基于条件经验模式分解和串并行CNN的脑电信号识别

针对运动想象脑电信号(EEG)的非线性、非平稳特点，该文提出一种结合条件经验模式分解(CEMD)和串并行卷积神经网络(SPCNN)的脑电信号识别方法。在CEMD过程中，采用各阶固有模式分量(IMF)与原始信号的相关性系数作为第1个IMF筛选条件，在此基础上，提出各阶IMF之间的相对能量占有率作为第2个IMF筛选条件。此外，为了考虑脑电信号各个通道之间的特征和突出每个通道内的特征，该文提出SPCNN网络模型对进行CEMD过程后的脑电信号进行分类。实验结果表明，在自行采集的脑电数据集上平均识别率达到94.58%。在公开数据集BCI competition IV 2b上平均识别率达到82.13%，比卷积神经网络提高了3.85%。最后，在自行设计的智能轮椅脑电控制平台上进行了轮椅前进、左转和右转在线控制实验，验证了该文算法对脑电信号识别的有效性。

Conditional Empirical Mode Decomposition and Serial Parallel CNN for ElectroEncephaloGram Signal Recognition

For the non-linear and non-stationary characteristics of motor imagery ElectroEncephaloGram (EEG) signals, an EEG signal recognition method based on Conditional Empirical Mode Decomposition (CEMD) and Serial Parallel Convolutional Neural Network (SPCNN) is proposed. In the CEMD process, the correlation coefficient between the Intrinsic Mode Functions (IMFs) and the original signal is used as the first condition to select IMFs. Based on this, the relative energy occupancy rates between the IMFs are proposed as the second condition to select IMFs. Further, to consider the characteristics between the EEG signal channels and highlight the features in each EEG signal channel, a SPCNN model is proposed to classify the processed EEG signals. The experimental results show that the average recognition rate reaches 94.58% on the dataset collected by ourselves. And the average recognition rate reaches 82.13% on the BCI competition IV 2b dataset, which is 3.85% higher than the average recognition rate of convolutional neural network. Finally, the online control experiments are carried out on the designed intelligent wheelchair platform, which proves the effectiveness of the proposed algorithm for EEG signals recognition.