Abstract
Subject-independent emotion recognitions take full advantage of existing EEG signals to construct intelligent models, and generalize to other subjects for detecting, differentiating and predicting. However, objective differences originating from different subjects strength the difficulties of constructing models, which are summarized as both of challenges: 1) the physiological difference products incorrect EEG signals, and 2) the psychological difference may be generated the early or late response signals to induce the local semantic invalidation. Aiming to above problems, we propose a novel EEG subject-independent model (termed of Unbiased Global Spatial Representation). First, present the self-incremental Auto-encoder network to obtain latent unified features to correct the physiological deviation. Second, leverage gramian angular fields (GAF) to transfer from one-dimension time sequences to two-dimension spatial images to remedy the local semantic invalidation. Finally, attention-CNN is constructed to extract more discriminate global features to enhance performances. The proposed model is verified in popular datasets for subject-independent emotion recognitions, and compare with classical models achieving satisfactory performances.
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Zhang, J., Wang, Y., Wei, G. (2023). Global Spatial Representation: EEG Correcting for Subject-Independent Emotion Recognition. In: Fujita, H., Wang, Y., Xiao, Y., Moonis, A. (eds) Advances and Trends in Artificial Intelligence. Theory and Applications. IEA/AIE 2023. Lecture Notes in Computer Science(), vol 13925. Springer, Cham. https://doi.org/10.1007/978-3-031-36819-6_34
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