###### Abstract  
Emotion recognition is a research area that spans multiple disciplines, including computational science, neuroscience, and cognitive psychology. The use of electroencephalogram (EEG) signals in emotion recognition is particularly promising due to their objective and nonartefactual nature. To effectively leverage the spatial information between electrodes, the temporal correlation of EEG sequences, and the various sub-bands of information corresponding to different emotions, we construct a 4D matrix comprising temporal-frequency-spatial features as the input to our proposed hybrid model. This model incorporates a residual network based on depthwise convolution (DC) and pointwise convolution (PC), which not only extracts the spatial-frequency information in the input signal, but also reduces the training parameters. To further improve performance, we apply frequency channel attention networks (FcaNet) to distribute weights to different channel features. Finally, we use a bidirectional long short-term memory network (Bi-LSTM) to learn the temporal information in the sequence in both directions. To highlight the temporal importance of the frame window in the sample, we choose the weighted sum of the hidden layer states at all frame moments as the input to softmax. Our experimental results demonstrate that the proposed method achieves excellent recognition performance. We experimentally validated all proposed methods on the DEAP dataset, which has authoritative status in the EEG emotion recognition domain. The average accuracy achieved was 97.84% for the four binary classifications of valence, arousal, dominance, and liking and 88.46% for the four classifications of high and low valence-arousal recognition. emotion recognition; electroencephalogram; 4D features; convolution; attention +
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of features that are highly relevant to emotions and the selection of a suitable classification model remain two obstacles for researchers. EEG signals have been demonstrated to contain emotion-related feature components in three dimensions: temporal, frequency, and spatial. Unfortunately, most of the literature considers only one or two of these three dimensions and performs pattern recognition by simple combinations, completely ignoring spatial information interactions across channels, prior knowledge across frequency bands, and information complementarity between different features. This not only fails to improve model accuracy, but also leads to feature redundancy and increased complexity. Therefore, it is important to improve emotion recognition accuracy by integrating information from different domains and adaptively capturing important temporal, frequency, and spatial features in subsequent classification models. Recently, deep learning, the successor to machine learning, has made significant contributions to various fields, including computer vision (CV) and natural language processing (NLP). A wide range of deep models have been developed and have shown impressive results. The application of these models has also had a significant impact on EEG emotion recognition, including the use of autoencoders (AEs), graph convolutional neural networks (GCNs), transformers, and other related techniques. Although these approaches have yielded promising outcomes, challenges remain that need to be addressed. Specifically, how to construct a network that is suitable for EEG signal feature extraction represents a crucial issue that must be addressed. Regarding the aforementioned challenges, a cascade network based on multidimensional features is proposed, which presents three main contributions:  
* EEG data are converted into a 4D matrix structure consisting of multiple frames, which contain information in three dimensions: temporal, frequency, and spatial, and can effectively represent the neural features of different emotions. * In this paper, a novel attention module FcaNet is introduced. FcaNet redistributes the weights of different channels to obtain high-quality discrimination.