Yu Ke, Ai-Nashash Hasan, Thakor Nitish, Li Xiaoping
Singapore Institute for Neurotechnology, National University of Singapore, Singapore.
Singapore Institute for Neurotechnology, National University of Singapore, Singapore; Department of Electrical Engineering, American University of Sharjah, Sharjah, United Arab Emirates.
PLoS One. 2014 Jun 16;9(6):e100097. doi: 10.1371/journal.pone.0100097. eCollection 2014.
The linear discriminant analysis (LDA) method is a classical and commonly utilized technique for dimensionality reduction and classification in brain-computer interface (BCI) systems. Being a first-order discriminator, LDA is usually preceded by the feature extraction of electroencephalogram (EEG) signals, as multi-density EEG data are of second order. In this study, an analytic bilinear classification method which inherits and extends LDA is proposed. This method considers 2-dimentional EEG signals as the feature input and performs classification using the optimized complex-valued bilinear projections. Without being transformed into frequency domain, the complex-valued bilinear projections essentially spatially and temporally modulate the phases and magnitudes of slow event-related potentials (ERPs) elicited by distinct brain states in the sense that they become more separable. The results show that the proposed method has demonstrated its discriminating capability in the development of a rapid image triage (RIT) system, which is a challenging variant of BCIs due to the fast presentation speed and consequently overlapping of ERPs.
线性判别分析（LDA）方法是脑机接口（BCI）系统中用于降维和分类的一种经典且常用的技术。作为一阶判别器，LDA通常在脑电图（EEG）信号特征提取之后，因为多密度EEG数据是二阶的。在本研究中，提出了一种继承并扩展LDA的解析双线性分类方法。该方法将二维EEG信号作为特征输入，并使用优化的复值双线性投影进行分类。复值双线性投影无需转换到频域，本质上在空间和时间上对不同脑状态引发的慢事件相关电位（ERP）的相位和幅度进行调制，从而使它们更易于分离。结果表明，该方法在快速图像分类（RIT）系统的开发中已展示出其判别能力，由于快速呈现速度以及随之而来的ERP重叠，RIT系统是BCI中一个具有挑战性的变体。
