Motor imagery EEG recognition based on conditional optimization empirical mode decomposition and multi-scale convolutional neural network
Motor imagery EEG recognition based on conditional optimization empirical mode decomposition and multi-scale convolutional neural network
复制标题
基于条件优化经验模态分解和多尺度卷积神经网络的运动想象脑电识别
DOI：
10.1016/j.eswa.2020.113285
复制
发表时间：
2020-07-01
影响因子：
8.5
通讯作者：
Dang, Xiaoyuan
中科院分区：
文献类型：
--
作者：
Tang, Xianlun;Li, Wei;Dang, Xiaoyuan
脑电信号分类在脑机接口系统中起着至关重要的作用。然而，EEG信号固有的复杂特性使得对其进行分析和建模具有挑战性。提出了一种基于条件经验模式分解（CEMD）和一维多尺度卷积神经网络（1DMSCNN）的运动想象脑电信号识别方法。CEMD算法以原始脑电信号与各固有模态分量（IMF）之间的相关系数为第一条件选择IMF，以IMF之间的相对能量占用率为第二条件。采用CEMD算法对脑电信号进行去噪处理。然后，提出了一种脑电信号合成方法，用于对通道间的事件相关同步/去同步（ERS/ERD）信息进行编码。最后，一个称为1DMSCNN的模型被建立来分类处理后的EEG信号。所提出的方法被应用到在我们的实验室和BCI竞争IV数据集2b收集的数据集。实验结果表明，与现有的方法相比，该方法可以获得更高的分类精度。此外，将该算法应用于脑电信号的在线识别，设计并实现了一个直接与大脑和轮椅交互的脑机接口系统。该系统可以通过脑电信号直接控制轮椅左右转向。在线实验结果表明，所设计的智能轮椅系统是一种可行的脑机接口应用。验证了该算法可用于专家系统和智能系统。我们的方法可以提供一个刺激发展的人机交互。(C)2020爱思唯尔有限公司保留所有权利。
Electroencephalogram (EEG) signals classification plays a crucial role in brain computer interfaces (BCIs) system. However, the inherent complex properties of EEG signals make it challenging to get them analyzed and modeled. In this paper, a novel method based on conditional empirical mode decomposition (CEMD) and one-dimensional multi-scale convolutional neural network (1DMSCNN) is proposed to recognize motor imagery (MI) EEG signals. In the CEMD algorithm, the correlation coefficient between the original EEG signal and each intrinsic modal component (IMF) is used as the first condition to select IMFs, and the relative energy occupancy rates between the IMFs are the second condition. The CEMD algorithm is applied to remove the noise of EEG signals. Then, an EEG signals combination method is proposed to encode event-related synchronization/de-synchronization (ERS/ERD) information between the channels. Finally, a model called 1DMSCNN is built to classify the processed EEG signals. The proposed method is applied to the dataset collected in our laboratory and BCI competition IV dataset 2b. The results indicate that the proposed method can achieve higher accuracy for EEG signals classification, compared with other state-of-the-art works. In addition, the proposed algorithm is applied to the online recognition of EEG signals, a BCI system that directly interacts with brain and wheelchair is designed and implemented. This system can directly command wheelchair to turn left and right through EEG signals. The online experimental results indicate that the designed intelligent wheelchair system is a feasible BCI application. It verifies the proposed algorithm can be used in expert and intelligent systems. Our method can provide a stimulus to the development of human-robot interaction. (C) 2020 Elsevier Ltd. All rights reserved.