Motor imagery EEG recognition based on conditional optimization empirical mode decomposition and multi-scale convolutional neural network

Abstract

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.