IEEE Trans Cybern. 2016 Dec;46(12):3171-3180. doi: 10.1109/TCYB.2015.2498974. Epub 2015 Nov 26.
The aim of this paper is to propose a real-time classification algorithm for the low-amplitude electroencephalography (EEG) signals, such as those produced by remembering an unpleasant odor, to drive a brain-computer interface. The peculiarity of these EEG signals is that they require ad hoc signals preprocessing by wavelet decomposition, and the definition of a set of features able to characterize the signals and to discriminate among different conditions. The proposed method is completely parameterized, aiming at a multiclass classification and it might be considered in the framework of machine learning. It is a two stages algorithm. The first stage is offline and it is devoted to the determination of a suitable set of features and to the training of a classifier. The second stage, the real-time one, is to test the proposed method on new data. In order to avoid redundancy in the set of features, the principal components analysis is adapted to the specific EEG signal characteristics and it is applied; the classification is performed through the support vector machine. Experimental tests on ten subjects, demonstrating the good performance of the algorithm in terms of both accuracy and efficiency, are also reported and discussed.
本文旨在提出一种针对低频振幅脑电图 (EEG) 信号的实时分类算法，例如记忆难闻气味时产生的 EEG 信号，以便驱动脑机接口。这些 EEG 信号的特点是需要通过小波分解进行特定的信号预处理，并定义一组能够描述信号并区分不同条件的特征。所提出的方法是完全参数化的，旨在进行多类分类，可以在机器学习的框架内进行考虑。它是一种两阶段算法。第一阶段是离线的，致力于确定合适的特征集和训练分类器。第二阶段是实时阶段，用于在新数据上测试所提出的方法。为了避免特征集中的冗余，主成分分析适用于特定的 EEG 信号特征，并进行应用；分类通过支持向量机进行。还报告和讨论了对十个受试者进行的实验测试，证明了该算法在准确性和效率方面的良好性能。
