Wang Lei, Xu Guizhi, Wang Jiang, Yang Shuo, Yan Weili
Province-Ministry Joint Key Laboratory of Electromagnetic Field and Electrical Apparatus Reliability, Hebei University of Technology, Tianjin, China.
Annu Int Conf IEEE Eng Med Biol Soc. 2008;2008:3848-51. doi: 10.1109/IEMBS.2008.4650049.
A motor based Brain-Computer Interface (BCI) translates the subject's motor intention into a control signal by means of the method which extracts characteristic feature from EEG recorded from the scalp. In this paper, the EEG signal recorded during three motor imagery tasks, which were imagination of left hand, right hand and foot movements, was investigated. A novel method named Hilbert-Huang transform (HHT) is introduced to extract the feature from signal. Firstly, raw signal is decomposed using Empirical Mode Decomposition (EMD). And then, several Intrinsic Mode Functions (IMF) are gained. For further study, the IMFs whose main frequency is higher than 5 Hz are selected. Secondly, based on the IMFs selected above, Hilbert spectrum is calculated. In each motor imagery task, local instantaneous energies, within specific frequency band of electrode C3 and C4, are selected as the features. A three-layer BP Neural Network classifier is structured for pattern classification. The classification results show that HHT can be used in EEG-based BCI research as a method to analysis non-linear and non-stationary signal.
基于运动的脑机接口（BCI）通过从头皮记录的脑电图（EEG）中提取特征的方法，将受试者的运动意图转换为控制信号。本文研究了在三种运动想象任务（即左手、右手和脚部运动想象）期间记录的EEG信号。引入了一种名为希尔伯特-黄变换（HHT）的新方法来从信号中提取特征。首先，使用经验模态分解（EMD）对原始信号进行分解，然后获得几个本征模态函数（IMF）。为了进一步研究，选择主频率高于5Hz的IMF。其次，基于上述选择的IMF计算希尔伯特谱。在每个运动想象任务中，选择电极C3和C4特定频带内的局部瞬时能量作为特征。构建了一个三层BP神经网络分类器进行模式分类。分类结果表明，HHT可作为一种分析非线性和非平稳信号的方法用于基于EEG的BCI研究。
