Electroencephalography （EEG） signal classification plays a crucial role in emotion recognition and brain-computer interface （BCI） applications. This paper proposes a parameter-sharing cross-map token attention （CMTA） model for intra- and inter-feature map interaction. Firstly， a spatial-temporal convolutional neural network （STCNN） is used to process EEG data， generating multiple EEG feature maps. Each feature map is treated as a token and fed into a parameter-sharing multi-modal module MT， which integrates a multi-layer perceptron （MLP） and a Transformer. The MLP captures intra-feature map interactions， while the Transformer enables information exchange between feature maps， thereby extracting richer features. Finally， an adaptive classifier （Adapt-Classifier） consisting of one-dimensional adaptive pooling and a fully connected layer is used to perform EEG classification. Experimental results show that the proposed method achieves a classification accuracy of 98.86% and a Kappa value of 0.982 9 on the SEED dataset for emotion recognition， an accuracy of 81.20% and a Kappa value of 0.748 4 on the BCI Competition IV Dataset 2a for motor imagery classification， and an accuracy of 86.55% and a Kappa value of 0.735 2 on the BCI Competition IV Dataset 2b. These results demonstrate the superior performance of the proposed method in EEG classification tasks and highlight its broad applicability across different EEG datasets.