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首页> 外文期刊>Neural Networks and Learning Systems, IEEE Transactions on >RSTFC: A Novel Algorithm for Spatio-Temporal Filtering and Classification of Single-Trial EEG
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RSTFC: A Novel Algorithm for Spatio-Temporal Filtering and Classification of Single-Trial EEG

机译：RSTFC：一种新的时空滤波和单试验脑电信号分类的算法

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摘要

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 -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.
机译：学习最佳时空滤波器是提取单次试验脑电图（EEG）分类特征的关键。挑战在于控制学习算法的复杂性，以减轻维度的诅咒并获得计算效率，以促进在线应用（例如脑机接口）的发展。为了解决这些障碍，本文提出了一种新颖的算法，用于单次EEG分类的正则化时空滤波和分类（RSTFC）。 RSTFC由两个模块组成。在特征提取模块中，开发了一种用于脑电信号的时空滤波监督的正则化算法。与现有的监督时空滤波器优化算法不同，该改进算法可以在特征值分解框架中同时优化空间和高阶时间滤波器，因此可以高效实现。在分类模块中，提出了一种用于稀疏Fisher线性判别分析的凸优化算法，用于同时特征选择和分类典型的高维时空滤波信号。通过在从17个受试者收集的三个脑机接口（BCI）竞争数据集上与几种最新方法进行比较，证明了RSTFC的有效性。结果表明，RSTFC产生的分类准确度明显高于竞争方法。本文还讨论了优化通道特定的时间滤波器优于优化所有通道共有的时间滤波器的优势。

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