Emotional EEG(electroencephalogram)signal is a non-stationary time series with low signal-to-noise ratio.Traditional feature extraction and classification methods are difficult to extract and classify the effective features of different emotional states.In regard to the above situation,a deep learning model that automatically fuses different frequency bands and time-frequency characteristics of EEG signals is proposed.Firstly,the preprocessed data is processed in frequency bands,and the differential entropy features of each frequency band are extracted.Then,the squeeze excitation module connected in the network assigns weight to the differential entropy features of different frequency bands to obtain the valuable information of the input data,and then uses the improved dense connection network for feature fusion and classification recognition to ensure the maximum information transmission between the network layers.Finally,the algorithm is verified by using the SEED emotional EEG of three classification dataset,and the classification accuracy is 96.03%,which is higher than the existing baseline learning algorithm.The proposed algorithm further enhances network feature extraction capabilities and demonstrates faster convergence,which is of great significance for improving the performance of the classifier.
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