现有的监督时空滤波器优化算法不同，该算法可以同时优化空间和高阶时间滤波器的特征值分解框架，从而实现高效。在分类模块中，提出了一种稀疏Fisher线性鉴别分析的凸优化算法，用于对典型的高维空时滤波信号同时进行特征选择和分类。RSTFC的有效性证明了它与几个国家的最先进的方法在三个脑-机接口（BCI）的竞争数据集收集了17个主题。结果表明，RSTFC产生显着更高的分类精度比竞争的方法。本文还讨论了优化通道特定的时间滤波器的优点，优化时间滤波器的共同所有通道。
Learning optimal spatio-temporal filters is a key to feature extraction for single-trial electroencephalogram (EEG) classification. The challenges are controlling the complexity of the learning algorithm so as to alleviate the curse of dimensionality and attaining computational efficiency to facilitate online applications, e.g., brain-computer interfaces (BCIs). To tackle these barriers, this paper presents a novel algorithm, termed regularized spatio-temporal filtering and classification (RSTFC), for single-trial EEG classification. RSTFC consists of two modules. In the feature extraction module, an l(2)-regularized algorithm is developed for supervised spatio-temporal filtering of the EEG signals. Unlike the existing supervised spatio-temporal filter optimization algorithms, the developed algorithm can simultaneously optimize spatial and high-order temporal filters in an eigenvalue decomposition framework and thus be implemented highly efficiently. In the classification module, a convex optimization algorithm for sparse Fisher linear discriminant analysis is proposed for simultaneous feature selection and classification of the typically high-dimensional spatio-temporally filtered signals. The effectiveness of RSTFC is demonstrated by comparing it with several state-of-the-arts methods on three brain-computer interface (BCI) competition data sets collected from 17 subjects. Results indicate that RSTFC yields significantly higher classification accuracies than the competing methods. This paper also discusses the advantage of optimizing channel-specific temporal filters over optimizing a temporal filter common to all channels.