The application of brain-computer interface (BCI) has always been limited by low decoding accuracy due to excessive noise in electroencephalogram (EEG) signals. The traditional methods employ some representative features while losing too much information. Deep learning methods have achieved good results, but subject to the insufficient ability of extracting discriminative features from EEG. In this paper, we propose a novel decoding framework that effectively uses spatial and temporal information by time-contained spatial filtering and spatial–temporal analysis network (TSF-STAN) for EEG multi-classification tasks. Firstly, the TSF with the joint one-versus-rest (Joint-OVR) strategy is given to map the signal to a new space, where each category can be more easily distinguished, while preserving the time-domain characteristics. Next, the STAN is designed to extract discriminative spatial and temporal features further with convolutional layers, and then perform classification. Detailed experiments have been carried out to verify the effectiveness of our framework on BCI competition IV-2a and IV-2b datasets of motor imagery (MI) EEG. The results show that our method has outperformed recent outstanding algorithms, with the average accuracy of 83.0% on IV-2a and 88.0% on IV-2b. Spatial and temporal information is well used to obtain better performance, which has a good potential of EEG decoding for application of BCI.
