A lightweight and accurate double-branch neural network for four-class motor imagery classification

•The proposed lightweight CNN based on parallel structure improves the MI EEG decoding performance of the existing shallow networks.•The experimental results revealed that it has excellent decoding performance on benchmark dataset compared with mainstream wide or deep and hybrid networks.•The proposed model maintains a good balance between performance and complexity, obtains a satisfactory decoding performance with a low resource cost.•It is friendly for BCI hardware loaded with the algorithms because of low computer resource.