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In order to increase the number of active brain computer interface (BCI) instruction sets, an experimental paradigm of sequential coding based on motor imagery and speech imagery is proposed. By dividing one motor imagery and one speech imagery, four kinds of imagination modes are obtained: 1) Motor imagery; 2) Speech imagery; 3) Motor imagery followed by speech imagery; 4) Speech imagery followed by motor imagery. An attention and multi-scale neural network (AMEEGNet) is designed for the data set. Firstly, the robust time representation of the signal is extracted by dilated convolution and three two-dimensional convolution with different size scales. Then, deep convolution and separable convolution are used to extract spatial features and frequency domain features, respectively. In addition, the squeeze-excitation module is added to the model to extract features with high classification accuracy adaptively. Finally, a network layer with full connection is used for classification. The model achieves an average accuracy of 71.1% on a temporal coding experiment dataset with four kinds of imagination. One the same dataset, EEGNet, MMCNN, Shallow ConvNet and TSGL-EEGNet achieved 57.9%, 60.5%, 68.3% and 68.4% accuracy, respectively. It can be seen that the proposed model has the highest recognition accuracy. [ABSTRACT FROM AUTHOR]