A novel transferability attention neural network model for EEG emotion recognition

The existed methods for electroencephalograph (EEG) emotion recognition always train the models based on all the EEG samples indistinguishably. However, some of the source (training) samples may lead to a negative influence because they are significant dissimilar with the target (test) samples. So it is necessary to give more attention to the EEG samples with strong transferability rather than forcefully training a classification model by all the samples. Furthermore, for an EEG sample, from the aspect of neuroscience, not all the brain regions of an EEG sample contain emotional information that can transferred to the test data effectively. Even some brain region data will make strong negative effect for learning the emotional classification model. Considering these two issues, in this paper, we propose a transferable attention neural network (TANN) for EEG emotion recognition, which learns the emotional discriminative information by highlighting the transferable EEG brain regions data and samples adaptively through local and global attention mechanism. This can be implemented by measuring the outputs of multiple brain-region-level discriminators and one single sample-level discriminator. Extensive experiments on EEG emotion recognition demonstrate that the proposed TANN is superior to those state-of-the-art methods. (c) 2021 Elsevier B.V. All rights reserved.

Automated summary

The traditional methods for EEG emotion recognition face limitations as not all EEG samples are suitable for training models, and some brain region data may even have negative effects on learning emotional classification models. Therefore, this paper proposes a new transferable attention neural network (TANN) method, which achieves better emotion recognition by highlighting EEG brain regions data and samples with strong transferability.