Pattern recognition of electroencephalogram (EEG) signals during motor imagery (MI) has been one of the most important research directions in the field of non-invasive brain-computer interface (BCI). In recent years, deep learning has further improved the recognition accuracy of EEG signals during MI. However, given the strong time variability of EEG signals, there are still some problems such as insufficient training samples and too high feature dimensions. To solve the above problems, this study proposes a new training strategy called “overlapped time slice”. Based on the existing cropped time slice strategy, this study adopts a novel overlapped time slice strategy and constructs a new loss function calculation and label prediction method with the overlapped time slice set. The overlapped time slice strategy can not only further increase the number of training samples but also reduce the feature space of a single sample to improve the performance of the deep neural network in EEG signal recognition. For the verification of the feasibility and effectiveness of the proposed overlapped strategy, three open-source EEG signal datasets, namely the BCI Competition IV datasets 1, 2a, and 2b, are selected in this study, and five kinds of deep neural network models are built on these three datasets. During experiments, the performance and efficiency of MI recognition are compared between the cropped strategy and the overlapped strategy. Experimental results show that the overlapped strategy has better recognition performance than that of the cropped strategy. Finally, nine groups of experiments are designed with different parameter combinations by adjusting the parameters of the overlapped time slice strategy. The experimental results demonstrate that parameter combination affects the final classification performance and that the classification performance is not in a linear relationship with the efficiency. The recognition accuracy of the proposed overlapped strategy on dataset 1, 2a, and 2b is 92.3%, 77.8%, and 86.3% respectively. Compared with the conventional cropped strategy, the proposed overlapped strategy has improved the performance significantly without necessarily reducing the efficiency.