A classification method for EEG motor imagery signals based on parallel convolutional neural network

Deep learning has been used popularly and successfully in state of art researches to classify different types of images. However, so far, the applications of deep learning methods for the electroencephalography (EEG) motor imagery classification is very limited. In this study, a pre-processing algorithm is proposed for the EEG signals representation. Then, a parallel convolutional neural network (PCNN) architecture is proposed to classify motor imagery signals. For the raw EEG signals representation, a new form of the images is created to combine spatial filtering and frequency bands extracting together. By feeding the represented images into the PCNN, it stacks three unique sub-models together aiming to optimize the performance of classification. The average accuracy of the proposed method achieves 83.0 +/- 3.4% on BCI Competition IV dataset 2b, which outperforms the compared methods at least 5.2%. The average Kappa value of the proposed method achieves 0.659 +/- 0.067 on dataset 2b, providing at least 20.5% improvement with respect to the compared algorithms. The results show that the proposed method performs better in EEG motor imagery signals classification.