Center for Engineering, Modeling and Applied Social Sciences (CECS), Federal University of ABC (UFABC), Santo André 09280-560, SP, Brazil.
Department of Computer Engineering and Automation (DCA), Universidade Estadual de Campinas (UNICAMP), Campinas 13083-852, SP, Brazil.
Sensors (Basel). 2024 Aug 22;24(16):5428. doi: 10.3390/s24165428.
Independent vector analysis (IVA) can be viewed as an extension of independent component analysis (ICA) to multiple datasets. It exploits the statistical dependency between different datasets through mutual information. In the context of motor imagery classification based on electroencephalogram (EEG) signals for the brain-computer interface (BCI), several methods have been proposed to extract features efficiently, mainly based on common spatial patterns, filter banks, and deep learning. However, most methods use only one dataset at a time, which may not be sufficient for dealing with a multi-source retrieving problem in certain scenarios. From this perspective, this paper proposes an original approach for feature extraction through multiple datasets based on IVA to improve the classification of EEG-based motor imagery movements. The IVA components were used as features to classify imagined movements using consolidated classifiers (support vector machines and K-nearest neighbors) and deep classifiers (EEGNet and EEGInception). The results show an interesting performance concerning the clustering of MI-based BCI patients, and the proposed method reached an average accuracy of 86.7%.
独立向量分析（IVA）可以被视为对多个数据集的独立成分分析（ICA）的扩展。它通过互信息利用不同数据集之间的统计相关性。在基于脑电信号（EEG）的运动想象分类的背景下，已经提出了几种有效的特征提取方法，主要基于共同空间模式、滤波器组和深度学习。然而，大多数方法一次只使用一个数据集，这在某些场景下可能不足以处理多源检索问题。从这个角度来看，本文提出了一种基于 IVA 的多数据集特征提取的原始方法，以提高基于 EEG 的运动想象运动的分类。使用整合分类器（支持向量机和 K-最近邻）和深度分类器（EEGNet 和 EEGInception），将 IVA 分量用作特征来对想象运动进行分类。结果表明，在基于 MI 的 BCI 患者的聚类方面表现出了有趣的性能，所提出的方法达到了 86.7%的平均准确率。
