Fusion Convolutional Neural Network for Multi-Class Motor Imagery of EEG Signals Classification

Classification of EEG signals based on motor imagery is an important task in Brain-Computer Interface (BCI). Deep learning approaches have been successfully used in several recent applications to learn features and classify different types of data. However, the number of researches using these approaches in BCI applications is very limited. In this paper, we aim at using the fusion of Convolutional Neural Networks (CNN) methods to improve the classification performance of EEG motor imagery signals in the framework of e-health Internet of Things. We propose and compare two classification methods based on the fusion of two CNNs. Our results show that the fusion of the CNNs with the Long Short-Term Memory (LSTM) layers offers a better classification performance compared to other state-of-the-art methods. The classification performance achieved by our proposed method using the BCI competition IV 2a dataset in terms of accuracy value is 61.68%. This method can be successfully applied to BCI systems where the amount of data is large due to daily recording.