Research on classification of Motor Imagery EEG signal based on CNN architecture

Abstract

BCI based on machine learning could makes use of the EEG signals to communicate to output under the condition of without the participation of peripheral nerves and muscles. Extracting the essential features of the EEG signals in the presence of artifacts, training the classification algorithms and optimizing the performance of classifier is critical procedure for BCI system. In the realization of BCI, the most important step is the feature extraction and classification of EEG signals. Due to the obvious individual difference and low signal-to-noise ratio of EEG signals, the current feature extraction and classification algorithms have low accuracy. The emergence of deep learning has attracted much attention in many fields. At present, some researchers try to apply deep learning algorithm to the recognition of EEG signals, and obtain good results. Based on convolutional Neural Networks (CNN), this paper studies the application of deep learning in motor imagery task classification by end-to-end deep learning.