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One of the challenges in emotion recognition is finding an effective way to represent spatialtemporal features from EEG. To fully utilize the features on multiple dimensions of EEG signals, we propose a parallel sequence-channel projection convolutional neural network, including temporal stream sub-network, spatial stream sub-network, and fusion classification block. Temporal stream extracts temporal continuity via sequence-projection layer while spatial stream captures spatial correlation via channelprojection layer. Both sequence-projection and channel-projection adopt length-synchronized convolutional kernel to decode whole time and space information. The size of length-synchronized convolutional kernel is equal to the length of transmitted EEG sequence. The fusion classification block combines the extracted temporal and spatial features into a joint spatial-temporal feature vector for emotion prediction. In addition, we present a baseline noise filtering module to amplify input signals and a random channels exchange strategy to enrich the baseline-removed emotional signals. Experimental evaluation on DEAP dataset reveals that the proposed method achieves state-of-the-art classification performance for the binary classification task. The recognition accuracies reach to 96.16% and 95.89% for valence and arousal. The proposed method can improve 3% to 6% than other latest advanced works. INDEX TERMS Emotion recognition, multi-channel EEG, data augmentation, length-synchronized convolutional kernel, spatial-temporal feature.